text:
@inproceedings{10.1145/3097983.3105807,
author = {Dwork, Cynthia},
title = {What's Fair?},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3105807},
doi = {10.1145/3097983.3105807},
abstract = {Data, algorithms, and systems have biases embedded within them reflecting designers' explicit and implicit choices, historical biases, and societal priorities. They form, literally and inexorably, a codification of values. "Unfairness" of algorithms -- for tasks ranging from advertising to recidivism prediction -- has attracted considerable attention in the popular press. The talk will discuss the nascent mathematically rigorous study of fairness in classification and scoring.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1},
numpages = {1},
keywords = {classification fairness, fairness, algorithmic bias, data mining., system bias, data bias, scoring fairness},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3105809,
author = {Miller, Ren\'{e}e J.},
title = {The Future of Data Integration},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3105809},
doi = {10.1145/3097983.3105809},
abstract = {The value of data explodes when it is integrated. In this talk, I present some important innovations in data integration over the last two decades. These include data exchange [1], which provides a foundation for reasoning about the correctness of transformed data, and the use of declarative mappings in integration [2]. I discuss how data mining has been used to facilitate data integration, including constraint discovery [3], mapping discovery [4], and in schema discovery to combat database decay and facilitate integration [5,6]. I present some important new data integration challenges that arise in data science. These include the use of mining for query and visualization recommendation over massive data lakes [7] and data set search, finding datasets of interest at interactive speeds [8].},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {3},
numpages = {1},
keywords = {data science},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3105808,
author = {Yu, Bin},
title = {Three Principles of Data Science: Predictability, Stability and Computability},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3105808},
doi = {10.1145/3097983.3105808},
abstract = {In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions.Making prediction as its central task and embracing computation as its core, machine learning has enabled wide-ranging data-driven successes. Prediction is a useful way to check with reality. Good prediction implicitly assumes stability between past and future. Stability (relative to data and model perturbations) is also a minimum requirement for interpretability and reproducibility of data driven results (cf. Yu, 2013). It is closely related to uncertainty assessment. Obviously, both prediction and stability principles can not be employed without feasible computational algorithms, hence the importance of computability.The three principles will be demonstrated in the context of two neuroscience projects and through analytical connections. In particular, the first project adds stability to predictive modeling used for reconstruction of movies from fMRI brain signlas for interpretable models. The second project use predictive transfer learning that combines AlexNet, GoogleNet and VGG with single V4 neuron data for state-of-the-art prediction performance. Our results lend support, to a certain extent, to the resemblance of these CNNs to brain and at the same time provide stable pattern interpretations of neurons in the difficult primate visual cortex V4.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {5},
numpages = {1},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3121426,
author = {Fayyad, Usama M. and Simoudis, Evangelos and Srivastava, Ashok},
title = {Foreword to the Applied Data Science: Invited Talks Track at KDD-2017},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3121426},
doi = {10.1145/3097983.3121426},
abstract = {The Applied Data Science (ADS) Invited Talks Track at KDD-2017 is a continuation of what has now become a "7-year tradition" at KDD conferences. This is the second year the track operates under the ADS name, an evolution from its origins at KDD-2011 as the "Industry Practice Expo". The KDD Conference on Knowledge Discovery and Data Mining (KDD) is the world's first, largest and best conference on Data Science, Data Mining, and Knowledge Discovery. It brings together a healthy mix of academic researchers, industry and government researchers, and practitioners from a wide range of institutions and fields. The primary focus on KDD is on peer-reviewed research contributions and the academic advancement of the field. This is an important goal and in fact the KDD conference is now recognized as the most competitive and prestigious forum for presenting high quality research results. KDD, being fundamentally an applied field, needs the strong representation of applied work of big impact. Over the years of running the conference we observed that our initial speaker-selection approach needed to be re-thought because of the important contributions made to the field outside traditional academic, industrial and government research laboratories. The result of this re-thinking was to create a forum that exposes important contributions to Data Science through Big Data Applications that address strategic problems. We wanted to effectively capture the rising importance of Data Science and Machine Learning especially in the Big Data environment where structured and unstructured data create special challenges, and of course present new opportunities. The goal of the Invited Talks Track is to curate contributions from leaders in our field who have made important contributions through the development of a system, the creation of a new and important business, or the development and market introduction of a product,. Some of these important contributions may never see an academic paper or detailed peer-reviewed paper written about them, yet they are of critical importance to our very applied field. To give you an idea of how rapidly growing this area is, and how this sector of our industry and promises to be highly disruptive across many industries, we cite a couple of articles out of a plethora of such coverage: According to IDC, the global revenues from Big Data and business will grow from $130.1 billion in 2016 to more than $203 billion in 2020, at a compound annual growth rate (CAGR) of 11.7% [1]. Furthermore, to quote from a Forbes article: "Data monetization" will become a major source of revenues, as the world will create 180 zettabytes of data (or 180 trillion gigabytes) in 2025, up from less than 10 zettabytes in 2015.? [2]},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {7–8},
numpages = {2},
keywords = {data science in practice, performance benchmarks, model deployment and monitoring, applications of data mining, model management, data benchmarks, applications, data mining practice, software implementations, data science},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3106682,
author = {Ari\~{n}o de la Rubia, Eduardo},
title = {More than the Sum of Its Parts: Building Domino Data Lab},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3106682},
doi = {10.1145/3097983.3106682},
abstract = {Industry has always leveraged cutting edge quantitative research techniques. From finance and insurance, to marketing and manufacturing, efficiencies and advantages have been seized through measurement, prediction, and the generation of insights' but never at this scale. Organizations which previously may have employed one or two data scientists are now scaling the work to dozens if not hundreds of practitioners. Where previously only a handful of organizations could boast that they were leveraging machine learning and statistical models, now it's a rarity to find an untouched industry or player. Organizations are now faced with the challenges of empowering, scaling, and measuring this workforce to sustain the transformation to the prediction economy. In this talk, I will discuss how and why we built the Domino Data Lab platform. I will talk about the challenges we faced technologically, organizationally and culturally when bringing a system of record to data science.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {9},
numpages = {1},
keywords = {data science scalability, domino data lab, data science organization., data science, industrial machine learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3105813,
author = {Berglund, Andy},
title = {Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3105813},
doi = {10.1145/3097983.3105813},
abstract = {The recent advances in genome sequencing and analyses of the billions of base pairs in genomic data have been a boon for moving forward our understanding of human disease. In this talk I will describe how genome sequencing has dramatically improved our understanding of the most common adult form of muscular dystrophy, which is myotonic dystrophy. Two different genetic mutations cause thousands of changes in the cells and tissues of myotonic dystrophy patients. Genome sequencing has allowed us to precisely determine the degree of changes across patients, correlate these changes to disease symptoms and allow us to determine quickly in cell and animal models the effectiveness of therapeutic strategies for myotonic dystrophy.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {11},
numpages = {1},
keywords = {myotonic dystrophy, genomic data, muscular dystrophy, data mining., genome sequencing},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3105817,
author = {Bloom, Josh},
title = {Industrial Machine Learning},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3105817},
doi = {10.1145/3097983.3105817},
abstract = {The ongoing digitization of the industrial-scale machines that power and enable human activity is itself a major global transformation. But the real revolution-in efficiencies, in improved and saved lives-will happen as machine learning automation and insights are properly coupled to the complex systems of industrial data. Leveraging a systems view of real-world use cases from aviation to transportation, I contrast the needs and approaches of consumer versus industrial machine learning. Particularly, I focus on three key areas: combining physics-based models to data-driven models, differential privacy and secure ML (including edge-to-cloud strategies), and interpretability of model predictions.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {13},
numpages = {1},
keywords = {data mining., interpretability, industrial machine learning, differential privacy, secure machine learning, data-driven models, physics-based models},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3105818,
author = {Cao, Longbing},
title = {Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3105818},
doi = {10.1145/3097983.3105818},
abstract = {Behavior is ubiquitous, and behavior intelligence and insight play an important role in data understanding and business problem-solving. Behavior Informatics [1,2] emerges as an important tool for discovering behavior intelligence and behavior insight. As a computational concept, behavior captures the aspects of the demographics of behavioral subjects and objects; social relationships or norms governing the interactions between behaviors of an individual or a group; behavior sequences or networks and their dynamics; and the impact or effect generated by the behaviors undertaken by subjects on objects. Accordingly, a behavior model [2] captures the subject and the object of a behavior or behavior sequence, the activities conducted by its subject on objects, and the relationships between activities; behavior subject, object, activities and relationships are characterized by their respective attributes. As a result, a behavior is represented as a behavior attributes-based vector; and a subject's behaviors at a time period form a vector-based sequence, namely, represented as a behavior attribute vector-based matrix [3]. With such behavior modeling and from the informatics perspective, behavior informatics takes a top-down approach to systematically and deeply represent, model, reason about, and aggregate behaviors [4]; and a bottom-up approach to analyze and learn behavior occurrences, non-occurrences, dynamics, impact, and utility [2].Accordingly, for a real-life problem, first, its data is converted to behavioral data according to the above behavior model, characterized by the relevant activities that form behavior sequences, and the properties of subjects, objects, activities, and relationships. Second, analytical tasks, such as behavior pattern analysis, abnormal behavior detection, coupled analysis of group behaviors, modeling of behavior impact and utility, discovery of high impact and high utility behaviors, analysis of non-occurring behaviors, and analysis of behavior evolution and dynamics, can be undertaken on such behavioral data. In this way, complex behaviors are quantifiable, computable [5], and manageable.This talk introduces some of real-life applications of behavior informatics in core business, capital markets and government services. It involves complex individual and group behaviors in relevant business, the interactions between clients and service providers, and relevant behavior sequences and attributes. The examples demonstrate the personalized and early prediction, the prevention and intervention of abnormal behaviors, and the active and tailored management of suspicious clients. Examples include the detection of pool manipulation through analyzing coupled sequences [3] of trading behaviors from multiple associated accounts in stock markets, the intervention of high-impact [6] behaviors in social security for preventing overpayments, the quantification and identification of high-utility [7] behaviors, the identification of and tailored intervention on self-finalizing versus non-self-finalizing taxpaying behaviors [8,9], and even the impact of non-occurring behaviors [10] in debt recovery and prevention. The real-life case studies show the value and potential of behavior informatics for handling complex and challenging risk management, fraud and non-compliance, and for active and tailored client management in business problems. The examples are associated with highly significant economic benefits and social impact as a result of applying the resultant behavior insight and behavior intelligence.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {15–16},
numpages = {2},
keywords = {non-occurring behavior, coupled behavior analysis, behavior insight, behavior informatics, behavior modeling, behavior computing, behavior intelligence, user modeling},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3105816,
author = {Desai, Paritosh},
title = {It Takes More than Math and Engineering to Hit the Bullseye with Data},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3105816},
doi = {10.1145/3097983.3105816},
abstract = {Adopting algorithmic decision-making in a large and complex enterprise such as a Fortune 50 retailer like Target takes much more than clean, reliable data and great data mining capabilities. Yet data practitioners too often start with advanced math and fancy algorithms, rather than working hand-in-hand with business partners to identify and understand the biggest business problems. (Then teams should move onto how algorithms can be applied to those problems.) Another key step for data scientists at large organizations: ensuring that their business partners -- the merchants, marketers and supply chain experts -- have a base-line understanding of advanced models as well as the proper analytical support tools. Obtaining widespread buy-in and enthusiasm also requires providing a user-friendly interface for business partners with optionality and flexibility that allows the intelligence to be applied to the many varied issues facing a modern retailer, from personalization to supply chain transformation to decisions on assortment and pricing. This talk will explore effective practices and processes -- the do's and don'ts -- for data scientists to succeed in large, complex organizations like a retailer with 1,800+ stores, major marketing campaigns across multiple channels and a fast growing online business.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {17},
numpages = {1},
keywords = {decision-making, data science, data mining., practices and processes, online business},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3105812,
author = {How, Jonathan P.},
title = {Planning and Learning under Uncertainty: Theory and Practice},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3105812},
doi = {10.1145/3097983.3105812},
abstract = {This talk will describe recent progress on modeling, planning, learning, and control of autonomous systems operating in dynamic environments, with an emphasis on addressing the challenges faced on various timescales. For example, autonomous robotic agents need to plan/execute safe paths and avoid imminent collisions given noisy sensory information (short timescale), learn how to interact with other agents (possibly humans) with intents that are not known (medium timescale), and perform complex cooperative tasks given imperfect models and knowledge of the environment and teammate actions (long timescale). These tasks are often constrained to be done using onboard computation and perception, which can add significant complexity to the system. The talk will highlight several recently developed solutions to these challenges that have been implemented to demonstrate high-speed agile flight of a quadrotor in unknown, cluttered environments, autonomous navigation of a ground vehicle in complex indoor environments alongside pedestrians, and real-time cooperative multiagent planning with an onboard deep learning-based perception system.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {19},
numpages = {1},
keywords = {invited talk},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3105810,
author = {Karpatne, Anuj and Kumar, Vipin},
title = {Big Data in Climate: Opportunities and Challenges for Machine Learning},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3105810},
doi = {10.1145/3097983.3105810},
abstract = {The climate and Earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, massive amount of data about Earth and its environment is now continuously being generated by a large number of Earth observing satellites as well as physics-based earth system models running on large-scale computational platforms. These massive and information-rich datasets offer huge potential for understanding how the Earth's climate and ecosystem have been changing and how they are being impacted by humans actions. We discuss the challenges involved in analyzing these massive data sets as well as opportunities they present for both advancing machine learning as well as the science of climate change.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {21–22},
numpages = {2},
keywords = {earth observation data, machine learning, climate science},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3105814,
author = {Mazumdar, Mainak},
title = {Addressing Challenges with Big Data for Media Measurement},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3105814},
doi = {10.1145/3097983.3105814},
abstract = {The digital media and TV - which is increasingly digitized, have amassed and generating enormous amount of data. While extremely useful, the big data generated by these platforms poses unique challenges for Data Scientists working on developing measurement framework and metrics. Most practitioners optimize speed and scale at the expense of accuracy, which is critical for any measurement. And, the trade-off between bias and variance is not in consideration. In this paper, we will demonstrate how Nielsen is combining proprietary ground truth data and methodologies with Big Data to address the accuracy and bias/variance challenges. We argue that high quality ground truth or training set is pre-requisite to deploying Big Data for high quality media measurement. To illustrate the point, we will share how Nielsen is combining its proprietary high quality panels with Set Top Box for TV measurement in the U.S.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {23},
numpages = {1},
keywords = {proprietary data, media measurement, digital media, tv, data mining, big data},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3106683,
author = {Pafka, Szil\'{a}rd},
title = {Machine Learning Software in Practice: Quo Vadis?},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3106683},
doi = {10.1145/3097983.3106683},
abstract = {Due to the hype in our industry in the last couple of years, there is a growing mismatch between software tools machine learning practitioners wish for, what they would truly need for their work, what's available (either commercially or open source) and what tool developers and researchers focus on. In this talk we will give a couple of examples of this mismatch. Several surveys and anecdotal evidence show that most practitioners work most of the time (at least in the modeling phase) with datasets that t in the RAM of a single server, therefore distributed computing tools are very of- ten overkill. Our benchmarks (available on github [1]) of the most widely used open source tools for binary classification (various implementations of algorithms such as linear methods, random forests, gradient boosted trees and neural networks) on such data show over 10x speed and over 10x RAM usage difference between various tools, with "big data" tools being the most inefficient. Significant performance gains have been obtained by those tools that incorporate various low-level (close to CPU and memory architecture) optimizations. Nevertheless, we will show that even the best tools show degrading performance on the multi-socket servers featuring a high number of cores, systems that have become widely accessible more recently. Finally, while most of this talk is about performance, we will also argue that machine learning tools that feature high-level easy-to-use APIs provide increasing productivity for practitioners and therefore are preferable.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {25},
numpages = {1},
keywords = {training speed, binary classification, memory footprint, software implementations, accuracy},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3105815,
author = {Parekh, Rajesh},
title = {Designing AI at Scale to Power Everyday Life},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3105815},
doi = {10.1145/3097983.3105815},
abstract = {The majority of the experiences and interactions people have on Facebook today are made possible with AI. Well over 1 billion people enjoy unique, personalized experiences on Facebook that are powered by a wealth of AI and machine learning algorithms. AI is an incredibly fast-moving field: engineers and researchers across the company are turning the latest research breakthroughs into tools, platforms, and infrastructure that make it possible for anyone at Facebook to use AI in the experiences and products they build. This talk will look at how Facebook is conducting and applying industry-leading research to help drive advancements in AI disciplines like computer vision, language understanding, speech and video. We will also talk about building an infrastructure that anyone at Facebook can use to easily reuse algorithms in different products, scale to run thousands of simultaneous custom experiments, and give concrete examples of how employees across the company are able to leverage these platforms to build new AI products and services.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {27},
numpages = {1},
keywords = {artificial intelligence, computer vision, speech, facebook, video, language understanding, machine learning infrastructure},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3105811,
author = {Potere, David},
title = {Spaceborne Data Enters the Mainstream},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3105811},
doi = {10.1145/3097983.3105811},
abstract = {Thanks to a diverse constellation of Earth observing satellites, humanity is effectively looking everywhere, at everything, all the time. And we've been quietly doing so for a long time. The challenge is that for many years these massive data archives have been stranded from operational, cloud-based, modern data science. That is changing fast. In this session we'll do a rapid primer on satellite imagery as a source of novel data about our Earth and discuss how machine learning is a key force for translating all of this Earth data into real insights. We'll use the global agriculture system as a case in point, highlighting some of potential that a spaceborne perspective brings to this vital sector of the economy. Along the way, we will explore some of the beautiful imagery of our home planet that fuels this new class of insights.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {29},
numpages = {1},
keywords = {data mining, machine learning, agriculture system., spaceborne data, satellite imagery},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3120998,
author = {Fayyad, Usama M. and Candel, Arno and Ari\~{n}o de la Rubia, Eduardo and Pafka, Szil\'{a}rd and Chong, Anthony and Lee, Jeong-Yoon},
title = {Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess?},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3120998},
doi = {10.1145/3097983.3120998},
abstract = {This panel aims to address areas that are widely acknowledged to be of critical importance to the success of Data Science projects and to the healthy growth of KDD/Data Science as a field of scientific research. However, despite this acknowledgement of their criticality, these areas receive insufficient attention in the major conferences in the field. Furthermore, there is a lack of actual actions and tools to address these areas in actual practice. These areas are summarized as follows:1. Ask any data scientist or machine learning practitioner what they spend the majority of their time working on, and you will most likely get an answer that indicates that 80% to 90% of their time is spent on "Data Chasing", "Data Sourcing", "Data Wrangling", "Data Cleaning" and generally what researchers would refer to-often dismissively-as "Data Preparation". The process of producing statistical or data mining models from data is typically "messy" and certainly lacks management tools to help manage, replicate, reconstruct, and capture all the knowledge that goes in 90% of activities of a Data Scientists. The intensive Data Engineering work that goes into exploring and determining the representation of problem and the significant amount of "data cleaning" that ensues creates a plethora of extracts, files, and many artifacts that are only meaningful to the data scientist.2. The severe lack of Benchmarks in the field, especially ones at big data scale is an impediment to true, objective, measurable progress on performance. The results of each paper are highly dependent on the large degree of freedom an author has on configuring competitive models and on determining which data sets to use (often Data that is not available to others to replicate results on)3. Monitoring the health of models in production, and deploying models into production environments efficiently and effectively is a black art and often an ignored area. Many models are effectively "orphans" with no means of getting appropriate health monitoring. The task of deploying a built model to production is frequently beyond the capabilities of a Data Scientists and the understanding of the IT team.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {31–32},
numpages = {2},
keywords = {performance benchmarks, memory footprint, accuracy, software implementations, model deployment and monitoring, data benchmarks, model management, training speed},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3120999,
author = {Muthukrishnan, Muthu and Tomkins, Andrew and Heck, Larry and Geramifard, Alborz and Agarwal, Deepak},
title = {The Future of Artificially Intelligent Assistants},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3120999},
doi = {10.1145/3097983.3120999},
abstract = {Artificial Intelligence has been present in literature at least since the ancient Greeks. Depictions present a wide range of perspectives of AI ranging from malefic overlords to depressive androids. Perhaps the most common recurring theme is the AI Assistant: C3PO from Star Wars; the Jetson's Rosie the Robot; the benign hyper-efficient Minds of Iain M. Banks's Culture novels; the eerie HAL 9000 of Arthur C. Clarke's 2001: A Space Odyssey. Today, artificially intelligent assistants are actual products in the marketplace, based on startling recent progress in technologies like speaker-independent speech recognition. These products are in their infancy, but are improving rapidly. In this panel, we will address the product and technology landscape, and will ask a series of experts in the field plus the members of the audience to take a stance on what the future of artificially intelligent assistants will look like.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {33–34},
numpages = {2},
keywords = {intelligent assistants, artificial intelligence, conversational bots, speech recognition},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098047,
author = {Angelino, Elaine and Larus-Stone, Nicholas and Alabi, Daniel and Seltzer, Margo and Rudin, Cynthia},
title = {Learning Certifiably Optimal Rule Lists},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098047},
doi = {10.1145/3097983.3098047},
abstract = {We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm provides the optimal solution, with a certificate of optimality. By leveraging algorithmic bounds, efficient data structures, and computational reuse, we achieve several orders of magnitude speedup in time and a massive reduction of memory consumption. We demonstrate that our approach produces optimal rule lists on practical problems in seconds. This framework is a novel alternative to CART and other decision tree methods.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {35–44},
numpages = {10},
keywords = {rule lists, interpretable models, optimization, decision trees},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098012,
author = {Avin, Chen and Lotker, Zvi and Nahum, Yinon and Peleg, David},
title = {Improved Degree Bounds and Full Spectrum Power Laws in Preferential Attachment Networks},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098012},
doi = {10.1145/3097983.3098012},
abstract = {Consider a random preferential attachment model G(p) for network evolution that allows both node and edge arrivals. Starting with an arbitrary nonempty graph G0, at each time step, there are two possible events: with probability p > 0 a new node arrives and a new edge is added between the new node and an existing node, and with probability 1 - p a new edge is added between two existing nodes. In both cases, the involved existing nodes are chosen at random according to preferential attachment, i.e., with probability proportional to their degree. G(p) is known to generate power law networks, i.e., the fraction of nodes with degree k is proportional to k-β. Here β=(4-p)/(2-p) is in the range (2,3].Denoting the number of nodes of degree k at time t by mk,t, we significantly improve some long-standing results. In particular, we show that mk,t is concentrated around its mean with a deviation of O(√t), which is independent of k. We also tightly bound the expectation Emk,t with an additive error of O(1/k), which is independent of t. These new bounds allow us to tightly estimate mk,t for a considerably larger k values than before. This, in turn, enables us to estimate other important quantities, e.g., the size of the k-rich club, namely, the set of all nodes with a degree at least k.Finally, we introduce a new generalized model, G(pt, rt, qt), which extends G(p) by allowing also time-varying probabilities for node and edge arrivals, as well as the formation of new components. We show that the extended model can produce power law networks with any exponent β in the range (1,∞). Furthermore, the concentration bounds established for mk,t in G(p) also apply in G(pt, rt, qt).},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {45–53},
numpages = {9},
keywords = {power law, networks, preferential attachment},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098023,
author = {Bai, Zilong and Walker, Peter and Tschiffely, Anna and Wang, Fei and Davidson, Ian},
title = {Unsupervised Network Discovery for Brain Imaging Data},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098023},
doi = {10.1145/3097983.3098023},
abstract = {A common problem with spatiotemporal data is how to simplify the data to discover an underlying network that consists of cohesive spatial regions (nodes) and relationships between those regions (edges). This network discovery problem naturally exists in a multitude of domains including climate data (dipoles), astronomical data (gravitational lensing) and the focus of this paper, fMRI scans of human subjects. Whereas previous work requires strong supervision, we propose an unsupervised matrix tri-factorization formulation with complex constraints and spatial regularization. We show that this formulation works well in controlled experiments with synthetic networks and is able to recover the underlying ground-truth network. We then show that for real fMRI data our approach can reproduce well known results in neurology regarding the default mode network in resting-state healthy and Alzheimer affected individuals.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {55–64},
numpages = {10},
keywords = {spatial regularization, spatiotemporal data, brain, fmri, network discovery},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097997,
author = {Baytas, Inci M. and Xiao, Cao and Zhang, Xi and Wang, Fei and Jain, Anil K. and Zhou, Jiayu},
title = {Patient Subtyping via Time-Aware LSTM Networks},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097997},
doi = {10.1145/3097983.3097997},
abstract = {In the study of various diseases, heterogeneity among patients usually leads to different progression patterns and may require different types of therapeutic intervention. Therefore, it is important to study patient subtyping, which is grouping of patients into disease characterizing subtypes. Subtyping from complex patient data is challenging because of the information heterogeneity and temporal dynamics. Long-Short Term Memory (LSTM) has been successfully used in many domains for processing sequential data, and recently applied for analyzing longitudinal patient records. The LSTM units are designed to handle data with constant elapsed times between consecutive elements of a sequence. Given that time lapse between successive elements in patient records can vary from days to months, the design of traditional LSTM may lead to suboptimal performance. In this paper, we propose a novel LSTM unit called Time-Aware LSTM (T-LSTM) to handle irregular time intervals in longitudinal patient records. We learn a subspace decomposition of the cell memory which enables time decay to discount the memory content according to the elapsed time. We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes. Experiments on synthetic and real world datasets show that the proposed T-LSTM architecture captures the underlying structures in the sequences with time irregularities.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {65–74},
numpages = {10},
keywords = {patient subtyping, recurrent neural network, long-short term memory},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097991,
author = {Chang, Xiaojun and Yu, Yao-Liang and Yang, Yi},
title = {Robust Top-<i>k</i> Multiclass SVM for Visual Category Recognition},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097991},
doi = {10.1145/3097983.3097991},
abstract = {Classification problems with a large number of classes inevitably involve overlapping or similar classes. In such cases it seems reasonable to allow the learning algorithm to make mistakes on similar classes, as long as the true class is still among the top-k (say) predictions. Likewise, in applications such as search engine or ad display, we are allowed to present k predictions at a time and the customer would be satisfied as long as her interested prediction is included. Inspired by the recent work of [15], we propose a very generic, robust multiclass SVM formulation that directly aims at minimizing a weighted and truncated combination of the ordered prediction scores. Our method includes many previous works as special cases. Computationally, using the Jordan decomposition Lemma we show how to rewrite our objective as the difference of two convex functions, based on which we develop an efficient algorithm that allows incorporating many popular regularizers (such as the l2 and l1 norms). We conduct extensive experiments on four real large-scale visual category recognition datasets, and obtain very promising performances.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {75–83},
numpages = {9},
keywords = {visual category recognition, top-k multiclass svm},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098017,
author = {Chen, Yu and Zaki, Mohammed J.},
title = {KATE: K-Competitive Autoencoder for Text},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098017},
doi = {10.1145/3097983.3098017},
abstract = {Autoencoders have been successful in learning meaningful representations from image datasets. However, their performance on text datasets has not been widely studied. Traditional autoencoders tend to learn possibly trivial representations of text documents due to their confoundin properties such as high-dimensionality, sparsity and power-law word distributions. In this paper, we propose a novel k-competitive autoencoder, called KATE, for text documents. Due to the competition between the neurons in the hidden layer, each neuron becomes specialized in recognizing specific data patterns, and overall the model can learn meaningful representations of textual data. A comprehensive set of experiments show that KATE can learn better representations than traditional autoencoders including denoising, contractive, variational, and k-sparse autoencoders. Our model also outperforms deep generative models, probabilistic topic models, and even word representation models (e.g., Word2Vec) in terms of several downstream tasks such as document classification, regression, and retrieval.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {85–94},
numpages = {10},
keywords = {representation learning, text analytics, autoencoders, competitive learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097999,
author = {Cohen, Reuven and Katzir, Liran and Yehezkel, Aviv},
title = {A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097999},
doi = {10.1145/3097983.3097999},
abstract = {In recent years there has been a growing interest in developing "streaming algorithms" for efficient processing and querying of continuous data streams. These algorithms seek to provide accurate results while minimizing the required storage and the processing time, at the price of a small inaccuracy in their output. A fundamental query of interest is the intersection size of two big data streams. This problem arises in many different application areas, such as network monitoring, database systems, data integration and information retrieval. In this paper we develop a new algorithm for this problem, based on the Maximum Likelihood (ML) method. We show that this algorithm outperforms all known schemes in terms of the estimation's quality (lower variance) and that it asymptotically achieves the optimal variance.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {95–103},
numpages = {9},
keywords = {cardinality estimation, set intersection, data mining, streaming algorithms},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098020,
author = {Cohen, Edith},
title = {HyperLogLog Hyperextended: Sketches for Concave Sublinear Frequency Statistics},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098020},
doi = {10.1145/3097983.3098020},
abstract = {One of the most common statistics computed over data elements is the number of distinct keys. A thread of research pioneered by Flajolet and Martin three decades ago culminated in the design of optimal approximate counting sketches, which have size that is double logarithmic in the number of distinct keys and provide estimates with a small relative error. Moreover, the sketches are composable, and thus suitable for streamed, parallel, or distributed computation.We consider here all statistics of the frequency distribution of keys, where a contribution of a key to the aggregate is concave and grows (sub)linearly with its frequency. These fundamental aggregations are very common in text, graphs, and logs analysis and include logarithms, low frequency moments, and cap statistics.We design composable sketches of double-logarithmic size for all concave sublinear statistics. Our design combines theoretical optimality and practical simplicity. In a nutshell, we specify tailored mapping functions of data elements to output elements so that our target statistics on the data elements is approximated by the (max-) distinct statistics of the output elements, which can be approximated using off-the-shelf sketches. Our key insight is relating these target statistics to the complement Laplace transform of the input frequencies.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {105–114},
numpages = {10},
keywords = {laplace transform, concave sublinear, distributed aggregation, streaming, frequency statistics},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098031,
author = {Conte, Alessio and Firmani, Donatella and Mordente, Caterina and Patrignani, Maurizio and Torlone, Riccardo},
title = {Fast Enumeration of Large K-Plexes},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098031},
doi = {10.1145/3097983.3098031},
abstract = {K-plexes are a formal yet flexible way of defining communities in networks. They generalize the notion of cliques and are more appropriate in most real cases: while a node of a clique C is connected to all other nodes of C, a node of a k-plex may miss up to k connections. Unfortunately, computing all maximal k-plexes is a gruesome task and state-of-the-art algorithms can only process small-size networks. In this paper we propose a new approach for enumerating large k-plexes in networks that speeds up the search by several orders of magnitude, leveraging on (i) methods for strongly reducing the search space and (ii) efficient techniques for the computation of maximal cliques. Several experiments show that our strategy is effective and is able to increase the size of the networks for which the computation of large k-plexes is feasible from a few hundred to several hundred thousand nodes.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {115–124},
numpages = {10},
keywords = {communities, k-plexes, cliques},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097993,
author = {Dau, Hoang Anh and Keogh, Eamonn},
title = {Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097993},
doi = {10.1145/3097983.3097993},
abstract = {Time series motif discovery has emerged as perhaps the most used primitive for time series data mining, and has seen applications to domains as diverse as robotics, medicine and climatology. There has been recent significant progress on the scalability of motif discovery. However, we believe that the current definitions of motif discovery are limited, and can create a mismatch between the user's intent/expectations, and the motif discovery search outcomes. In this work, we explain the reasons behind these issues, and introduce a novel and general framework to address them. Our ideas can be used with current state-of-the-art algorithms with virtually no time or space overhead, and are fast enough to allow real-time interaction and hypotheses testing on massive datasets. We demonstrate the utility of our ideas on domains as diverse as seismology and epileptic seizure monitoring.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {125–134},
numpages = {10},
keywords = {interactive data mining, time series, motif discovery, matrix profile},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098036,
author = {Dong, Yuxiao and Chawla, Nitesh V. and Swami, Ananthram},
title = {Metapath2vec: Scalable Representation Learning for Heterogeneous Networks},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098036},
doi = {10.1145/3097983.3098036},
abstract = {We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {135–144},
numpages = {10},
keywords = {feature learning, latent representations, network embedding, heterogeneous information networks, heterogeneous representation learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098054,
author = {Epasto, Alessandro and Lattanzi, Silvio and Paes Leme, Renato},
title = {Ego-Splitting Framework: From Non-Overlapping to Overlapping Clusters},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098054},
doi = {10.1145/3097983.3098054},
abstract = {We propose ego-splitting, a new framework for detecting clusters in complex networks which leverage the local structures known as ego-nets (i.e. the subgraph induced by the neighborhood of each node) to de-couple overlapping clusters. Ego-splitting is a highly scalable and flexible framework, with provable theoretical guarantees, that reduces the complex overlapping clustering problem to a simpler and more amenable non-overlapping (partitioning) problem. We can scale community detection to graphs with tens of billions of edges and outperform previous solutions based on ego-nets analysis.More precisely, our framework works in two steps: a local ego-net analysis phase, and a global graph partitioning phase. In the local step, we first partition the nodes' ego-nets using a partitioning algorithm. We then use the computed clusters to split each node into its persona nodes that represent the instantiations of the node in its communities. Finally, in the global step, we partition the newly created graph to obtain an overlapping clustering of the original graph.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {145–154},
numpages = {10},
keywords = {ego-nets, overlapping clustering, large-scale graph algorithms},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098068,
author = {Fox, Ian and Ang, Lynn and Jaiswal, Mamta and Pop-Busui, Rodica and Wiens, Jenna},
title = {Contextual Motifs: Increasing the Utility of Motifs Using Contextual Data},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098068},
doi = {10.1145/3097983.3098068},
abstract = {Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at the time the data were collected). We hypothesize that these additional contextual data could increase the utility of motifs. Thus, we propose an extension to motifs, contextual motifs, that incorporates context. Recognizing that, oftentimes, context may be unobserved or unavailable, we focus on methods to jointly infer motifs and context. Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs. In particular, we discovered contextual motifs in continuous glucose monitor (CGM) data collected from patients with type 1 diabetes. Compared to their contextless counterparts, these contextual motifs led to better predictions of hypo- and hyperglycemic events. Our results suggest that even when inferred, context is useful in both a long- and short-term prediction horizon when processing and interpreting physiological waveform data.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {155–164},
numpages = {10},
keywords = {contextual motifs, blood glucose, motif discovery},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098044,
author = {Fu, Yanjie and Liu, Guannan and Teng, Mingfei and Aggarwal, Charu},
title = {Unsupervised P2P Rental Recommendations via Integer Programming},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098044},
doi = {10.1145/3097983.3098044},
abstract = {Due to the sparseness of quality rating data, unsupervised recommender systems are used in many applications in Peer to Peer (P2P) rental marketplaces such as Airbnb, FlipKey, and HomeAway. We present an integer programming based recommender systems, where both accommodation benefits and community risks of lodging places are measured and incorporated into an objective function as utility measurements. More specifically, we first present an unsupervised fused scoring method for quantifying the accommodation benefits and community risks of a lodging with crowd-sourced geo-tagged data. In order to the utility of recommendations, we formulate the unsupervised P2P rental recommendations as a constrained integer programming problem, where the accommodation benefits of recommendations are maximized and the community risks of recommendations are minimized, while maintaining constraints on personalization. Furthermore, we provide an efficient solution for the optimization problem by developing a learning-to-integer-programming method for combining aggregated listwise learning to rank into branching variable selection. We apply the proposed approach to the Airbnb data of New York City and provide lodging recommendations to travelers. In our empirical experiments, we demonstrate both the efficiency and effectiveness of our method in terms of striving a trade-off between the user satisfaction, time on market, and the number of reviews, and achieving a balance between positive and negative sides.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {165–173},
numpages = {9},
keywords = {integer programming, learning to optimize, unsupervised recommendations},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098002,
author = {Gu, Yupeng and Sun, Yizhou and Gao, Jianxi},
title = {The Co-Evolution Model for Social Network Evolving and Opinion Migration},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098002},
doi = {10.1145/3097983.3098002},
abstract = {Almost all real-world social networks are dynamic and evolving with time, where new links may form and old links may drop, largely determined by the homophily of social actors (i.e., nodes in the network). Meanwhile, (latent) properties of social actors, such as their opinions, are changing along the time, partially due to social influence received from the network, which will in turn affect the network structure. Social network evolution and node property migration are usually treated as two orthogonal problems, and have been studied separately. In this paper, we propose a co-evolution model that closes the loop by modeling the two phenomena together, which contains two major components: (1) a network generative model when the node property is known; and (2) a property migration model when the social network structure is known. Simulation shows that our model has several nice properties: (1) it can model a broad range of phenomena such as opinion convergence (i.e., herding) and community-based opinion divergence; and (2) it allows to control the evolution via a set of factors such as social influence scope, opinion leader, and noise level. Finally, the usefulness of our model is demonstrated by an application of co-sponsorship prediction for legislative bills in Congress, which outperforms several state-of-the-art baselines.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {175–184},
numpages = {10},
keywords = {co-evolution, network generation models, dynamic networks},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098010,
author = {Gu, Bin and Liu, Guodong and Huang, Heng},
title = {Groups-Keeping Solution Path Algorithm for Sparse Regression with Automatic Feature Grouping},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098010},
doi = {10.1145/3097983.3098010},
abstract = {Feature selection is one of the most important data mining research topics with many applications. In practical problems, features often have group structure to effect the outcomes. Thus, it is crucial to automatically identify homogenous groups of features for high-dimensional data analysis. Octagonal shrinkage and clustering algorithm for regression (OSCAR) is an important sparse regression approach with automatic feature grouping and selection by ℓ1 norm and pairwise ℓ∞ norm. However, due to over-complex representation of the penalty (especially the pairwise ℓ∞ norm), so far OSCAR has no solution path algorithm which is mostly useful for tuning the model. To address this challenge, in this paper, we propose a groups-keeping solution path algorithm to solve the OSCAR model (OscarGKPath). Given a set of homogenous groups of features and an accuracy bound ε, OscarGKPath can fit the solutions in an interval of regularization parameters while keeping the feature groups. The entire solution path can be obtained by combining multiple such intervals. We prove that all solutions in the solution path produced by OscarGKPath can strictly satisfy the given accuracy bound ε. The experimental results on benchmark datasets not only confirm the effectiveness of our OscarGKPath algorithm, but also show the superiority of our OscarGKPath in cross validation compared with the existing batch algorithm.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {185–193},
numpages = {9},
keywords = {feature selection, solution path, automatic feature grouping, oscar, sparse regression},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098034,
author = {Guidotti, Riccardo and Monreale, Anna and Nanni, Mirco and Giannotti, Fosca and Pedreschi, Dino},
title = {Clustering Individual Transactional Data for Masses of Users},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098034},
doi = {10.1145/3097983.3098034},
abstract = {Mining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {195–204},
numpages = {10},
keywords = {transactional clustering, personal cart assistant, parameter-free clustering algorithm, representative basket},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098037,
author = {Hallac, David and Park, Youngsuk and Boyd, Stephen and Leskovec, Jure},
title = {Network Inference via the Time-Varying Graphical Lasso},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098037},
doi = {10.1145/3097983.3098037},
abstract = {Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {205–213},
numpages = {9},
keywords = {time series analysis, graphical lasso, network inference, admm, convex optimization},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098060,
author = {Hallac, David and Vare, Sagar and Boyd, Stephen and Leskovec, Jure},
title = {Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098060},
doi = {10.1145/3097983.3098060},
abstract = {Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {215–223},
numpages = {9},
keywords = {time series analysis, networks, clustering, convex optimization, alternating minimization},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098074,
author = {He, Junxian and Hu, Zhiting and Berg-Kirkpatrick, Taylor and Huang, Ying and Xing, Eric P.},
title = {Efficient Correlated Topic Modeling with Topic Embedding},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098074},
doi = {10.1145/3097983.3098074},
abstract = {Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient inference in the low-dimensional embedding space, reducing previous cubic or quadratic time complexity to linear w.r.t the topic size. We further speedup variational inference with a fast sampler to exploit sparsity of topic occurrence. Extensive experiments show that our approach is capable of handling model and data scales which are several orders of magnitude larger than existing correlation results, without sacrificing modeling quality by providing competitive or superior performance in document classification and retrieval.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {225–233},
numpages = {9},
keywords = {correlated topic models, scalability, topic embedding},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098038,
author = {Hope, Tom and Chan, Joel and Kittur, Aniket and Shahaf, Dafna},
title = {Accelerating Innovation Through Analogy Mining},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098038},
doi = {10.1145/3097983.3098038},
abstract = {The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {235–243},
numpages = {9},
keywords = {innovation, product dimensions, text embedding, text mining, creativity, computational analogy},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098080,
author = {Hsieh, Cho-Jui and Si, Si and Dhillon, Inderjit S.},
title = {Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098080},
doi = {10.1145/3097983.3098080},
abstract = {Nonlinear kernel machines often yield superior predictive performance on various tasks; however, they suffer from severe computational challenges. In this paper, we show how to overcome the important challenge of speeding up kernel machines using multiple computers. In particular, we develop a parallel block minimization framework, and demonstrate its good scalability in solving nonlinear kernel SVM and logistic regression. Our framework proceeds by dividing the problem into smaller subproblems by forming a block-diagonal approximation of the Hessian matrix. The subproblems are then solved approximately in parallel. After that, a communication efficient line search procedure is developed to ensure sufficient reduction of the objective function value by exploiting the problem structure of kernel machines. We prove global linear convergence rate of the proposed method with a wide class of subproblem solvers, and our analysis covers strongly convex and some non-strongly convex functions. We apply our algorithm to solve large-scale kernel SVM problems on distributed systems, and show a significant improvement over existing parallel solvers. As an example, on the covtype dataset with half-a-million samples, our algorithm can obtain an approximate solution with 96% accuracy in 20 seconds using 32 machines, while all the other parallel kernel SVM solvers require more than 2000 seconds to achieve a solution with 95% accuracy. Moreover, our algorithm is the first distributed kernel SVM solver that can scale to massive data sets. On the KDDB dataset (20 million samples and 30 million features), our parallel solver can compute the kernel SVM solution within half an hour using 32 machines with 640 cores in total, while existing solvers can not scale to this dataset.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {245–254},
numpages = {10},
keywords = {classification, distributed algorithm, kernel methods},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098079,
author = {Kobren, Ari and Monath, Nicholas and Krishnamurthy, Akshay and McCallum, Andrew},
title = {A Hierarchical Algorithm for Extreme Clustering},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098079},
doi = {10.1145/3097983.3098079},
abstract = {Many modern clustering methods scale well to a large number of data points, N, but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy, incremental algorithm for hierarchical clustering that scales to both massive N and K---a problem setting we term extreme clustering. Our algorithm efficiently routes new data points to the leaves of an incrementally-built tree. Motivated by the desire for both accuracy and speed, our approach performs tree rotations for the sake of enhancing subtree purity and encouraging balancedness. We prove that, under a natural separability assumption, our non-greedy algorithm will produce trees with perfect dendrogram purity regardless of data arrival order. Our experiments demonstrate that PERCH constructs more accurate trees than other tree-building clustering algorithms and scales well with both N and K, achieving a higher quality clustering than the strongest flat clustering competitor in nearly half the time.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {255–264},
numpages = {10},
keywords = {clustering, large-scale learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098032,
author = {Kuang, Kun and Cui, Peng and Li, Bo and Jiang, Meng and Yang, Shiqiang},
title = {Estimating Treatment Effect in the Wild via Differentiated Confounder Balancing},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098032},
doi = {10.1145/3097983.3098032},
abstract = {Estimating treatment effect plays an important role on decision making in many fields, such as social marketing, healthcare, and public policy. The key challenge on estimating treatment effect in the wild observational studies is to handle confounding bias induced by imbalance of the confounder distributions between treated and control units. Traditional methods remove confounding bias by re-weighting units with supposedly accurate propensity score estimation under the unconfoundedness assumption. Controlling high-dimensional variables may make the unconfoundedness assumption more plausible, but poses new challenge on accurate propensity score estimation. One strand of recent literature seeks to directly optimize weights to balance confounder distributions, bypassing propensity score estimation. But existing balancing methods fail to do selection and differentiation among the pool of a large number of potential confounders, leading to possible underperformance in many high dimensional settings. In this paper, we propose a data-driven Differentiated Confounder Balancing (DCB) algorithm to jointly select confounders, differentiate weights of confounders and balance confounder distributions for treatment effect estimation in the wild high dimensional settings. The synergistic learning algorithm we proposed is more capable of reducing the confounding bias in many observational studies. To validate the effectiveness of our DCB algorithm, we conduct extensive experiments on both synthetic and real datasets. The experimental results clearly demonstrate that our DCB algorithm outperforms the state-of-the-art methods. We further show that the top features ranked by our algorithm generate accurate prediction of online advertising effect.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {265–274},
numpages = {10},
keywords = {high dimensional inference, treatment effect estimation, causal inference},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098066,
author = {Lakkaraju, Himabindu and Kleinberg, Jon and Leskovec, Jure and Ludwig, Jens and Mullainathan, Sendhil},
title = {The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098066},
doi = {10.1145/3097983.3098066},
abstract = {Evaluating whether machines improve on human performance is one of the central questions of machine learning. However, there are many domains where the data is selectively labeled, in the sense that the observed outcomes are themselves a consequence of the existing choices of the human decision-makers. For instance, in the context of judicial bail decisions, we observe the outcome of whether a defendant fails to return for their court appearance only if the human judge decides to release the defendant on bail. This selective labeling makes it harder to evaluate predictive models as the instances for which outcomes are observed do not represent a random sample of the population. Here we propose a novel framework for evaluating the performance of predictive models on selectively labeled data. We develop an approach called contraction which allows us to compare the performance of predictive models and human decision-makers without resorting to counterfactual inference. Our methodology harnesses the heterogeneity of human decision-makers and facilitates effective evaluation of predictive models even in the presence of unmeasured confounders (unobservables) which influence both human decisions and the resulting outcomes. Experimental results on real world datasets spanning diverse domains such as health care, insurance, and criminal justice demonstrate the utility of our evaluation metric in comparing human decisions and machine predictions.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {275–284},
numpages = {10},
keywords = {selective labels, unobservables, unmeasured confounders, evaluating machine learning algorithms},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097994,
author = {Li, Xiaoli and Huan, Jun},
title = {Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097994},
doi = {10.1145/3097983.3097994},
abstract = {Developing transparent predictive analytics has attracted significant research attention recently. There have been multiple theories on how to model learning transparency but none of them aims to understand the internal and often complicated modeling processes. In this paper we adopt a contemporary philosophical concept called "constructivism", which is a theory regarding how human learns. We hypothesize that a critical aspect of transparent machine learning is to "reveal" model construction with two key process: (1) the assimilation process where we enhance our existing learning models and (2) the accommodation process where we create new learning models. With this intuition we propose a new learning paradigm, constructivism learning, using a Bayesian nonparametric model to dynamically handle the creation of new learning tasks. Our empirical study on both synthetic and real data sets demonstrate that the new learning algorithm is capable of delivering higher quality models (as compared to base lines and state-of-the-art) and at the same time increasing the transparency of the learning process.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {285–294},
numpages = {10},
keywords = {constructivisim learning, transparent machine learning, dynamic task construction, sequential dirichlet process},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098006,
author = {Li, Liangyue and Tong, Hanghang and Wang, Yong and Shi, Conglei and Cao, Nan and Buchler, Norbou},
title = {Is the Whole Greater Than the Sum of Its Parts?},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098006},
doi = {10.1145/3097983.3098006},
abstract = {The PART-WHOLE relationship routinely finds itself in many disciplines, ranging from collaborative teams, crowdsourcing, autonomous systems to networked systems. From the algorithmic perspective, the existing work has primarily focused on predicting the outcomes of the whole and parts, by either separate models or linear joint models, which assume the outcome of the parts has a linear and independent effect on the outcome of the whole. In this paper, we propose a joint predictive method named PAROLE to simultaneously and mutually predict the part and whole outcomes. The proposed method offers two distinct advantages over the existing work. First (Model Generality), we formulate joint PART-WHOLE outcome prediction as a generic optimization problem, which is able to encode a variety of complex relationships between the outcome of the whole and parts, beyond the linear independence assumption. Second (Algorithm Efficacy), we propose an effective and efficient block coordinate descent algorithm, which is able to find the coordinate-wise optimum with a linear complexity in both time and space. Extensive empirical evaluations on real-world datasets demonstrate that the proposed PAROLE (1) leads to consistent prediction performance improvement by modeling the non-linear part-whole relationship as well as part-part interdependency, and (2) scales linearly in terms of the size of the training dataset.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {295–304},
numpages = {10},
keywords = {part-whole relationship, joint predictive model},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098077,
author = {Li, Xiaopeng and She, James},
title = {Collaborative Variational Autoencoder for Recommender Systems},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098077},
doi = {10.1145/3097983.3098077},
abstract = {Modern recommender systems usually employ collaborative filtering with rating information to recommend items to users due to its successful performance. However, because of the drawbacks of collaborative-based methods such as sparsity, cold start, etc., more attention has been drawn to hybrid methods that consider both the rating and content information. Most of the previous works in this area cannot learn a good representation from content for recommendation task or consider only text modality of the content, thus their methods are very limited in current multimedia scenario. This paper proposes a Bayesian generative model called collaborative variational autoencoder (CVAE) that considers both rating and content for recommendation in multimedia scenario. The model learns deep latent representations from content data in an unsupervised manner and also learns implicit relationships between items and users from both content and rating. Unlike previous works with denoising criteria, the proposed CVAE learns a latent distribution for content in latent space instead of observation space through an inference network and can be easily extended to other multimedia modalities other than text. Experiments show that CVAE is able to significantly outperform the state-of-the-art recommendation methods with more robust performance.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {305–314},
numpages = {10},
keywords = {bayesian, recommender systems, generative models, autoencoder, deep learning, variational inference},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098081,
author = {Li, Ping},
title = {Linearized GMM Kernels and Normalized Random Fourier Features},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098081},
doi = {10.1145/3097983.3098081},
abstract = {The method of "random Fourier features (RFF)" has become a popular tool for approximating the "radial basis function (RBF)" kernel. The variance of RFF is actually large. Interestingly, the variance can be substantially reduced by a simple normalization step as we theoretically demonstrate. We name the improved scheme as the "normalized RFF (NRFF)", and we provide a technical proof of the asymptotic variance of NRFF, as validated by simulations.We also propose the "generalized min-max (GMM)" kernel as a measure of data similarity, where data vectors can have both positive and negative entries. GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling (GCWS)" which linearizes this (nonlinear) kernel. We provide an extensive empirical evaluation of the RBF and GMM kernels on more than 50 datasets. For a majority of the datasets, the (tuning-free) GMM kernel outperforms the best-tuned RBF kernel.We then conduct extensive classification experiments for comparing the linearized RBF kernel using NRFF with the linearized GMM kernel using GCWS. We observe that, in order to reach a similar accuracy, GCWS typically requires substantially fewer samples than NRFF, even on datasets where the original RBF kernel outperforms the original GMM kernel. As the training, storage, and processing costs are directly proportional to the sample size, our experiments can help demonstrate that GCWS would be a more practical scheme for large-scale machine learning applications.The empirical success of GCWS (compared to NRFF) can also be explained theoretically, from at least two aspects. Firstly, the relative variance (normalized by the squared expectation) of GCWS is substantially smaller than that of NRFF, except for the very high similarity region (where the variances of both methods approach zero). Secondly, if we are allowed to make a general model assumption on the data, then we can show analytically that GCWS exhibits much smaller variance than NRFF for estimating the same object (e.g., the RBF kernel), except for the very high similarity region.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {315–324},
numpages = {10},
keywords = {normalized random fourier features, large-scale machine learning, gmm kernel, hashing},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098008,
author = {Lian, Defu and Liu, Rui and Ge, Yong and Zheng, Kai and Xie, Xing and Cao, Longbing},
title = {Discrete Content-Aware Matrix Factorization},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098008},
doi = {10.1145/3097983.3098008},
abstract = {Precisely recommending relevant items from massive candidates to a large number of users is an indispensable yet computationally expensive task in many online platforms (e.g., Amazon.com and Netflix.com). A promising way is to project users and items into a Hamming space and then recommend items via Hamming distance. However, previous studies didn't address the cold-start challenges and couldn't make the best use of preference data like implicit feedback. To fill this gap, we propose a Discrete Content-aware Matrix Factorization (DCMF) model, 1) to derive compact yet informative binary codes at the presence of user/item content information; 2) to support the classification task based on a local upper bound of logit loss; 3) to introduce an interaction regularization for dealing with the sparsity issue. We further develop an efficient discrete optimization algorithm for parameter learning. Based on extensive experiments on three real-world datasets, we show that DCFM outperforms the state-of-the-arts on both regression and classification tasks.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {325–334},
numpages = {10},
keywords = {collaborative filtering, discrete hashing, content-based filtering, recommendation},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098049,
author = {Liu, Junming and Fu, Yanjie and Ming, Jingci and Ren, Yong and Sun, Leilei and Xiong, Hui},
title = {Effective and Real-Time In-App Activity Analysis in Encrypted Internet Traffic Streams},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098049},
doi = {10.1145/3097983.3098049},
abstract = {The mobile in-App service analysis, aiming at classifying mobile internet traffic into different types of service usages, has become a challenging and emergent task for mobile service providers due to the increasing adoption of secure protocols for in-App services. While some efforts have been made for the classification of mobile internet traffic, existing methods rely on complex feature construction and large storage cache, which lead to low processing speed, and thus not practical for online real-time scenarios. To this end, we develop an iterative analyzer for classifying encrypted mobile traffic in a real-time way. Specifically, we first select an optimal set of most discriminative features from raw features extracted from traffic packet sequences by a novel Maximizing Inner activity similarity and Minimizing Different activity similarity (MIMD) measurement. To develop the online analyzer, we first represent a traffic flow with a series of time windows, which are described by the optimal feature vector and are updated iteratively at the packet level. Instead of extracting feature elements from a series of raw traffic packets, our feature elements are updated when a new traffic packet is observed and the storage of raw traffic packets is not required. The time windows generated from the same service usage activity are grouped by our proposed method, namely, recursive time continuity constrained KMeans clustering (rCKC). The feature vectors of cluster centers are then fed into a random forest classifier to identify corresponding service usages. Finally, we provide extensive experiments on real-world Internet traffic data from Wechat, Whatsapp, and Facebook to demonstrate the effectiveness and efficiency of our approach. The results show that the proposed analyzer provides high accuracy in real-world scenarios, and has low storage cache requirement as well as fast processing speed.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {335–344},
numpages = {10},
keywords = {service usage classification, in-app analytics, internet traffic analysis, time series segmentation},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097984,
author = {Luo, Tingjin and Zhang, Weizhong and Qiu, Shang and Yang, Yang and Yi, Dongyun and Wang, Guangtao and Ye, Jieping and Wang, Jie},
title = {Functional Annotation of Human Protein Coding Isoforms via Non-Convex Multi-Instance Learning},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097984},
doi = {10.1145/3097983.3097984},
abstract = {Functional annotation of human genes is fundamentally important for understanding the molecular basis of various genetic diseases. A major challenge in determining the functions of human genes lies in the functional diversity of proteins, that is, a gene can perform different functions as it may consist of multiple protein coding isoforms (PCIs). Therefore, differentiating functions of PCIs can significantly deepen our understanding of the functions of genes. However, due to the lack of isoform-level gold-standards (ground-truth annotation), many existing functional annotation approaches are developed at gene-level. In this paper, we propose a novel approach to differentiate the functions of PCIs by integrating sparse simplex projection---that is, a nonconvex sparsity-inducing regularizer---with the framework of multi-instance learning (MIL). Specifically, we label the genes that are annotated to the function under consideration as positive bags and the genes without the function as negative bags. Then, by sparse projections onto simplex, we learn a mapping that embeds the original bag space to a discriminative feature space. Our framework is flexible to incorporate various smooth and non-smooth loss functions such as logistic loss and hinge loss. To solve the resulting highly nontrivial non-convex and non-smooth optimization problem, we further develop an efficient block coordinate descent algorithm. Extensive experiments on human genome data demonstrate that the proposed approaches significantly outperform the state-of-the-art methods in terms of functional annotation accuracy of human PCIs and efficiency.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {345–354},
numpages = {10},
keywords = {human pcis, alternative splicing, non-convex problem, multiple instance learning, key instance detection},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098062,
author = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles},
title = {Discovering Reliable Approximate Functional Dependencies},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098062},
doi = {10.1145/3097983.3098062},
abstract = {Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependence of the target on any set of other attributes in the data? How can we reliably, without bias to sample size or dimensionality, measure the strength of such a dependence? And, how can we efficiently discover the optimal or α-approximate top-k dependencies? These are exactly the questions we answer in this paper.As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed. Moreover, we give an effective optimistic estimator of this score, by which for the first time we can mine the approximate functional dependencies from data with guarantees of optimality. Empirical evaluation shows that the derived score achieves a good bias for variance trade-off, can be used within an efficient discovery algorithm, and indeed discovers meaningful dependencies. Most important, it remains reliable in the face of data sparsity.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {355–363},
numpages = {9},
keywords = {information theory, pattern discovery},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097989,
author = {Mautz, Dominik and Ye, Wei and Plant, Claudia and B\"{o}hm, Christian},
title = {Towards an Optimal Subspace for K-Means},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097989},
doi = {10.1145/3097983.3097989},
abstract = {Is there an optimal dimensionality reduction for k-means, revealing the prominent cluster structure hidden in the data? We propose SUBKMEANS, which extends the classic k-means algorithm. The goal of this algorithm is twofold: find a sufficient k-means-style clustering partition and transform the clusters onto a common subspace, which is optimal for the cluster structure. Our solution is able to pursue these two goals simultaneously. The dimensionality of this subspace is found automatically and therefore the algorithm comes without the burden of additional parameters. At the same time this subspace helps to mitigate the curse of dimensionality. The SUBKMEANS optimization algorithm is intriguingly simple and efficient. It is easy to implement and can readily be adopted to the current situation. Furthermore, it is compatible to many existing extensions and improvements of k-means.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {365–373},
numpages = {9},
keywords = {subspace, dimensionality reduction, k-means, clustering},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098014,
author = {Perros, Ioakeim and Papalexakis, Evangelos E. and Wang, Fei and Vuduc, Richard and Searles, Elizabeth and Thompson, Michael and Sun, Jimeng},
title = {SPARTan: Scalable PARAFAC2 for Large & Sparse Data},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098014},
doi = {10.1145/3097983.3098014},
abstract = {In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling medical features across a set of patients, the number and duration of treatments may vary widely in time, meaning there is no meaningful way to align their clinical records across time points for analysis purposes. To handle such data, the state-of-the-art tensor model is the so-called PARAFAC2, which yields interpretable and robust output and can naturally handle sparse data. However, its main limitation up to now has been the lack of efficient algorithms that can handle large-scale datasets.In this work, we fill this gap by developing a scalable method to compute the PARAFAC2 decomposition of large and sparse datasets, called SPARTan. Our method exploits special structure within PARAFAC2, leading to a novel algorithmic reformulation that is both faster (in absolute time) and more memory-efficient than prior work. We evaluate SPARTan on both synthetic and real datasets, showing 22X performance gains over the best previous implementation and also handling larger problem instances for which the baseline fails. Furthermore, we are able to apply SPARTan to the mining of temporally-evolving phenotypes on data taken from real and medically complex pediatric patients. The clinical meaningfulness of the phenotypes identified in this process, as well as their temporal evolution over time for several patients, have been endorsed by clinical experts.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {375–384},
numpages = {10},
keywords = {parafac2, phenotyping, sparse tensor factorization, unsupervised learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098061,
author = {Ribeiro, Leonardo F.R. and Saverese, Pedro H.P. and Figueiredo, Daniel R.},
title = {<i>Struc2vec</i>: Learning Node Representations from Structural Identity},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098061},
doi = {10.1145/3097983.3098061},
abstract = {Structural identity is a concept of symmetry in which network nodes are identified according to the network structure and their relationship to other nodes. Structural identity has been studied in theory and practice over the past decades, but only recently has it been addressed with representational learning techniques. This work presents struc2vec, a novel and flexible framework for learning latent representations for the structural identity of nodes. struc2vec uses a hierarchy to measure node similarity at different scales, and constructs a multilayer graph to encode structural similarities and generate structural context for nodes. Numerical experiments indicate that state-of-the-art techniques for learning node representations fail in capturing stronger notions of structural identity, while struc2vec exhibits much superior performance in this task, as it overcomes limitations of prior approaches. As a consequence, numerical experiments indicate that struc2vec improves performance on classification tasks that depend more on structural identity.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {385–394},
numpages = {10},
keywords = {node embeddings, structural identity, feature learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098046,
author = {Sathe, Saket and Aggarwal, Charu C.},
title = {Similarity Forests},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098046},
doi = {10.1145/3097983.3098046},
abstract = {Random forests are among the most successful methods used in data mining because of their extraordinary accuracy and effectiveness. However, their use is primarily limited to multidimensional data because they sample features from the original data set. In this paper, we propose a method for extending random forests to work with any arbitrary set of data objects, as long as similarities can be computed among the data objects. Furthermore, since it is understood that similarity computation between all O(n2) pairs of n objects might be expensive, our method computes only a very small fraction of the O(n2) pairwise similarities between objects to construct the forests. Our results show that the proposed similarity forest approach is very efficient and accurate on a wide variety of data sets. Therefore, this paper significantly extends the applicability of random forest methods to arbitrary data domains. Furthermore, the approach even outperforms traditional random forests on multidimensional data. We show that similarity forests are robust to the noisy similarity values that are ubiquitous in real-world applications. In many practical settings, the similarity values between objects are incompletely specified because of the difficulty in collecting such values. Similarity forests can be used in such cases with straightforward modifications.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {395–403},
numpages = {9},
keywords = {random forests, classification, data mining},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098025,
author = {Shah, Parikshit and Soni, Akshay and Chevalier, Troy},
title = {Online Ranking with Constraints: A Primal-Dual Algorithm and Applications to Web Traffic-Shaping},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098025},
doi = {10.1145/3097983.3098025},
abstract = {We study the online constrained ranking problem motivated by an application to web-traffic shaping: an online stream of sessions arrive in which, within each session, we are asked to rank items. The challenge involves optimizing the ranking in each session so that local vs. global objectives are controlled: within each session one wishes to maximize a reward (local) while satisfying certain constraints over the entire set of sessions (global). A typical application of this setup is that of page optimization in a web portal. We wish to rank items so that not only is user engagement maximized in each session, but also other business constraints (such as the number of views/clicks delivered to various publishing partners) are satisfied.We describe an online algorithm for performing this optimization. A novel element of our approach is the use of linear programming duality and connections to the celebrated Hungarian algorithm. This framework enables us to determine a set of shadow prices for each traffic-shaping constraint that can then be used directly in the final ranking function to assign near-optimal rankings. The (dual) linear program can be solved off-line periodically to determine the prices. At serving time these prices are used as weights to compute weighted rank-scores for the items, and the simplicity of the approach facilitates scalability to web applications. We provide rigorous theoretical guarantees for the performance of our online algorithm and validate our approach using numerical experiments on real web-traffic data from a prominent internet portal.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {405–414},
numpages = {10},
keywords = {online ranking, primal dual algorithm, traffic shaping, constrained ranking},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097995,
author = {Shen, Chih-Ya and Huang, Liang-Hao and Yang, De-Nian and Shuai, Hong-Han and Lee, Wang-Chien and Chen, Ming-Syan},
title = {On Finding Socially Tenuous Groups for Online Social Networks},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097995},
doi = {10.1145/3097983.3097995},
abstract = {Existing research on finding social groups mostly focuses on dense subgraphs in social networks. However, finding socially tenuous groups also has many important applications. In this paper, we introduce the notion of k-triangles to measure the tenuity of a group. We then formulate a new research problem, Minimum k-Triangle Disconnected Group (MkTG), to find a socially tenuous group from online social networks. We prove that MkTG is NP-Hard and inapproximable within any ratio in arbitrary graphs but polynomial-time tractable in threshold graphs. Two algorithms, namely TERA and TERA-ADV, are designed to exploit graph-theoretical approaches for solving MkTG on general graphs effectively and efficiently. Experimental results on seven real datasets manifest that the proposed algorithms outperform existing approaches in both efficiency and solution quality.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {415–424},
numpages = {10},
keywords = {k-triangles, socially tenuous groups},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097990,
author = {Shi, Yu and Chan, Po-Wei and Zhuang, Honglei and Gui, Huan and Han, Jiawei},
title = {PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097990},
doi = {10.1145/3097983.3097990},
abstract = {As a powerful representation paradigm for networked and multi-typed data, the heterogeneous information network (HIN) is ubiquitous. Meanwhile, defining proper relevance measures has always been a fundamental problem and of great pragmatic importance for network mining tasks. Inspired by our probabilistic interpretation of existing path-based relevance measures, we propose to study HIN relevance from a probabilistic perspective. We also identify, from real-world data, and propose to model cross-meta-path synergy, which is a characteristic important for defining path-based HIN relevance and has not been modeled by existing methods. A generative model is established to derive a novel path-based relevance measure, which is data-driven and tailored for each HIN. We develop an inference algorithm to find the maximum a posteriori (MAP) estimate of the model parameters, which entails non-trivial tricks. Experiments on two real-world datasets demonstrate the effectiveness of the proposed model and relevance measure.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {425–434},
numpages = {10},
keywords = {relevance measures, heterogeneous information networks, graph mining, meta-paths},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098007,
author = {Song, Qingquan and Huang, Xiao and Ge, Hancheng and Caverlee, James and Hu, Xia},
title = {Multi-Aspect Streaming Tensor Completion},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098007},
doi = {10.1145/3097983.3098007},
abstract = {Tensor completion has become an effective computational tool in many real-world data-driven applications. Beyond traditional static setting, with the increasing popularity of high velocity streaming data, it requires efficient online processing without reconstructing the whole model from scratch. Existing work on streaming tensor completion is usually built upon the assumption that tensors only grow in one mode. Unfortunately, the assumption does not hold in many real-world situations in which tensors may grow in multiple modes, i.e., multi-aspect streaming tensors. Efficiently modeling and completing these incremental tensors without sacrificing its effectiveness remains a challenging task due to the uncertainty of tensor mode changes and complex data structure of multi-aspect streaming tensors. To bridge this gap, we propose a Multi-Aspect Streaming Tensor completion framework (MAST) based on CANDECOMP/PARAFAC (CP) decomposition to track the subspace of general incremental tensors for completion. In addition, we investigate a special situation where time is one mode of the tensors, and leverage its extra structure information to improve the general framework towards higher effectiveness. Experimental results on four datasets collected from various real-world applications demonstrate the effectiveness and efficiency of the proposed framework.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {435–443},
numpages = {9},
keywords = {cp decomposition, dynamic tensor analysis, tensor completion},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098035,
author = {Spring, Ryan and Shrivastava, Anshumali},
title = {Scalable and Sustainable Deep Learning via Randomized Hashing},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098035},
doi = {10.1145/3097983.3098035},
abstract = {Current deep learning architectures are growing larger in order to learn from complex datasets. These architectures require giant matrix multiplication operations to train millions of parameters. Conversely, there is another growing trend to bring deep learning to low-power, embedded devices. The matrix operations, associated with the training and testing of deep networks, are very expensive from a computational and energy standpoint. We present a novel hashing-based technique to drastically reduce the amount of computation needed to train and test neural networks. Our approach combines two recent ideas, Adaptive Dropout and Randomized Hashing for Maximum Inner Product Search (MIPS), to select the nodes with the highest activations efficiently. Our new algorithm for deep learning reduces the overall computational cost of the forward and backward propagation steps by operating on significantly fewer nodes. As a consequence, our algorithm uses only 5% of the total multiplications, while keeping within 1% of the accuracy of the original model on average. A unique property of the proposed hashing-based back-propagation is that the updates are always sparse. Due to the sparse gradient updates, our algorithm is ideally suited for asynchronous, parallel training, leading to near-linear speedup, as the number of cores increases. We demonstrate the scalability and sustainability (energy efficiency) of our proposed algorithm via rigorous experimental evaluations on several datasets.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {445–454},
numpages = {10},
keywords = {neural networks, deep learning, locality-sensitive hashing, parallel computing, randomized algorithms},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097987,
author = {Tagami, Yukihiro},
title = {AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-Label Classification},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097987},
doi = {10.1145/3097983.3097987},
abstract = {Extreme multi-label classification methods have been widely used in Web-scale classification tasks such as Web page tagging and product recommendation. In this paper, we present a novel graph embedding method called "AnnexML". At the training step, AnnexML constructs a k-nearest neighbor graph of label vectors and attempts to reproduce the graph structure in the embedding space. The prediction is efficiently performed by using an approximate nearest neighbor search method that efficiently explores the learned k-nearest neighbor graph in the embedding space. We conducted evaluations on several large-scale real-world data sets and compared our method with recent state-of-the-art methods. Experimental results show that our AnnexML can significantly improve prediction accuracy, especially on data sets that have larger a label space. In addition, AnnexML improves the trade-off between prediction time and accuracy. At the same level of accuracy, the prediction time of AnnexML was up to 58 times faster than that of SLEEC, which is a state-of-the-art embedding-based method.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {455–464},
numpages = {10},
keywords = {k-nearest neighbor graph, extreme multi-label classification, learning-to-rank, approximate nearest neighbor search},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098039,
author = {Tolomei, Gabriele and Silvestri, Fabrizio and Haines, Andrew and Lalmas, Mounia},
title = {Interpretable Predictions of Tree-Based Ensembles via Actionable Feature Tweaking},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098039},
doi = {10.1145/3097983.3098039},
abstract = {Machine-learned models are often described as "black boxes". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst some features are inherently static, representing properties that cannot be influenced (e.g., the age of an individual), others capture characteristics that could be adjusted (e.g., the daily amount of carbohydrates taken). Nonetheless, once a model is learned from the data, each prediction it makes on new instances is irreversible - assuming every instance to be a static point located in the chosen feature space. There are many circumstances however where it is important to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter such a prediction when the mutated instance is input back to the model.In this paper, we present a technique that exploits the internals of a tree-based ensemble classifier to offer recommendations for transforming true negative instances into positively predicted ones. We demonstrate the validity of our approach using an online advertising application. First, we design a Random Forest classifier that effectively separates between two types of ads: low (negative) and high (positive) quality ads (instances). Then, we introduce an algorithm that provides recommendations that aim to transform a low quality ad (negative instance) into a high quality one (positive instance). Finally, we evaluate our approach on a subset of the active inventory of a large ad network, Yahoo Gemini.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {465–474},
numpages = {10},
keywords = {random forest, actionable feature tweaking, altering model predictions, recommending feature changes, model interpretability},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097988,
author = {Wang, Shen and He, Lifang and Cao, Bokai and Lu, Chun-Ta and Yu, Philip S. and Ragin, Ann B.},
title = {Structural Deep Brain Network Mining},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097988},
doi = {10.1145/3097983.3097988},
abstract = {Mining from neuroimaging data is becoming increasingly popular in the field of healthcare and bioinformatics, due to its potential to discover clinically meaningful structure patterns that could facilitate the understanding and diagnosis of neurological and neuropsychiatric disorders. Most recent research concentrates on applying subgraph mining techniques to discover connected subgraph patterns in the brain network. However, the underlying brain network structure is complicated. As a shallow linear model, subgraph mining cannot capture the highly non-linear structures, resulting in sub-optimal patterns. Therefore, how to learn representations that can capture the highly non-linearity of brain networks and preserve the underlying structures is a critical problem.In this paper, we propose a Structural Deep Brain Network mining method, namely SDBN, to learn highly non-linear and structure-preserving representations of brain networks. Specifically, we first introduce a novel graph reordering approach based on module identification, which rearranges the order of the nodes to preserve the modular structure of the graph. Next, we perform structural augmentation to further enhance the spatial information of the reordered graph. Then we propose a deep feature learning framework for combining supervised learning and unsupervised learning in a small-scale setting, by augmenting Convolutional Neural Network (CNN) with decoding pathways for reconstruction. With the help of the multiple layers of non-linear mapping, the proposed SDBN approach can capture the highly non-linear structure of brain networks. Further, it has better generalization capability for high-dimensional brain networks and works well even for small sample learning. Benefit from CNN's task-oriented learning style, the learned hierarchical representation is meaningful for the clinical task. To evaluate the proposed SDBN method, we conduct extensive experiments on four real brain network datasets for disease diagnoses. The experiment results show that SDBN can capture discriminative and meaningful structural graph representations for brain disorder diagnosis.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {475–484},
numpages = {10},
keywords = {graph reordering, brain network, deep learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098001,
author = {Wang, Suhang and Aggarwal, Charu and Liu, Huan},
title = {Randomized Feature Engineering as a Fast and Accurate Alternative to Kernel Methods},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098001},
doi = {10.1145/3097983.3098001},
abstract = {Feature engineering has found increasing interest in recent years because of its ability to improve the effectiveness of various machine learning models. Although tailored feature engineering methods have been designed for various domains, there are few that simulate the consistent effectiveness of kernel methods. At the core, the success of kernel methods is achieved by using similarity functions that emphasize local variations in similarity. Unfortunately, this ability comes at the price of the high level of computational resources required and the inflexibility of the representation as it only provides the similarity of two data points instead of vector representations of each data point; while the vector representations can be readily used as input to facilitate various models for different tasks. Furthermore, kernel methods are also highly susceptible to overfitting and noise and it cannot capture the variety of data locality. In this paper, we first analyze the inner working and weaknesses of kernel method, which serves as guidance for designing feature engineering. With the guidance, we explore the use of randomized methods for feature engineering by capturing multi-granular locality of data. This approach has the merit of being time and space efficient for feature construction. Furthermore, the approach is resistant to overfitting and noise because the randomized approach naturally enables fast and robust ensemble methods. Extensive experiments on a number of real world datasets are conducted to show the effectiveness of the approach for various tasks such as clustering, classification and outlier detection.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {485–494},
numpages = {10},
keywords = {randomized feature engineering, unsupervised feature learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098067,
author = {Wang, Pengfei and Fu, Yanjie and Liu, Guannan and Hu, Wenqing and Aggarwal, Charu},
title = {Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098067},
doi = {10.1145/3097983.3098067},
abstract = {While exploring human mobility can benefit many applications such as smart transportation, city planning, and urban economics, there are two key questions that need to be answered: (i) What is the nature of the spatial diffusion of human mobility across regions with different urban functions? (ii) How to spot and trace the trip purposes of human mobility trajectories? To answer these questions, we study large-scale and city-wide taxi trajectories; and furtherly organize them as arrival sequences according to the chronological arrival time. We figure out an important property across different regions from the arrival sequences, namely human mobility synchronization effect, which can be exploited to explain the phenomenon that two regions have similar arrival patterns in particular time periods if they share similar urban functions. In addition, the arrival sequences are mixed by arrival events with distinct trip purposes, which can be revealed by the regional environment of both the origins and destinations. To that end, in this paper, we develop a joint model that integrates Mixture of Hawkes Process (MHP) with a hierarchical topic model to capture the arrival sequences with mixed trip purposes. Essentially, the human mobility synchronization effect is encoded as a synchronization rate in the MHP; while the regional environment is modeled by introducing latent Trip Purpose and POI Topic to generate the Point of Interests (POIs) in the regions. Moreover, we provide an effective inference algorithm for parameter learning. Finally, we conduct intensive experiments on synthetic data and real-world data, and the experimental results have demonstrated the effectiveness of the proposed model.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {495–503},
numpages = {9},
keywords = {trip purpose, variational inference, hawkes process, synchronization, human mobility},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098072,
author = {Wang, Sibo and Yang, Renchi and Xiao, Xiaokui and Wei, Zhewei and Yang, Yin},
title = {FORA: Simple and Effective Approximate Single-Source Personalized PageRank},
year = {2017},
isbn = {978145034887

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr