text:
{New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623350,
author = {Spasojevic, Nemanja and Yan, Jinyun and Rao, Adithya and Bhattacharyya, Prantik},
title = {LASTA: Large Scale Topic Assignment on Multiple Social Networks},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623350},
doi = {10.1145/2623330.2623350},
abstract = {Millions of people use social networks everyday to talk about a variety of subjects, publish opinions and share information. Understanding this data to infer user's topical interests is a challenging problem with applications in various data-powered products. In this paper, we present 'LASTA' (Large Scale Topic Assignment), a full production system used at Klout, Inc., which mines topical interests from five social networks and assigns over 10,000 topics to hundreds of millions of users on a daily basis. The system continuously collects streams of user data and is reactive to fresh information, updating topics for users as interests shift. LASTA generates over 50 distinct features derived from signals such as user generated posts and profiles, user reactions such as comments and retweets, user attributions such as lists, tags and endorsements, as well as signals based on social graph connections. We show that using this diverse set of features leads to a better representation of a user's topical interests as compared to using only generated text or only graph based features. We also show that using cross-network information for a user leads to a more complete and accurate understanding of the user's topics, as compared to using any single network. We evaluate LASTA's topic assignment system on an internal labeled corpus of 32,264 user-topic labels generated from real users.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1809–1818},
numpages = {10},
keywords = {topic assignment, large scale, distributed systems, user modeling, online social networks, interest mining},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623359,
author = {Zoeter, Onno and Dance, Christopher and Clinchant, St\'{e}phane and Andreoli, Jean-Marc},
title = {New Algorithms for Parking Demand Management and a City-Scale Deployment},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623359},
doi = {10.1145/2623330.2623359},
abstract = {On-street parking, just as any publicly owned utility, is used inefficiently if access is free or priced very far from market rates. This paper introduces a novel demand management solution: using data from dedicated occupancy sensors an iteration scheme updates parking rates to better match demand. The new rates encourage parkers to avoid peak hours and peak locations and reduce congestion and underuse. The solution is deliberately simple so that it is easy to understand, easily seen to be fair and leads to parking policies that are easy to remember and act upon. We study the convergence properties of the iteration scheme and prove that it converges to a reasonable distribution for a very large class of models. The algorithm is in use to change parking rates in over 6000 spaces in downtown Los Angeles since June 2012 as part of the LA Express Park project. Initial results are encouraging with a reduction of congestion and underuse, while in more locations rates were decreased than increased.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1819–1828},
numpages = {10},
keywords = {transportation, demand management},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623331,
author = {Shakarian, Paulo and Salmento, Joseph and Pulleyblank, William and Bertetto, John},
title = {Reducing Gang Violence through Network Influence Based Targeting of Social Programs},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623331},
doi = {10.1145/2623330.2623331},
abstract = {In this paper, we study a variant of the social network maximum influence problem and its application to intelligently approaching individual gang members with incentives to leave a gang. The goal is to identify individuals who when influenced to leave gangs will propagate this action. We study this emerging application by exploring specific facets of the problem that must be addressed when modeling this particular situation. We formulate a new influence maximization variant - the "social incentive influence" (SII) problem and study it both formally and in the context of the law-enforcement domain. Using new techniques from unconstrained submodular maximization, we develop an approximation algorithm for SII and present a suite of experimental results - including tests on real-world police data from Chicago.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1829–1836},
numpages = {8},
keywords = {complex networks, propagation in networks, network diffusion},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623356,
author = {Lee, Pei and Lakshmanan, Laks V.S. and Tiwari, Mitul and Shah, Sam},
title = {Modeling Impression Discounting in Large-Scale Recommender Systems},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623356},
doi = {10.1145/2623330.2623356},
abstract = {Recommender systems have become very important for many online activities, such as watching movies, shopping for products, and connecting with friends on social networks. User behavioral analysis and user feedback (both explicit and implicit) modeling are crucial for the improvement of any online recommender system. Widely adopted recommender systems at LinkedIn such as "People You May Know" and "Endorsements" are evolving by analyzing user behaviors on impressed recommendation items.In this paper, we address modeling impression discounting of recommended items, that is, how to model user's no-action feedback on impressed recommended items. The main contributions of this paper include (1) large-scale analysis of impression data from LinkedIn and KDD Cup; (2) novel anti-noise regression techniques, and its application to learn four different impression discounting functions including linear decay, inverse decay, exponential decay, and quadratic decay; (3) applying these impression discounting functions to LinkedIn's "People You May Know" and "Endorsements" recommender systems.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1837–1846},
numpages = {10},
keywords = {impression discounting, recommender system},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623375,
author = {Beckman, Richard and Bisset, Keith R. and Chen, Jiangzhuo and Lewis, Bryan and Marathe, Madhav and Stretz, Paula},
title = {ISIS: A Networked-Epidemiology Based Pervasive Web App for Infectious Disease Pandemic Planning and Response},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623375},
doi = {10.1145/2623330.2623375},
abstract = {We describe ISIS, a high-performance-computing-based application to support computational epidemiology of infectious diseases. ISIS has been developed over the last seven years in close coordination with public health and policy experts. It has been used in a number of important federal planning and response exercises. ISIS grew out of years of experience in developing and using HPC-oriented models of complex socially coupled systems. This identified the guiding principle that complex models will be used by domain experts only if they can do realistic analysis without becoming computing experts.Using ISIS, one can carry out detailed computational experiments as they pertain to planning and response in the event of a pandemic. ISIS is designed to support networked epidemiology -- study of epidemic processes over social contact networks. The current system can handle airborne infectious diseases such as influenza, pertussis, and smallpox. ISIS is comprised of the following basic components: (i) a web app that serves as the user-interface, (ii) a middleware that coordinates user interaction via the web app with backend models and databases, (iii) a backend computational modeling framework that is comprised of highly resolved epidemic simulations combined with highly realistic control strategies that include pharmaceutical as well as non-pharmaceutical interventions and (iv) a backend data management framework that manages complex unstructured and semi-structured data.ISIS has been used in over a dozen case studies defined by the DoD, DHHS, NIH, BARDA and NSC. We describe three recent studies illustrating the use of ISIS in real-world settings:(i) uses of ISIS during the H1N1 pandemic, Cii) supporting a US military planning exercise, and (iii) distribution of limited stockpile of pharmaceuticals using public and private outlets.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1847–1856},
numpages = {10},
keywords = {computational epidemiology, public health, hpc, web app, simulation},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623341,
author = {Kohavi, Ron and Deng, Alex and Longbotham, Roger and Xu, Ya},
title = {Seven Rules of Thumb for Web Site Experimenters},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623341},
doi = {10.1145/2623330.2623341},
abstract = {Web site owners, from small web sites to the largest properties that include Amazon, Facebook, Google, LinkedIn, Microsoft, and Yahoo, attempt to improve their web sites, optimizing for criteria ranging from repeat usage, time on site, to revenue. Having been involved in running thousands of controlled experiments at Amazon, Booking.com, LinkedIn, and multiple Microsoft properties, we share seven rules of thumb for experimenters, which we have generalized from these experiments and their results. These are principles that we believe have broad applicability in web optimization and analytics outside of controlled experiments, yet they are not provably correct, and in some cases exceptions are known.To support these rules of thumb, we share multiple real examples, most being shared in a public paper for the first time. Some rules of thumb have previously been stated, such as 'speed matters,' but we describe the assumptions in the experimental design and share additional experiments that improved our understanding of where speed matters more: certain areas of the web page are more critical.This paper serves two goals. First, it can guide experimenters with rules of thumb that can help them optimize their sites. Second, it provides the KDD community with new research challenges on the applicability, exceptions, and extensions to these, one of the goals for KDD's industrial track.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1857–1866},
numpages = {10},
keywords = {controlled experiments, a/b testing, randomized experiments},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623340,
author = {Sipos, Ruben and Fradkin, Dmitriy and Moerchen, Fabian and Wang, Zhuang},
title = {Log-Based Predictive Maintenance},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623340},
doi = {10.1145/2623330.2623340},
abstract = {Success of manufacturing companies largely depends on reliability of their products. Scheduled maintenance is widely used to ensure that equipment is operating correctly so as to avoid unexpected breakdowns. Such maintenance is often carried out separately for every component, based on its usage or simply on some fixed schedule. However, scheduled maintenance is labor-intensive and ineffective in identifying problems that develop between technician's visits. Unforeseen failures still frequently occur. In contrast, predictive maintenance techniques help determine the condition of in-service equipment in order to predict when and what repairs should be performed. The main goal of predictive maintenance is to enable pro-active scheduling of corrective work, and thus prevent unexpected equipment failures.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1867–1876},
numpages = {10},
keywords = {crisp-dm, predictive maintenance, log mining, machine learning},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623667,
author = {Spangler, Scott and Wilkins, Angela D. and Bachman, Benjamin J. and Nagarajan, Meena and Dayaram, Tajhal and Haas, Peter and Regenbogen, Sam and Pickering, Curtis R. and Comer, Austin and Myers, Jeffrey N. and Stanoi, Ioana and Kato, Linda and Lelescu, Ana and Labrie, Jacques J. and Parikh, Neha and Lisewski, Andreas Martin and Donehower, Lawrence and Chen, Ying and Lichtarge, Olivier},
title = {Automated Hypothesis Generation Based on Mining Scientific Literature},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623667},
doi = {10.1145/2623330.2623667},
abstract = {Keeping up with the ever-expanding flow of data and publications is untenable and poses a fundamental bottleneck to scientific progress. Current search technologies typically find many relevant documents, but they do not extract and organize the information content of these documents or suggest new scientific hypotheses based on this organized content. We present an initial case study on KnIT, a prototype system that mines the information contained in the scientific literature, represents it explicitly in a queriable network, and then further reasons upon these data to generate novel and experimentally testable hypotheses. KnIT combines entity detection with neighbor-text feature analysis and with graph-based diffusion of information to identify potential new properties of entities that are strongly implied by existing relationships. We discuss a successful application of our approach that mines the published literature to identify new protein kinases that phosphorylate the protein tumor suppressor p53. Retrospective analysis demonstrates the accuracy of this approach and ongoing laboratory experiments suggest that kinases identified by our system may indeed phosphorylate p53. These results establish proof of principle for automated hypothesis generation and discovery based on text mining of the scientific literature.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1877–1886},
numpages = {10},
keywords = {text mining, scientific discovery, hypothesis generation},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623377,
author = {Srikant, Shashank and Aggarwal, Varun},
title = {A System to Grade Computer Programming Skills Using Machine Learning},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623377},
doi = {10.1145/2623330.2623377},
abstract = {The automatic evaluation of computer programs is a nascent area of research with a potential for large-scale impact. Extant program assessment systems score mostly based on the number of test-cases passed, providing no insight into the competency of the programmer. In this paper, we present a system to grade computer programs automatically. In addition to grading a program on its programming practices and complexity, the key kernel of the system is a machine-learning based algorithm which determines closeness of the logic of the given program to a correct program. This algorithm uses a set of highly-informative features, derived from the abstract representations of a given program, that capture the program's functionality. These features are then used to learn a model to grade the programs, which are built against evaluations done by experts. We show that the regression models provide much better grading than the ubiquitous test-case-pass based grading and rivals the grading accuracy of other open-response problems such as essay grading . We also show that our novel features add significant value over and above basic keyword/expression count features. In addition to this, we propose a novel way of posing computer-program grading as a one-class modeling problem and report encouraging preliminary results. We show the value of the system through a case study in a real-world industrial deployment. To the best of the authors' knowledge, this is the first time a system using machine learning has been developed and used for grading programs. The work is timely with regard to the recent boom in Massively Online Open Courseware (MOOCs), which promises to produce a significant amount of hand-graded digitized data.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1887–1896},
numpages = {10},
keywords = {one-class learning, feature engineering, supervised learning, mooc, automatic grading, recruitment},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623357,
author = {Yuan, Shuai and Wang, Jun and Chen, Bowei and Mason, Peter and Seljan, Sam},
title = {An Empirical Study of Reserve Price Optimisation in Real-Time Bidding},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623357},
doi = {10.1145/2623330.2623357},
abstract = {In this paper, we report the first empirical study and live test of the reserve price optimisation problem in the context of Real-Time Bidding (RTB) display advertising from an operational environment. A reserve price is the minimum that the auctioneer would accept from bidders in auctions, and in a second price auction it could potentially uplift the auctioneer's revenue by charging winners the reserve price instead of the second highest bids. As such it has been used for sponsored search and been well studied in that context. However, comparing with sponsored search and contextual advertising, this problem in the RTB context is less understood yet more critical for publishers because 1) bidders have to submit a bid for each individual impression, which mostly is associated with user data that is subject to change over time. This, coupled with practical constraints such as the budget, campaigns' life time, etc. makes the theoretical result from optimal auction theory not necessarily applicable and a further empirical study is required to confirm its optimality from the real-world system; 2) in RTB an advertiser is facing nearly unlimited supply and the auction is almost done in "last second", which encourages spending less on the high cost ad placements. This could imply the loss of bid volume over time if a correct reserve price is not in place. In this paper we empirically examine several commonly adopted algorithms for setting up a reserve price. We report our results of a large scale online experiment in a production platform. The results suggest the our proposed game theory based OneShot algorithm performed the best and the superiority is significant in most cases.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1897–1906},
numpages = {10},
keywords = {display advertising, online advertising, real-time bidding, reserve price, revenue optimisation},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623336,
author = {Yang, Shuang-Hong and Kolcz, Alek and Schlaikjer, Andy and Gupta, Pankaj},
title = {Large-Scale High-Precision Topic Modeling on Twitter},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623336},
doi = {10.1145/2623330.2623336},
abstract = {We are interested in organizing a continuous stream of sparse and noisy texts, known as "tweets", in real time into an ontology of hundreds of topics with measurable and stringently high precision. This inference is performed over a full-scale stream of Twitter data, whose statistical distribution evolves rapidly over time. The implementation in an industrial setting with the potential of affecting and being visible to real users made it necessary to overcome a host of practical challenges. We present a spectrum of topic modeling techniques that contribute to a deployed system. These include non-topical tweet detection, automatic labeled data acquisition, evaluation with human computation, diagnostic and corrective learning and, most importantly, high-precision topic inference. The latter represents a novel two-stage training algorithm for tweet text classification and a close-loop inference mechanism for combining texts with additional sources of information. The resulting system achieves 93% precision at substantial overall coverage.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1907–1916},
numpages = {10},
keywords = {topic modeling, large-scale machine learning, text classification, social media},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623332,
author = {Jagadeesh, Vignesh and Piramuthu, Robinson and Bhardwaj, Anurag and Di, Wei and Sundaresan, Neel},
title = {Large Scale Visual Recommendations from Street Fashion Images},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623332},
doi = {10.1145/2623330.2623332},
abstract = {We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data. Specifically, we propose two classes of data driven models in the Deterministic Fashion Recommenders (DFR) and Stochastic Fashion Recommenders (SFR) for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive experimentation on a large-scale data set and baseline them against existing ideas from color science. We also illustrate key fashion insights learned through these experiments and show how they can be employed to design better recommendation systems. The industrial applicability of proposed models is in the context of mobile fashion shopping. Finally, we also outline a large-scale annotated data set of fashion images Fashion-136K) that can be exploited for future research in data driven visual fashion.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1925–1934},
numpages = {10},
keywords = {user behavior, color modeling, visual recommenders, e-commerce, fashion},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623351,
author = {Zhao, Xin Wayne and Guo, Yanwei and He, Yulan and Jiang, Han and Wu, Yuexin and Li, Xiaoming},
title = {We Know What You Want to Buy: A Demographic-Based System for Product Recommendation on Microblogs},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623351},
doi = {10.1145/2623330.2623351},
abstract = {Product recommender systems are often deployed by e-commerce websites to improve user experience and increase sales. However, recommendation is limited by the product information hosted in those e-commerce sites and is only triggered when users are performing e-commerce activities. In this paper, we develop a novel product recommender system called METIS, a MErchanT Intelligence recommender System, which detects users' purchase intents from their microblogs in near real-time and makes product recommendation based on matching the users' demographic information extracted from their public profiles with product demographics learned from microblogs and online reviews. METIS distinguishes itself from traditional product recommender systems in the following aspects: 1) METIS was developed based on a microblogging service platform. As such, it is not limited by the information available in any specific e-commerce website. In addition, METIS is able to track users' purchase intents in near real-time and make recommendations accordingly. 2) In METIS, product recommendation is framed as a learning to rank problem. Users' characteristics extracted from their public profiles in microblogs and products' demographics learned from both online product reviews and microblogs are fed into learning to rank algorithms for product recommendation. We have evaluated our system in a large dataset crawled from Sina Weibo. The experimental results have verified the feasibility and effectiveness of our system. We have also made a demo version of our system publicly available and have implemented a live system which allows registered users to receive recommendations in real time.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1935–1944},
numpages = {10},
keywords = {e-commerce, product recommender, product demographic, microblog},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623368,
author = {Xu, Ye and Li, Zang and Gupta, Abhishek and Bugdayci, Ahmet and Bhasin, Anmol},
title = {Modeling Professional Similarity by Mining Professional Career Trajectories},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623368},
doi = {10.1145/2623330.2623368},
abstract = {For decades large corporations as well as labor placement services have maintained extensive yet static resume databanks. Online professional networks like LinkedIn have taken these resume databanks to a dynamic, constantly updated and massive scale professional profile dataset spanning career records from hundreds of industries, millions of companies and hundreds of millions of people worldwide. Using this professional profile dataset, this paper attempts to model profiles of individuals as a sequence of positions held by them as a time-series of nodes, each of which represents one particular position or job experience in the individual's career trajectory. These career trajectory models can be employed in various utility applications including career trajectory planning for students in schools & universities using knowledge inferred from real world career outcomes. They can also be employed for decoding sequences to uncover paths leading to certain professional milestones from a user's current professional status. We deploy the proposed technique to ascertain professional similarity between two individuals by developing a similarity measure SimCareers (Similar Career Paths). The measure employs sequence alignment between two career trajectories to quantify professional similarity between career paths. To the best of our knowledge, SimCareers is the first framework to model professional similarity between two people taking account their career trajectory information. We posit, that using the temporal and structural features of a career trajectory for modeling profile similarity is a far more superior approach than using similarity measures on semi-structured attribute representation of a profile for this application. We validate our hypothesis by extensive quantitative evaluations on a gold dataset of similar profiles generated from recruiting activity logs from actual recruiters using LinkedIn. In addition, we show significant improvements in engagement by running an A/B test on a real-world application called Similar Profiles on LinkedIn, world's largest online professional network.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1945–1954},
numpages = {10},
keywords = {similarity, social networks, user profiles, career paths},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623334,
author = {Tagami, Yukihiro and Hotta, Toru and Tanaka, Yusuke and Ono, Shingo and Tsukamoto, Koji and Tajima, Akira},
title = {Filling Context-Ad Vocabulary Gaps with Click Logs},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623334},
doi = {10.1145/2623330.2623334},
abstract = {Contextual advertising is a form of textual advertising usually displayed on third party Web pages. One of the main problems with contextual advertising is determining how to select ads that are relevant to the page content and/or the user information in order to achieve both effective advertising and a positive user experience. Typically, the relevance of an ad to page content is indicated by a tf-idf score that measures the word overlap between the page and the ad content, so this problem is transformed into a similarity search in a vector space. However, such an approach is not useful if the vocabulary used on the page is expected to be different from that in the ad. There have been studies proposing the use of semantic categories or hidden classes to overcome this problem. With these approaches it is necessary to expand the ad retrieval system or build new index to handle the categories or classes, and it is not always easy to maintain the number of categories and classes required for business needs. In this work, we propose a translation method that learns the mapping of the contextual information to the textual features of ads by using past click data. The contextual information includes the user's demographic information and behavioral information as well as page content information. The proposed method is able to retrieve more preferable ads while maintaining the sparsity of the inverted index and the performance of the ad retrieval system. In addition, it is easy to implement and there is no need to modify an existing ad retrieval system. We evaluated this approach offline on a data set based on logs from an ad network. Our method achieved better results than existing methods. We also applied our approach with a real ad serving system and compared the online performance using A/B testing. Our approach achieved an improvement over the existing production system.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1955–1964},
numpages = {10},
keywords = {modeling, contextual advertising, learning-to-rank, click feedback},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630814,
author = {Ramakrishnan, Raghu and Webb, Geoffrey I.},
title = {Does Social Good Justify Risking Personal Privacy?},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630814},
doi = {10.1145/2623330.2630814},
abstract = {When data-driven improvements involve personally identifiable data, or even data that can be used to infer sensitive information about individuals, we face the dilemma that we potentially risk compromising privacy. As we see increased emphasis on using data mining to effect improvements in a range of socially beneficial activities, from improving matching of talented students to opportunities for higher education, or improving allocation of funds across competing school programs, or reducing hospitalization time following surgery, the dilemma can often be especially acute. The data involved often is personally identifiable or revealing and sensitive, and many of the institutions that must be involved in gathering and maintaining custody of the data are not equipped to adequately secure the data, raising the risk of privacy breaches. How should we approach this trade-off? Can we assess the risks? Can we control or mitigate them? Can we develop guidelines for when the risk is or is not worthwhile, and for how best to handle data in different common scenarios? Chairs Raghu Ramakrishnan and Geoffrey I. Webb bring this panel of leading data miners and privacy experts together to address these critical issues.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1965},
numpages = {1},
keywords = {social good, privacy},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630802,
author = {Bengio, Yoshua},
title = {Scaling up Deep Learning},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630802},
doi = {10.1145/2623330.2630802},
abstract = {Deep learning has rapidly moved from a marginal approach in the machine learning community less than ten years ago to one that has strong industrial impact, in particular for high-dimensional perceptual data such as speech and images, but also natural language. The demand for experts in deep learning is growing very fast (faster than we can graduate PhDs), thereby considerably increasing their market value. Deep learning is based on the idea of learning multiple levels of representation, with higher levels computed as a function of lower levels, and corresponding to more abstract concepts automatically discovered by the learner. Deep learning arose out of research on artificial neural networks and graphical models and the literature on that subject has considerably grown in recent years, culminating in the creation of a dedicated conference (ICLR). The tutorial will introduce some of the basic algorithms, both on the supervised and unsupervised sides, as well as discuss some of the guidelines for successfully using them in practice. Finally, it will introduce current research questions regarding the challenge of scaling up deep learning to much larger models that can successfully extract information from huge datasets.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1966},
numpages = {1},
keywords = {conditional computing, deep learning, big data, neural networks},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630803,
author = {Bordes, Antoine and Gabrilovich, Evgeniy},
title = {Constructing and Mining Web-Scale Knowledge Graphs: KDD 2014 Tutorial},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630803},
doi = {10.1145/2623330.2630803},
abstract = {Recent years have witnessed a proliferation of large-scale knowledge graphs, such as Freebase, YAGO, Google's Knowledge Graph, and Microsoft's Satori. Whereas there is a large body of research on mining homogeneous graphs, this new generation of information networks are highly heterogeneous, with thousands of entity and relation types and billions of instances of vertices and edges. In this tutorial, we will present the state of the art in constructing, mining, and growing knowledge graphs. The purpose of the tutorial is to equip newcomers to this exciting field with an understanding of the basic concepts, tools and methodologies, available datasets, and open research challenges. A publicly available knowledge base (Freebase) will be used throughout the tutorial to exemplify the different techniques.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1967},
numpages = {1},
keywords = {big knowledge},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630804,
author = {Han, Jiawei and Wang, Chi and El-Kishky, Ahmed},
title = {Bringing Structure to Text: Mining Phrases, Entities, Topics, and Hierarchies},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630804},
doi = {10.1145/2623330.2630804},
abstract = {Mining phrases, entity concepts, topics, and hierarchies from massive text corpus is an essential problem in the age of big data. Text data in electronic forms are ubiquitous, ranging from scientific articles to social networks, enterprise logs, news articles, social media and general web pages. It is highly desirable but challenging to bring structure to unstructured text data, uncover underlying hierarchies, relationships, patterns and trends, and gain knowledge from such data.In this tutorial, we provide a comprehensive survey on the state-of-the art of data-driven methods that automatically mine phrases, extract and infer latent structures from text corpus, and construct multi-granularity topical groupings and hierarchies of the underlying themes. We study their principles, methodologies, algorithms and applications using several real datasets including research papers and news articles and demonstrate how these methods work and how the uncovered latent entity structures may help text understanding, knowledge discovery and management.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1968},
numpages = {1},
keywords = {information networks, phrase mining, text mining, topic model},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630805,
author = {Marathe, Madhav V. and Vullikanti, Anil Kumar S.},
title = {Computational Epidemiology},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630805},
doi = {10.1145/2623330.2630805},
abstract = {As recent pandemics such as SARS and the Swine Flu outbreak have shown, diseases spread very fast in today's interconnected world, making public health an important research area. Some of the basic questions are: How can an outbreak be contained before it becomes an epidemic, and what disease surveillance strategies should be implemented? These problems have been studied traditionally using differential equation methods, which are amenable to analysis and closed form solutions. However, these models are based on complete mixing assumptions, which do not hold for realistic populations, thereby limiting their utility.In this tutorial, we focus on an approach based on diffusion processes on complex networks. This captures more realistic populations, but leads to novel mathematical and computational challenges. The structure of the underlying networks has a significant impact on the dynamical properties, motivating the need for improved network models, and efficient algorithms for computing network and dynamical properties that scale to large networks. We provide an overview of the state of the art in computational epidemiology, which is a multi-disciplinary research area, that overlaps different areas in computer science, including data mining, machine learning, high performance computing and theoretical computer science, as well as mathematics, economics and statistics. Specifically, we will discuss mathematical and computational models, problems of inference, forecasting and state assessment, and epidemic containment.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1969},
numpages = {1},
keywords = {agent based simulations, social networks, epidemiology, diffusion},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630806,
author = {Feng, Mengling and Ghassemi, Mohammad and Brennan, Thomas and Ellenberger, John and Hussain, Ishrar and Mark, Roger},
title = {Management and Analytic of Biomedical Big Data with Cloud-Based in-Memory Database and Dynamic Querying: A Hands-on Experience with Real-World Data},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630806},
doi = {10.1145/2623330.2630806},
abstract = {Analyzing Biomedical Big Data (BBD) is computationally expensive due to high dimensionality and large data volume. Performance and scalability issues of traditional database management systems (DBMS) often limit the usage of more sophisticated and complex data queries and analytic models. Moreover, in the conventional setting, data management and analysis use separate software platforms. Exporting and importing large amounts of data across platforms require a significant amount of computational and I/O resources, as well as potentially putting sensitive data at a security risk. In this tutorial, the participants will learn the difference between in-memory DBMS and traditional DBMS through hands-on exercises using SAP's cloud-based HANA in-memory DBMS in conjunction with the Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) dataset. MIMIC is an open-access critical care EHR archive (over 4TB in size) and consists of structured, unstructured and waveform data. Furthermore, this tutorial will seek to educate the participants on how a combination of dynamic querying, and in-memory DBMS may enhance the management and analysis of complex clinical data.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1970},
numpages = {1},
keywords = {in-memory dbms, big data, dynamic querying, biomedical data},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630807,
author = {Amatriain, Xavier and Mobasher, Bamshad},
title = {The Recommender Problem Revisited: Morning Tutorial},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630807},
doi = {10.1145/2623330.2630807},
abstract = {In 2006, Netflix announced a $1M prize competition to advance recommendation algorithms. The recommendation problem was simplified as the accuracy in predicting a user rating measured by the Root Mean Squared Error. While that formulation helped get the attention of the research community in the area, it may have put an excessive focus on what is simply one of possible approaches to recommendations. In this tutorial we will describe different components of modern recommender systems such as: personalized ranking, similarity, explanations, context-awareness, or search as recommendation. In the first part, we will use the Netflix use case as a driving example of a prototypical industrial-scale recommender system. We will also review the usage of modern algorithmic approaches that include algorithms such as Factorization Machines, Restricted Boltzmann Machines, SimRank, Deep Neural Networks, or Listwise Learning-to-rank. In the second part, we will focus on the area of context-aware recommendations where the two dimensional user-item recommender problem is turned into an n-dimensional space.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1971},
numpages = {1},
keywords = {personalization, recommender systems, data mining, machine learning},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630808,
author = {Bonchi, Francesco and Garcia-Soriano, David and Liberty, Edo},
title = {Correlation Clustering: From Theory to Practice},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630808},
doi = {10.1145/2623330.2630808},
abstract = {Correlation clustering is arguably the most natural formulation of clustering. Given a set of objects and a pairwise similarity measure between them, the goal is to cluster the objects so that, to the best possible extent, similar objects are put in the same cluster and dissimilar objects are put in different clusters. As it just needs a definition of similarity, its broad generality makes it applicable to a wide range of problems in different contexts, and in particular makes it naturally suitable to clustering structured objects for which feature vectors can be difficult to obtain.Despite its simplicity, generality and wide applicability, correlation clustering has so far received much more attention from the algorithmic theory community than from the data mining community. The goal of this tutorial is to show how correlation clustering can be a powerful addition to the toolkit of the data mining researcher and practitioner, and to encourage discussions and further research in the area.In the tutorial we will survey the problem and its most common variants, with an emphasis on the algorithmic techniques and key ideas developed to derive efficient solutions. We will motivate the problems and discuss real-world applications, the scalability issues that may arise, and the existing approaches to handle them.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1972},
numpages = {1},
keywords = {clustering},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630809,
author = {Salakhutdinov, Ruslan},
title = {Deep Learning},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630809},
doi = {10.1145/2623330.2630809},
abstract = {Building intelligent systems that are capable of extracting high-level representations from high-dimensional data lies at the core of solving many AI related tasks, including visual object or pattern recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires deep architectures that involve many layers of nonlinear processing. Many existing learning algorithms use shallow architectures, including neural networks with only one hidden layer, support vector machines, kernel logistic regression, and many others. The internal representations learned by such systems are necessarily simple and are incapable of extracting some types of complex structure from high-dimensional input. In the past few years, researchers across many different communities, from applied statistics to engineering, computer science, and neuroscience, have proposed several deep (hierarchical) models that are capable of extracting meaningful, high-level representations. An important property of these models is that they can extract complex statistical dependencies from data and efficiently learn high-level representations by re-using and combining intermediate concepts, allowing these models to generalize well across a wide variety of tasks. The learned high-level representations have been shown to give state-of-the-art results in many challenging learning problems and have been successfully applied in a wide variety of application domains, including visual object recognition, information retrieval, natural language processing, and speech perception. A few notable examples of such models include Deep Belief Networks, Deep Boltzmann Machines, Deep Autoencoders, and sparse coding-based methods. The goal of the tutorial is to introduce the recent developments of various deep learning methods to the KDD community. The core focus will be placed on algorithms that can learn multi-layer hierarchies of representations, emphasizing their applications in information retrieval, object recognition, and speech perception.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1973},
numpages = {1},
keywords = {boltzmann machines, autoencoders, sparse coding, graphical models, deep learning},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630810,
author = {Zhu, Feida and Sun, Huan and Yan, Xifeng},
title = {Network Mining and Analysis for Social Applications},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630810},
doi = {10.1145/2623330.2630810},
abstract = {The recent blossom of social network and communication services in both public and corporate settings have generated a staggering amount of network data of all kinds. Unlike the bio-networks and the chemical compound graph data often used in traditional network mining and analysis, the new network data grown out of the social applications are characterized by their rich attributes, high heterogeneity, enormous sizes and complex patterns of various semantic meanings, all of which have posed significant research challenges to the graph/network mining community. In this tutorial, we aim to examine some recent advances in network mining and analysis for social applications, covering a diverse collection of methodologies and applications from the perspectives of event, relationship, collaboration, and network pattern. We would present the problem settings, the challenges, the recent research advances and some future directions for each perspective. Topics include but are not limited to correlation mining, iceberg finding, anomaly detection, relationship discovery, information flow, task routing, and pattern mining.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1974},
numpages = {1},
keywords = {network analysis, social applications, network mining},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630811,
author = {Cormode, Graham and Duffield, Nick},
title = {Sampling for Big Data: A Tutorial},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630811},
doi = {10.1145/2623330.2630811},
abstract = {One response to the proliferation of large datasets has been to develop ingenious ways to throw resources at the problem, using massive fault tolerant storage architectures, parallel and graphical computation models such as MapReduce, Pregel and Giraph. However, not all environments can support this scale of resources, and not all queries need an exact response. This motivates the use of sampling to generate summary datasets that support rapid queries, and prolong the useful life of the data in storage. To be effective, sampling must mediate the tensions between resource constraints, data characteristics, and the required query accuracy. The state-of-the-art in sampling goes far beyond simple uniform selection of elements, to maximize the usefulness of the resulting sample. This tutorial reviews progress in sample design for large datasets, including streaming and graph-structured data. Applications are discussed to sampling network traffic and social networks.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1975},
numpages = {1},
keywords = {random sampling},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630812,
author = {H\"{a}m\"{a}l\"{a}inen, Wilhelmiina and Webb, Geoffrey I.},
title = {Statistically Sound Pattern Discovery},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630812},
doi = {10.1145/2623330.2630812},
abstract = {Pattern discovery is a core data mining activity. Initial approaches were dominated by the frequent pattern discovery paradigm -- only patterns that occur frequently in the data were explored. Having been thoroughly researched and its limitations now well understood, this paradigm is giving way to a new one, which can be called statistically sound pattern discovery. In this paradigm, the main impetus is to discover statistically significant patterns, which are unlikely to have occurred by chance and are likely to hold in future data. Thus, the new paradigm provides a strict control over false discoveries and overfitting.This tutorial covers both classic and cutting-edge research topics on pattern discovery combined to statistical significance testing. We start with an advanced introduction to the relevant forms of statistical significance testing, including different schools and alternative models, their underlying assumptions, practical issues, and limitations. We then discuss their application to data mining specific problems, including evaluation of nested patterns, the multiple testing problem, algorithmic strategies and real-world considerations. We present the current state-of-the art solutions and explore in detail how this approach to pattern discovery can deliver efficient and effective discovery of small sets of interesting patterns.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1976},
numpages = {1},
keywords = {association mining, statistics, pattern discovery},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630813,
author = {Tang, Jiliang and Tang, Jie and Liu, Huan},
title = {Recommendation in Social Media: Recent Advances and New Frontiers},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630813},
doi = {10.1145/2623330.2630813},
abstract = {The pervasive use of social media generates massive data in an unprecedented rate and the information overload problem becomes increasingly severe for social media users. Recommendation has been proven to be effective in mitigating the information overload problem, demonstrated its strength in improving the quality of user experience, and positively impacted the success of social media. New types of data introduced by social media not only provide more information to advance traditional recommender systems but also manifest new research possibilities for recommendation. In this tutorial, we aim to provide a comprehensive overview of various recommendation tasks in social media, especially their recent advances and new frontiers. We introduce basic concepts, review state-of-the-art algorithms, and deliberate the emerging challenges and opportunities. Finally we summarize the tutorial with discussions on open issues and challenges about recommendation in social media. Updated information about the tutorial can be found at url{http://www.public.asu.edu/~jtang20/Recommendation.htm}.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1977},
numpages = {1},
keywords = {friend recommendation, spatio-temporal-social, cross-domain recommendation, location-aware recommendation, location recommendation, content recommendation, social-aware recommendation, social recommendation},
location = {New York, New York, USA},
series = {KDD '14}
}

license_type: permissive

branch_name: refs/heads/main

extension: bib


text:
% Encoding: UTF-8

@inproceedings{agarwal2020optimistic,
  title={An Optimistic Perspective on Offline Reinforcement Learning},
  author={Agarwal, Rishabh and Schuurmans, Dale and Norouzi, Mohammad},
  journal={International Conference on Machine Learning},
  year={2020}
}

@article{DBLP:journals/corr/abs-2004-07219,
  author    = {Justin Fu and
               Aviral Kumar and
               Ofir Nachum and
               George Tucker and
               Sergey Levine},
  title     = {{D4RL:} Datasets for Deep Data-Driven Reinforcement Learning},
  journal   = {CoRR},
  volume    = {abs/2004.07219},
  year      = {2020},
  url       = {https://arxiv.org/abs/2004.07219},
  eprinttype = {arXiv},
  eprint    = {2004.07219},
  timestamp = {Tue, 21 Apr 2020 16:51:52 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2004-07219.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2006-13888,
  author    = {{\c{C}}aglar G{\"{u}}l{\c{c}}ehre and
               Ziyu Wang and
               Alexander Novikov and
               Tom Le Paine and
               Sergio G{\'{o}}mez Colmenarejo and
               Konrad Zolna and
               Rishabh Agarwal and
               Josh Merel and
               Daniel J. Mankowitz and
               Cosmin Paduraru and
               Gabriel Dulac{-}Arnold and
               Jerry Li and
               Mohammad Norouzi and
               Matt Hoffman and
               Ofir Nachum and
               George Tucker and
               Nicolas Heess and
               Nando de Freitas},
  title     = {{RL} Unplugged: Benchmarks for Offline Reinforcement Learning},
  journal   = {CoRR},
  volume    = {abs/2006.13888},
  year      = {2020},
  url       = {https://arxiv.org/abs/2006.13888},
  eprinttype = {arXiv},
  eprint    = {2006.13888},
  timestamp = {Wed, 20 Jan 2021 16:29:33 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2006-13888.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2103-16596,
  author    = {Justin Fu and
               Mohammad Norouzi and
               Ofir Nachum and
               George Tucker and
               Ziyu Wang and
               Alexander Novikov and
               Mengjiao Yang and
               Michael R. Zhang and
               Yutian Chen and
               Aviral Kumar and
               Cosmin Paduraru and
               Sergey Levine and
               Tom Le Paine},
  title     = {Benchmarks for Deep Off-Policy Evaluation},
  journal   = {CoRR},
  volume    = {abs/2103.16596},
  year      = {2021},
  url       = {https://arxiv.org/abs/2103.16596},
  eprinttype = {arXiv},
  eprint    = {2103.16596},
  timestamp = {Wed, 07 Apr 2021 15:31:46 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2103-16596.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1911-06854,
  author    = {Cameron Voloshin and
               Hoang Minh Le and
               Nan Jiang and
               Yisong Yue},
  title     = {Empirical Study of Off-Policy Policy Evaluation for Reinforcement
               Learning},
  journal   = {CoRR},
  volume    = {abs/1911.06854},
  year      = {2019},
  url       = {http://arxiv.org/abs/1911.06854},
  eprinttype = {arXiv},
  eprint    = {1911.06854},
  timestamp = {Mon, 06 Jan 2020 18:26:48 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1911-06854.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2007-09055,
  author    = {Tom Le Paine and
               Cosmin Paduraru and
               Andrea Michi and
               {\c{C}}aglar G{\"{u}}l{\c{c}}ehre and
               Konrad Zolna and
               Alexander Novikov and
               Ziyu Wang and
               Nando de Freitas},
  title     = {Hyperparameter Selection for Offline Reinforcement Learning},
  journal   = {CoRR},
  volume    = {abs/2007.09055},
  year      = {2020},
  url       = {https://arxiv.org/abs/2007.09055},
  eprinttype = {arXiv},
  eprint    = {2007.09055},
  timestamp = {Wed, 20 Jan 2021 16:29:33 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2007-09055.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1903-08738,
  author    = {Hoang Minh Le and
               Cameron Voloshin and
               Yisong Yue},
  title     = {Batch Policy Learning under Constraints},
  journal   = {CoRR},
  volume    = {abs/1903.08738},
  year      = {2019},
  url       = {http://arxiv.org/abs/1903.08738},
  eprinttype = {arXiv},
  eprint    = {1903.08738},
  timestamp = {Thu, 06 Jun 2019 18:03:43 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1903-08738.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2007-13609,
  author    = {Ilya Kostrikov and
               Ofir Nachum},
  title     = {Statistical Bootstrapping for Uncertainty Estimation in Off-Policy
               Evaluation},
  journal   = {CoRR},
  volume    = {abs/2007.13609},
  year      = {2020},
  url       = {https://arxiv.org/abs/2007.13609},
  eprinttype = {arXiv},
  eprint    = {2007.13609},
  timestamp = {Wed, 29 Jul 2020 15:36:39 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2007-13609.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

license: MIT

gha_license_id: NOASSERTION

gha_language: Julia


text:
#
# Copyright (C) 2018-2023 QuasarApp.
# Distributed under the MIT software license, see the accompanying
# Everyone is permitted to copy and distribute verbatim copies
# of this license document, but changing it is not allowed.
#

if(DEFINED CROSSPLATFORM_BUILD_TOOLCHAIN)
  return()
else()
  set(CROSSPLATFORM_BUILD_TOOLCHAIN 1)
endif()

# use TARGET_PLATFORM_TOOLCHAIN

# This module include crossplatform toolchains by target platform.
# To select target platform set the TARGET_PLATFORM_TOOLCHAIN define.
# Availabel platforms:
# * wasm32
# * win32-g++
# * win64-g++
#
# Note
# For add full support of the wasm32 build you shold use the initWasmSupport method. This method prepare static build for wasm executable and qt resources.
#
# initWasmSupport arguments:
#  * name - This is name of your initialize targets. You can set it as a CMAKE_PROJECT
#  * deployFile - This is path to the deploy file of the cqtdepoyer tool. Use it for deploy your site.

if (NOT DEFINED TARGET_PLATFORM_TOOLCHAIN)
    message(STATUS "The crossplatform build is disabled")
    return()

endif()

set(CROSSPLATFORM_BUILD_TOOLCHAIN_PATH ${CMAKE_CURRENT_LIST_DIR}/${CMAKE_HOST_SYSTEM_NAME}/${TARGET_PLATFORM_TOOLCHAIN}.cmake)

message("CROSSPLATFORM_BUILD_TOOLCHAIN_PATH = ${CROSSPLATFORM_BUILD_TOOLCHAIN_PATH}")
if(EXISTS ${CROSSPLATFORM_BUILD_TOOLCHAIN_PATH})

    message(STATUS "${TARGET_PLATFORM_TOOLCHAIN} exits in ${CROSSPLATFORM_BUILD_TOOLCHAIN_PATH}")

    include("${CROSSPLATFORM_BUILD_TOOLCHAIN_PATH}")

else(EXISTS ${CROSSPLATFORM_BUILD_TOOLCHAIN_PATH})
    message(STATUS "${TARGET_PLATFORM_TOOLCHAIN} not exits in ${CROSSPLATFORM_BUILD_TOOLCHAIN_PATH}")
endif()

if (DEFINED TARGET_PLATFORM_TOOLCHAIN)
    if (${TARGET_PLATFORM_TOOLCHAIN} STREQUAL "wasm32")
        set(QA_WASM32 ON CACHE BOOL "Custom define that enabled only when wasm32 platform are used" FORCE)
    endif(${TARGET_PLATFORM_TOOLCHAIN} STREQUAL "wasm32")
endif(DEFINED TARGET_PLATFORM_TOOLCHAIN)

function(initWasmSupport name deployFile)
    if(NOT TARGET ${name})
        message("the ${name} target is not created!")
        return()

    endif(NOT TARGET ${name})

    message("init support wasm build for target ${name}")

    if (DEFINED TARGET_PLATFORM_TOOLCHAIN)
        if (${TARGET_PLATFORM_TOOLCHAIN} STREQUAL "wasm32")
            message(added deploy step for site)
            set_target_properties(${name} PROPERTIES OUTPUT_NAME "${name}.js")

            addDeployFromFile(${deployFile})

            include(${CMAKE_CURRENT_FUNCTION_LIST_DIR}/../QtStaticCMake/QtStaticCMake.cmake)

            qt_generate_plugin_import(${name} VERBOSE)

            # EXTRA_PLUGIN are the one required by plugin loaded by qt_generate_plugin_import
            # It's not automatic yet :( All this workflow might change in future version of qt
            # with better and better cmake support
            qt_generate_qml_plugin_import(${name}
              QML_SRC ${CMAKE_CURRENT_SOURCE_DIR}
              EXTRA_PLUGIN
                QtQuickVirtualKeyboardPlugin
                QtQuickVirtualKeyboardSettingsPlugin
                QtQuickVirtualKeyboardStylesPlugin
                QmlFolderListModelPlugin
                QQuickLayoutsPlugin
              VERBOSE
            )

        endif(${TARGET_PLATFORM_TOOLCHAIN} STREQUAL "wasm32")
    endif(DEFINED TARGET_PLATFORM_TOOLCHAIN)

endfunction()

repository: QuasarApp/CMake

repo_name: QuasarApp/CMake

gha_license_id: MIT

gha_language: CMake

extension: cmake


text:
@InProceedings{berenguel:cvprw:2023,
  author    = {Berenguel-Baeta, Bruno and Andr{\'e}, Antoine and Caron, Guillaume and
Bermudez-Cameo, Jesus and Guerrero, Josechu J.},
  title     = {Visual Gyroscope: Combination of Deep Learning Features and Direct Alignment for Panoramic Stabilization},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2023},
  pages     = {6444-6447},
  address   = {Vancouver, BC, Canada},
  month     = {June 19},
  publisher = {IEEE},
  url       = {https://hal.science/hal-04066957},
  doi       = {}
}

branch_name: refs/heads/master

gha_language: TeX


text:
@article{Hou_2012,
 author = {Hou, D. and Zhang, L. and Deng, F. and Fang, W. and Wang, R. and Liu, X. and Guo, L. and Rayner, S. and Chen, X. and Wang, H. and Hu, Z.},
 title = {Comparative Proteomics Reveal Fundamental Structural and Functional Differences between the Two Progeny Phenotypes of a Baculovirus},
 journal = {Journal of Virology},
 publisher = {American Society for Microbiology},
 year = {2012},
 month = {oct},
 number = {2},
 volume = {87},
 pages = {829--839},
 url = {https://doi.org/10.1128%2Fjvi.02329-12},
 doi = {10.1128/jvi.02329-12}
}