text:
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    year = {2011},
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@book{Taylor:2009:Text-To-Speech_Synthesis,
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@book{Proakis:2006:Digital_Signal,
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@book{Wackernagel:2003:Multivariate_Geostatistics,
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@book{Bishop:2006:Pattern_Recognition_and_Machine_Learning,
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@book{Murphy:2012:Probabilistic_Perspective,
    author = {Kevin P. Murphy},
    publisher = {MIT Press},
    title = {Machine Learning: A Probabilistic Perspective},
    year = {2012},
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@book{Rasmussen:2006:Gaussian_Processes,
    author = {Carl Edward Rasmussen and Christopher K. I. Williams},
    publisher = {MIT Press},
    title = {Gaussian Processes for Machine Learning},
    year = {2006},
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@book{Nocedal:2006:Numerical_Optimisation,
    author = {J. Nocedal and S. J. Wright},
    edition = {2},
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    title = {Numerical Optimization},
    year = {2006},
}

@book{Trefethen:1997:Numerical_LA,
    author = {Lloyd N. Trefethen and David Bau},
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    title = {Numerical Linear Algebra},
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@book{Jaynes:2003:Probability_Theory_Logic_of_Science,
    author = {E. T. Jaynes},
    publisher = {Cambridge University Press},
    title = {Probability Theory: Logic of Science},
    year = {2003},
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@book{Pavliotis:2012:Probabilistic_Perspective,
    author = {Grigorios A. Pavliotis},
    publisher = {Springer Science and Business Media},
    title = {Stochastic Processes and Applications: Diffusion Processes, the Fokker-Planck and Langevin Equations},
    year = {2014},
}

@inproceedings{Chaudhuri:2017:Active_Heteroscedastic_Regression,
    author = {Kamalika Chaudhuri and Prateek Jain and Nagarajan Natarajan},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {694--702},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Active Heteroscedastic Regression},
    volume = {70},
    year = {2017},
}

@inproceedings{Jiang:2017:Efficient_Nonmyopic_Active_Search,
    author = {Shali Jiang and Gustavo Malkomes and Geoff Converse and Alyssa Shofner and Benjamin Moseley and Roman Garnett},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1714--1723},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Efficient Nonmyopic Active Search},
    volume = {70},
    year = {2017},
}

@inproceedings{Krishnamurthy:2017:Active_Learning_for_Cost-Sensitive_Classification,
    author = {Akshay Krishnamurthy and Alekh Agarwal and Tzu-Kuo Huang and Hal III Daume and John Langford},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1915--1924},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Active Learning for Cost-Sensitive Classification},
    volume = {70},
    year = {2017},
}

@inproceedings{Lipor:2017:Leveraging_Union_of_Subspace_Structure,
    author = {John Lipor and Laura Balzano},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2130--2139},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Leveraging Union of Subspace Structure to Improve Constrained Clustering},
    volume = {70},
    year = {2017},
}

@inproceedings{Tosh:2017:Diameter-Based_Active_Learning,
    author = {Christopher Tosh and Sanjoy Dasgupta},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3444--3452},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Diameter-Based Active Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Donahue:2017:Dance_Dance_Convolution,
    author = {Chris Donahue and Zachary C. Lipton and Julian McAuley},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1039--1048},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Dance Dance Convolution},
    volume = {70},
    year = {2017},
}

@inproceedings{Gilmer:2017:Neural_Message_Passing_for_Quantum,
    author = {Justin Gilmer and Samuel S. Schoenholz and Patrick F. Riley and Oriol Vinyals and George E. Dahl},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1263--1272},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Neural Message Passing for Quantum Chemistry},
    volume = {70},
    year = {2017},
}

@inproceedings{Rippel:2017:Real-Time_Adaptive_Image_Compression,
    author = {Oren Rippel and Lubomir Bourdev},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2922--2930},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Real-Time Adaptive Image Compression},
    volume = {70},
    year = {2017},
}

@inproceedings{Shi:2017:World_of_Bits_An_Open-Domain,
    author = {Tianlin Shi and Andrej Karpathy and Linxi Fan and Jonathan Hernandez and Percy Liang},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3135--3144},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {World of Bits: An Open-Domain Platform for Web-Based Agents},
    volume = {70},
    year = {2017},
}

@inproceedings{Tompson:2017:Accelerating_Eulerian_Fluid_Simulation_With,
    author = {Jonathan Tompson and Kristofer Schlachter and Pablo Sprechmann and Ken Perlin},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3424--3433},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Accelerating {E}ulerian Fluid Simulation With Convolutional Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Dawson:2017:An_Infinite_Hidden_Markov_Model,
    author = {Colin Reimer Dawson and Chaofan Huang and Clayton T. Morrison},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {942--950},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {An Infinite Hidden {M}arkov Model With Similarity-Biased Transitions},
    volume = {70},
    year = {2017},
}

@inproceedings{Ji:2017:From_Patches_to_Images_A,
    author = {Geng Ji and Michael C. Hughes and Erik B. Sudderth},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1675--1683},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {From Patches to Images: A Nonparametric Generative Model},
    volume = {70},
    year = {2017},
}

@inproceedings{Palla:2017:A_Birth-Death_Process_for_Feature,
    author = {Konstantina Palla and David Knowles and Zoubin Ghahramani},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2751--2759},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {A Birth-Death Process for Feature Allocation},
    volume = {70},
    year = {2017},
}

@inproceedings{Walder:2017:Fast_Bayesian_Intensity_Estimation_for,
    author = {Christian J. Walder and Adrian N. Bishop},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3579--3588},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Fast {B}ayesian Intensity Estimation for the Permanental Process},
    volume = {70},
    year = {2017},
}

@inproceedings{Wang:2017:Batched_High-Dimensional_Bayesian_Optimization_via,
    author = {Zi Wang and Chengtao Li and Stefanie Jegelka and Pushmeet Kohli},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3656--3664},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Batched High-Dimensional {B}ayesian Optimization via Structural Kernel Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Gonzalez:2017:Preferential_Bayesian_Optimization,
    author = {Javier Gonzalez and Zhenwen Dai and Andreas Damianou and Neil D. Lawrence},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1282--1291},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Preferential {B}ayesian Optimization},
    volume = {70},
    year = {2017},
}

@inproceedings{Hernandez-Lobato:2017:Parallel_and_Distributed_Thompson_Sampling,
    author = {Jose Miguel Hernandez-Lobato and James Requeima and Edward O. Pyzer-Knapp and Alan Aspuru-Guzik},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1470--1479},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Parallel and Distributed Thompson Sampling for Large-Scale Accelerated Exploration of Chemical Space},
    volume = {70},
    year = {2017},
}

@inproceedings{Jenatton:2017:Bayesian_Optimization_With_Tree-Structured_Dependencies,
    author = {Rodolphe Jenatton and Cedric Archambeau and Javier Gonzalez and Matthias Seeger},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1655--1664},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {{B}ayesian Optimization With Tree-Structured Dependencies},
    volume = {70},
    year = {2017},
}

@inproceedings{Kandasamy:2017:Multi-Fidelity_Bayesian_Optimisation_With_Continuous,
    author = {Kirthevasan Kandasamy and Gautam Dasarathy and Jeff Schneider and Barnabas Poczos},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1799--1808},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Multi-Fidelity {B}ayesian Optimisation With Continuous Approximations},
    volume = {70},
    year = {2017},
}

@inproceedings{Wang:2017:Max-Value_Entropy_Search_for_Efficient,
    author = {Zi Wang and Stefanie Jegelka},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3627--3635},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Max-Value Entropy Search for Efficient {B}ayesian Optimization},
    volume = {70},
    year = {2017},
}

@inproceedings{Achab:2017:Uncovering_Causality_From_Multivariate_Hawkes,
    author = {Massil Achab and Emmanuel Bacry and Stephane Ga\"{\i}ffas and Iacopo Mastromatteo and Jean-Fran{\c{c}}ois Muzy},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1--10},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Uncovering Causality From Multivariate {H}awkes Integrated Cumulants},
    volume = {70},
    year = {2017},
}

@inproceedings{Chaudhry:2017:Uncertainty_Assessment_and_False_Discovery,
    author = {Aditya Chaudhry and Pan Xu and Quanquan Gu},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {684--693},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Uncertainty Assessment and False Discovery Rate Control in High-Dimensional {G}ranger Causal Inference},
    volume = {70},
    year = {2017},
}

@inproceedings{Chen:2017:Identification_and_Model_Testing_in,
    author = {Bryant Chen and Daniel Kumor and Elias Bareinboim},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {757--766},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Identification and Model Testing in Linear Structural Equation Models Using Auxiliary Variables},
    volume = {70},
    year = {2017},
}

@inproceedings{Kocaoglu:2017:Cost-Optimal_Learning_of_Causal_Graphs,
    author = {Murat Kocaoglu and Alex Dimakis and Sriram Vishwanath},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1875--1884},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Cost-Optimal Learning of Causal Graphs},
    volume = {70},
    year = {2017},
}

@inproceedings{Shalit:2017:Estimating_Individual_Treatment_Effect_Generalization,
    author = {Uri Shalit and Fredrik D. Johansson and David Sontag},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3076--3085},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Estimating Individual Treatment Effect: Generalization Bounds and Algorithms},
    volume = {70},
    year = {2017},
}

@inproceedings{Forney:2017:Counterfactual_Data-Fusion_for_Online_Reinforcement,
    author = {Andrew Forney and Judea Pearl and Elias Bareinboim},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1156--1164},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Counterfactual Data-Fusion for Online Reinforcement Learners},
    volume = {70},
    year = {2017},
}

@inproceedings{Hartford:2017:Deep_IV_A_Flexible_Approach,
    author = {Jason Hartford and Greg Lewis and Kevin Leyton-Brown and Matt Taddy},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1414--1423},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Deep {IV}: A Flexible Approach for Counterfactual Prediction},
    volume = {70},
    year = {2017},
}

@inproceedings{Kallus:2017:Recursive_Partitioning_for_Personalization_Using,
    author = {Nathan Kallus},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1789--1798},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Recursive Partitioning for Personalization Using Observational Data},
    volume = {70},
    year = {2017},
}

@inproceedings{Sen:2017:Identifying_Best_Interventions_Through_Online,
    author = {Rajat Sen and Karthikeyan Shanmugam and Alexandros G. Dimakis and Sanjay Shakkottai},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3057--3066},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Identifying Best Interventions Through Online Importance Sampling},
    volume = {70},
    year = {2017},
}

@inproceedings{Bachem:2017:Distributed_and_Provably_Good_Seedings,
    author = {Olivier Bachem and Mario Lucic and Andreas Krause},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {292--300},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Distributed and Provably Good Seedings for K-Means in Constant Rounds},
    volume = {70},
    year = {2017},
}

@inproceedings{Ho:2017:Multilevel_Clustering_via_Wasserstein_Means,
    author = {Nhat Ho and XuanLong Nguyen and Mikhail Yurochkin and Hung Hai Bui and Viet Huynh and Dinh Phung},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1501--1509},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Multilevel Clustering via {W}asserstein Means},
    volume = {70},
    year = {2017},
}

@inproceedings{Lattanzi:2017:Consistent_K-Clustering,
    author = {Silvio Lattanzi and Sergei Vassilvitskii},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1975--1984},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Consistent K-Clustering},
    volume = {70},
    year = {2017},
}

@inproceedings{Tsakiris:2017:Hyperplane_Clustering_via_Dual_Principal,
    author = {Manolis C. Tsakiris and Rene Vidal},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3472--3481},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Hyperplane Clustering via Dual Principal Component Pursuit},
    volume = {70},
    year = {2017},
}

@inproceedings{Yang:2017:Towards_K-Means-Friendly_Spaces_Simultaneous_Deep,
    author = {Bo Yang and Xiao Fu and Nicholas D. Sidiropoulos and Mingyi Hong},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3861--3870},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Towards K-Means-Friendly Spaces: Simultaneous Deep Learning and Clustering},
    volume = {70},
    year = {2017},
}

@inproceedings{Braverman:2017:Clustering_High_Dimensional_Dynamic_Data,
    author = {Vladimir Braverman and Gereon Frahling and Harry Lang and Christian Sohler and Lin F. Yang},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {576--585},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Clustering High Dimensional Dynamic Data Streams},
    volume = {70},
    year = {2017},
}

@inproceedings{Chang:2017:Multiple_Clustering_Views_From_Multiple,
    author = {Yale Chang and Junxiang Chen and Michael H. Cho and Peter J. Castaldi and Edwin K. Silverman and Jennifer G. Dy},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {674--683},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Multiple Clustering Views From Multiple Uncertain Experts},
    volume = {70},
    year = {2017},
}

@inproceedings{Laclau:2017:Co-Clustering_Through_Optimal_Transport,
    author = {Charlotte Laclau and Ievgen Redko and Basarab Matei and Younes Bennani and Vincent Brault},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1955--1964},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Co-Clustering Through Optimal Transport},
    volume = {70},
    year = {2017},
}

@inproceedings{Panahi:2017:Clustering_by_Sum_of_Norms,
    author = {Ashkan Panahi and Devdatt Dubhashi and Fredrik D. Johansson and Chiranjib Bhattacharyya},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2769--2777},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery},
    volume = {70},
    year = {2017},
}

@inproceedings{Bian:2017:Guarantees_for_Greedy_Maximization_of,
    author = {Andrew An Bian and Joachim M. Buhmann and Andreas Krause and Sebastian Tschiatschek},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {498--507},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Guarantees for Greedy Maximization of Non-Submodular Functions With Applications},
    volume = {70},
    year = {2017},
}

@inproceedings{Bogunovic:2017:Robust_Submodular_Maximization_A_Non-Uniform,
    author = {Ilija Bogunovic and Slobodan Mitrovic and Jonathan Scarlett and Volkan Cevher},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {508--516},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Robust Submodular Maximization: A Non-Uniform Partitioning Approach},
    volume = {70},
    year = {2017},
}

@inproceedings{Hassidim:2017:Robust_Guarantees_of_Stochastic_Greedy,
    author = {Avinatan Hassidim and Yaron Singer},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1424--1432},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Robust Guarantees of Stochastic Greedy Algorithms},
    volume = {70},
    year = {2017},
}

@inproceedings{Khanna:2017:On_Approximation_Guarantees_for_Greedy,
    author = {Rajiv Khanna and Ethan R. Elenberg and Alexandros G. Dimakis and Joydeep Ghosh and Sahand Negahban},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1837--1846},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {On Approximation Guarantees for Greedy Low Rank Optimization},
    volume = {70},
    year = {2017},
}

@inproceedings{Stan:2017:Probabilistic_Submodular_Maximization_in_Sub-Linear,
    author = {Serban Stan and Morteza Zadimoghaddam and Andreas Krause and Amin Karbasi},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3241--3250},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Probabilistic Submodular Maximization in Sub-Linear Time},
    volume = {70},
    year = {2017},
}

@inproceedings{Allen-Zhu:2017:Near-Optimal_Design_of_Experiments_via,
    author = {Zeyuan Allen-Zhu and Yuanzhi Li and Aarti Singh and Yining Wang},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {126--135},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Near-Optimal Design of Experiments via Regret Minimization},
    volume = {70},
    year = {2017},
}

@inproceedings{Eckstein:2017:Rule-Enhanced_Penalized_Regression_by_Column,
    author = {Jonathan Eckstein and Noam Goldberg and Ai Kagawa},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1059--1067},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Rule-Enhanced Penalized Regression by Column Generation Using Rectangular Maximum Agreement},
    volume = {70},
    year = {2017},
}

@inproceedings{Horvnakova:2017:Analysis_and_Optimization_of_Graph,
    author = {Andrea Hor{\v{n}}akova and Jan-Hendrik Lange and Bjoern Andres},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1539--1548},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Analysis and Optimization of Graph Decompositions by Lifted Multicuts},
    volume = {70},
    year = {2017},
}

@inproceedings{Mirzasoleiman:2017:Deletion-Robust_Submodular_Maximization_Data_Summarization,
    author = {Baharan Mirzasoleiman and Amin Karbasi and Andreas Krause},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2449--2458},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Deletion-Robust Submodular Maximization: Data Summarization With ``The Right to Be Forgotten''},
    volume = {70},
    year = {2017},
}

@inproceedings{Staib:2017:Robust_Budget_Allocation_via_Continuous,
    author = {Matthew Staib and Stefanie Jegelka},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3230--3240},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Robust Budget Allocation via Continuous Submodular Functions},
    volume = {70},
    year = {2017},
}

@inproceedings{Akrour:2017:Local_Bayesian_Optimization_of_Motor,
    author = {Riad Akrour and Dmitry Sorokin and Jan Peters and Gerhard Neumann},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {41--50},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Local {B}ayesian Optimization of Motor Skills},
    volume = {70},
    year = {2017},
}

@inproceedings{Chebotar:2017:Combining_Model-Based_and_Model-Free_Updates,
    author = {Yevgen Chebotar and Karol Hausman and Marvin Zhang and Gaurav Sukhatme and Stefan Schaal and Sergey Levine},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {703--711},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Chou:2017:Improving_Stochastic_Policy_Gradients_in,
    author = {Po-Wei Chou and Daniel Maturana and Sebastian Scherer},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {834--843},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Improving Stochastic Policy Gradients in Continuous Control With Deep Reinforcement Learning Using the Beta Distribution},
    volume = {70},
    year = {2017},
}

@inproceedings{Pan:2017:Prediction_Under_Uncertainty_in_Sparse,
    author = {Yunpeng Pan and Xinyan Yan and Evangelos A. Theodorou and Byron Boots},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2760--2768},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Prediction Under Uncertainty in Sparse Spectrum {G}aussian Processes With Applications to Filtering and Control},
    volume = {70},
    year = {2017},
}

@inproceedings{Umlauft:2017:Learning_Stable_Stochastic_Nonlinear_Dynamical,
    author = {Jonas Umlauft and Sandra Hirche},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3502--3510},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Learning Stable Stochastic Nonlinear Dynamical Systems},
    volume = {70},
    year = {2017},
}

@inproceedings{Arjevani:2017:Oracle_Complexity_of_Second-Order_Methods,
    author = {Yossi Arjevani and Ohad Shamir},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {205--213},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Oracle Complexity of Second-Order Methods for Finite-Sum Problems},
    volume = {70},
    year = {2017},
}

@inproceedings{Chen:2017:Strong_NP-Hardness_for_Sparse_Optimization,
    author = {Yichen Chen and Dongdong Ge and Mengdi Wang and Zizhuo Wang and Yinyu Ye and Hao Yin},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {740--747},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Strong {NP}-Hardness for Sparse Optimization With Concave Penalty Functions},
    volume = {70},
    year = {2017},
}

@inproceedings{Malherbe:2017:Global_Optimization_of_Lipschitz_Functions,
    author = {Cedric Malherbe and Nicolas Vayatis},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2314--2323},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Global Optimization of {L}ipschitz Functions},
    volume = {70},
    year = {2017},
}

@inproceedings{Mirrokni:2017:Tight_Bounds_for_Approximate_Caratheodory,
    author = {Vahab Mirrokni and Renato Paes Leme and Adrian Vladu and Sam Chiu-wai Wong},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2440--2448},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Tight Bounds for Approximate {C}aratheodory and Beyond},
    volume = {70},
    year = {2017},
}

@inproceedings{Xu:2017:Stochastic_Convex_Optimization_Faster_Local,
    author = {Yi Xu and Qihang Lin and Tianbao Yang},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3821--3830},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence},
    volume = {70},
    year = {2017},
}

@inproceedings{Aksoylar:2017:Connected_Subgraph_Detection_With_Mirror,
    author = {Cem Aksoylar and Lorenzo Orecchia and Venkatesh Saligrama},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {51--59},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Connected Subgraph Detection With Mirror Descent on {SDP}s},
    volume = {70},
    year = {2017},
}

@inproceedings{Shen:2017:GSOS_Gauss-Seidel_Operator_Splitting_Algorithm,
    author = {Li Shen and Wei Liu and Ganzhao Yuan and Shiqian Ma},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3125--3134},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {{GSOS}: {G}auss-{S}eidel Operator Splitting Algorithm for Multi-Term Nonsmooth Convex Composite Optimization},
    volume = {70},
    year = {2017},
}

@inproceedings{Tang:2017:Gradient_Projection_Iterative_Sketch_for,
    author = {Junqi Tang and Mohammad Golbabaee and Mike E. Davies},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3377--3386},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares},
    volume = {70},
    year = {2017},
}

@inproceedings{Thi:2017:Stochastic_DCA_for_the_Large-Sum,
    author = {Hoai An Le Thi and Hoai Minh Le and Duy Nhat Phan and Bach Tran},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3394--3403},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Stochastic {DCA} for the Large-Sum of Non-Convex Functions Problem and Its Application to Group Variable Selection in Classification},
    volume = {70},
    year = {2017},
}

@inproceedings{Tu:2017:Breaking_Locality_Accelerates_Block_Gauss-Seidel,
    author = {Stephen Tu and Shivaram Venkataraman and Ashia C. Wilson and Alex Gittens and Michael I. Jordan and Benjamin Recht},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3482--3491},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Breaking Locality Accelerates Block {G}auss-{S}eidel},
    volume = {70},
    year = {2017},
}

@inproceedings{Goldstein:2017:Convex_Phase_Retrieval_Without_Lifting,
    author = {Tom Goldstein and Christoph Studer},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1273--1281},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Convex Phase Retrieval Without Lifting via {P}hase{M}ax},
    volume = {70},
    year = {2017},
}

@inproceedings{Hong:2017:Prox-PDA_The_Proximal_Primal-Dual_Algorithm,
    author = {Mingyi Hong and Davood Hajinezhad and Ming-Min Zhao},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1529--1538},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Prox-{PDA}: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Lei:2017:Doubly_Greedy_Primal-Dual_Coordinate_Descent,
    author = {Qi Lei and Ian En-Hsu Yen and Chao-yuan Wu and Inderjit S. Dhillon and Pradeep Ravikumar},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2034--2042},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization},
    volume = {70},
    year = {2017},
}

@inproceedings{Wang:2017:Exploiting_Strong_Convexity_From_Data,
    author = {Jialei Wang and Lin Xiao},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3694--3702},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Exploiting Strong Convexity From Data With Primal-Dual First-Order Algorithms},
    volume = {70},
    year = {2017},
}

@inproceedings{Yang:2017:A_Richer_Theory_of_Convex,
    author = {Tianbao Yang and Qihang Lin and Lijun Zhang},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3901--3910},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {A Richer Theory of Convex Constrained Optimization With Reduced Projections and Improved Rates},
    volume = {70},
    year = {2017},
}

@inproceedings{Ali:2017:A_Semismooth_Newton_Method_for,
    author = {Alnur Ali and Eric Wong and J. Zico Kolter},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {70--79},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {A Semismooth {N}ewton Method for Fast, Generic Convex Programming},
    volume = {70},
    year = {2017},
}

@inproceedings{Botev:2017:Practical_Gauss-Newton_Optimisation_for_Deep,
    author = {Aleksandar Botev and Hippolyt Ritter and David Barber},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {557--565},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Practical {G}auss-{N}ewton Optimisation for Deep Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Sugiyama:2017:Tensor_Balancing_on_Statistical_Manifold,
    author = {Mahito Sugiyama and Hiroyuki Nakahara and Koji Tsuda},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3270--3279},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Tensor Balancing on Statistical Manifold},
    volume = {70},
    year = {2017},
}

@inproceedings{Ye:2017:Approximate_Newton_Methods_and_Their,
    author = {Haishan Ye and Luo Luo and Zhihua Zhang},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3931--3939},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Approximate {N}ewton Methods and Their Local Convergence},
    volume = {70},
    year = {2017},
}

@inproceedings{Zhou:2017:Stochastic_Adaptive_Quasi-Newton_Methods_for,
    author = {Chaoxu Zhou and Wenbo Gao and Donald Goldfarb},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {4150--4159},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Stochastic Adaptive Quasi-{N}ewton Methods for Minimizing Expected Values},
    volume = {70},
    year = {2017},
}

@inproceedings{Braun:2017:Lazifying_Conditional_Gradient_Algorithms,
    author = {Gabor Braun and Sebastian Pokutta and Daniel Zink},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {566--575},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Lazifying Conditional Gradient Algorithms},
    volume = {70},
    year = {2017},
}

@inproceedings{Johnson:2017:StingyCD_Safely_Avoiding_Wasteful_Updates,
    author = {Tyler B. Johnson and Carlos Guestrin},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1752--1760},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {{S}tingy{CD}: Safely Avoiding Wasteful Updates in Coordinate Descent},
    volume = {70},
    year = {2017},
}

@inproceedings{Lan:2017:Conditional_Accelerated_Lazy_Stochastic_Gradient,
    author = {Guanghui Lan and Sebastian Pokutta and Yi Zhou and Daniel Zink},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1965--1974},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Conditional Accelerated Lazy Stochastic Gradient Descent},
    volume = {70},
    year = {2017},
}

@inproceedings{Nguyen:2017:SARAH_A_Novel_Method_for,
    author = {Lam M. Nguyen and Jie Liu and Katya Scheinberg and Martin Taka{\v{c}}},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2613--2621},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {{SARAH}: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient},
    volume = {70},
    year = {2017},
}

@inproceedings{Stich:2017:Approximate_Steepest_Coordinate_Descent,
    author = {Sebastian U. Stich and Anant Raj and Martin Jaggi},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3251--3259},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Approximate Steepest Coordinate Descent},
    volume = {70},
    year = {2017},
}

@inproceedings{Franceschi:2017:Forward_and_Reverse_Gradient-Based_Hyperparameter,
    author = {Luca Franceschi and Michele Donini and Paolo Frasconi and Massimiliano Pontil},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1165--1173},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Forward and Reverse Gradient-Based Hyperparameter Optimization},
    volume = {70},
    year = {2017},
}

@inproceedings{Hu:2017:Dissipativity_Theory_for_Nesterovs_Accelerated,
    author = {Bin Hu and Laurent Lessard},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1549--1557},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Dissipativity Theory for {N}esterov's Accelerated Method},
    volume = {70},
    year = {2017},
}

@inproceedings{Li:2017:Stochastic_Modified_Equations_and_Adaptive,
    author = {Qianxiao Li and Cheng Tai and Weinan E.},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2101--2110},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms},
    volume = {70},
    year = {2017},
}

@inproceedings{Namkoong:2017:Adaptive_Sampling_Probabilities_for_Non-Smooth,
    author = {Hongseok Namkoong and Aman Sinha and Steve Yadlowsky and John C. Duchi},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2574--2583},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Adaptive Sampling Probabilities for Non-Smooth Optimization},
    volume = {70},
    year = {2017},
}

@inproceedings{Tian:2017:An_Analytical_Formula_of_Population,
    author = {Yuandong Tian},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3404--3413},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {An Analytical Formula of Population Gradient for Two-Layered {R}e{LU} Network and Its Applications in Convergence and Critical Point Analysis},
    volume = {70},
    year = {2017},
}

@inproceedings{Allen-Zhu:2017:Natasha_Faster_Non-Convex_Stochastic_Optimization,
    author = {Zeyuan Allen-Zhu},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {89--97},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter},
    volume = {70},
    year = {2017},
}

@inproceedings{Carmon:2017:Convex_Until_Proven_Guilty_Dimension-Free,
    author = {Yair Carmon and John C. Duchi and Oliver Hinder and Aaron Sidford},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {654--663},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {``{C}onvex Until Proven Guilty'': Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions},
    volume = {70},
    year = {2017},
}

@inproceedings{Jin:2017:How_to_Escape_Saddle_Points,
    author = {Chi Jin and Rong Ge and Praneeth Netrapalli and Sham M. Kakade and Michael I. Jordan},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1724--1732},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {How to Escape Saddle Points Efficiently},
    volume = {70},
    year = {2017},
}

@inproceedings{Kohler:2017:Sub-Sampled_Cubic_Regularization_for_Non-Convex,
    author = {Jonas Moritz Kohler and Aurelien Lucchi},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1895--1904},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Sub-Sampled Cubic Regularization for Non-Convex Optimization},
    volume = {70},
    year = {2017},
}

@inproceedings{Li:2017:Convergence_Analysis_of_Proximal_Gradient,
    author = {Qunwei Li and Yi Zhou and Yingbin Liang and Pramod K. Varshney},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2111--2119},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Convergence Analysis of Proximal Gradient With Momentum for Nonconvex Optimization},
    volume = {70},
    year = {2017},
}

@inproceedings{Bergmann:2017:Learning_Texture_Manifolds_With_the,
    author = {Urs Bergmann and Nikolay Jetchev and Roland Vollgraf},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {469--477},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Learning Texture Manifolds With the Periodic Spatial {GAN}},
    volume = {70},
    year = {2017},
}

@inproceedings{Kalchbrenner:2017:Video_Pixel_Networks,
    author = {Nal Kalchbrenner and Aaron van den Oord and Karen Simonyan and Ivo Danihelka and Oriol Vinyals and Alex Graves and Koray Kavukcuoglu},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1771--1779},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Video Pixel Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Kolesnikov:2017:PixelCNN_Models_With_Auxiliary_Variables,
    author = {Alexander Kolesnikov and Christoph H. Lampert},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1905--1914},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {{P}ixel{CNN} Models With Auxiliary Variables for Natural Image Modeling},
    volume = {70},
    year = {2017},
}

@inproceedings{Mescheder:2017:Adversarial_Variational_Bayes_Unifying_Variational,
    author = {Lars Mescheder and Sebastian Nowozin and Andreas Geiger},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2391--2400},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Reed:2017:Parallel_Multiscale_Autoregressive_Density_Estimation,
    author = {Scott Reed and Aaron van den Oord and Nal Kalchbrenner and Sergio Gomez Colmenarejo and Ziyu Wang and Yutian Chen and Dan Belov and Nando de Freitas},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2912--2921},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Parallel Multiscale Autoregressive Density Estimation},
    volume = {70},
    year = {2017},
}

@inproceedings{Arjovsky:2017:Wasserstein_Generative_Adversarial_Networks,
    author = {Martin Arjovsky and Soumith Chintala and Leon Bottou},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {214--223},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {{W}asserstein Generative Adversarial Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Arora:2017:Generalization_and_Equilibrium_in_Generative,
    author = {Sanjeev Arora and Rong Ge and Yingyu Liang and Tengyu Ma and Yi Zhang},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {224--232},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Generalization and Equilibrium in Generative Adversarial Nets ({GAN}s)},
    volume = {70},
    year = {2017},
}

@inproceedings{Kim:2017:Learning_to_Discover_Cross-Domain_Relations,
    author = {Taeksoo Kim and Moonsu Cha and Hyunsoo Kim and Jung Kwon Lee and Jiwon Kim},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1857--1865},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Learning to Discover Cross-Domain Relations With Generative Adversarial Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Mroueh:2017:McGan_Mean_and_Covariance_Feature,
    author = {Youssef Mroueh and Tom Sercu and Vaibhava Goel},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2527--2535},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {{M}c{G}an: Mean and Covariance Feature Matching {GAN}},
    volume = {70},
    year = {2017},
}

@inproceedings{Odena:2017:Conditional_Image_Synthesis_With_Auxiliary,
    author = {Augustus Odena and Christopher Olah and Jonathon Shlens},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2642--2651},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Conditional Image Synthesis With Auxiliary Classifier {GAN}s},
    volume = {70},
    year = {2017},
}

@inproceedings{Hoffman:2017:Learning_Deep_Latent_Gaussian_Models,
    author = {Matthew D. Hoffman},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1510--1519},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Learning Deep Latent {G}aussian Models With {M}arkov Chain {M}onte {C}arlo},
    volume = {70},
    year = {2017},
}

@inproceedings{Liu:2017:Zero-Inflated_Exponential_Family_Embeddings,
    author = {Li-Ping Liu and David M. Blei},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2140--2148},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Zero-Inflated Exponential Family Embeddings},
    volume = {70},
    year = {2017},
}

@inproceedings{Shu:2017:Bottleneck_Conditional_Density_Estimation,
    author = {Rui Shu and Hung H. Bui and Mohammad Ghavamzadeh},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3164--3172},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Bottleneck Conditional Density Estimation},
    volume = {70},
    year = {2017},
}

@inproceedings{Zhao:2017:Learning_Hierarchical_Features_From_Deep,
    author = {Shengjia Zhao and Jiaming Song and Stefano Ermon},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {4091--4099},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Learning Hierarchical Features From Deep Generative Models},
    volume = {70},
    year = {2017},
}

@inproceedings{Czarnecki:2017:Understanding_Synthetic_Gradients_and_Decoupled,
    author = {Wojciech Marian Czarnecki and Grzegorz {\'{S}}wirszcz and Max Jaderberg and Simon Osindero and Oriol Vinyals and Koray Kavukcuoglu},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {904--912},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Understanding Synthetic Gradients and Decoupled Neural Interfaces},
    volume = {70},
    year = {2017},
}

@inproceedings{Jaderberg:2017:Decoupled_Neural_Interfaces_Using_Synthetic,
    author = {Max Jaderberg and Wojciech Marian Czarnecki and Simon Osindero and Oriol Vinyals and Alex Graves and David Silver and Koray Kavukcuoglu},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1627--1635},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Decoupled Neural Interfaces Using Synthetic Gradients},
    volume = {70},
    year = {2017},
}

@inproceedings{Shrikumar:2017:Learning_Important_Features_Through_Propagating,
    author = {Avanti Shrikumar and Peyton Greenside and Anshul Kundaje},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3145--3153},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Learning Important Features Through Propagating Activation Differences},
    volume = {70},
    year = {2017},
}

@inproceedings{Sun:2017:MeProp_Sparsified_Back_Propagation_for,
    author = {Xu Sun and Xuancheng Ren and Shuming Ma and Houfeng Wang},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3299--3308},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Me{P}rop: Sparsified Back Propagation for Accelerated Deep Learning With Reduced Overfitting},
    volume = {70},
    year = {2017},
}

@inproceedings{Tokui:2017:Evaluating_the_Variance_of_Likelihood-Ratio,
    author = {Seiya Tokui and Issei Sato},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3414--3423},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Evaluating the Variance of Likelihood-Ratio Gradient Estimators},
    volume = {70},
    year = {2017},
}

@inproceedings{Bojanowski:2017:Unsupervised_Learning_by_Predicting_Noise,
    author = {Piotr Bojanowski and Armand Joulin},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {517--526},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Unsupervised Learning by Predicting Noise},
    volume = {70},
    year = {2017},
}

@inproceedings{Henriques:2017:Warped_Convolutions_Efficient_Invariance_to,
    author = {Joao F. Henriques and Andrea Vedaldi},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1461--1469},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Warped Convolutions: Efficient Invariance to Spatial Transformations},
    volume = {70},
    year = {2017},
}

@inproceedings{Khasanova:2017:Graph-Based_Isometry_Invariant_Representation_Learning,
    author = {Renata Khasanova and Pascal Frossard},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1847--1856},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Graph-Based Isometry Invariant Representation Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Lei:2017:Deriving_Neural_Architectures_From_Sequence,
    author = {Tao Lei and Wengong Jin and Regina Barzilay and Tommi Jaakkola},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2024--2033},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Deriving Neural Architectures From Sequence and Graph Kernels},
    volume = {70},
    year = {2017},
}

@inproceedings{Ravanbakhsh:2017:Equivariance_Through_Parameter-Sharing,
    author = {Siamak Ravanbakhsh and Jeff Schneider and Barnabas Poczos},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2892--2901},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Equivariance Through Parameter-Sharing},
    volume = {70},
    year = {2017},
}

@inproceedings{Cortes:2017:AdaNet_Adaptive_Structural_Learning_of,
    author = {Corinna Cortes and Xavier Gonzalvo and Vitaly Kuznetsov and Mehryar Mohri and Scott Yang},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {874--883},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {{A}da{N}et: Adaptive Structural Learning of Artificial Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Finn:2017:Model-Agnostic_Meta-Learning_for_Fast_Adaptation,
    author = {Chelsea Finn and Pieter Abbeel and Sergey Levine},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1126--1135},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Kim:2017:SplitNet_Learning_to_Semantically_Split,
    author = {Juyong Kim and Yookoon Park and Gunhee Kim and Sung Ju Hwang},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1866--1874},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {{S}plit{N}et: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization},
    volume = {70},
    year = {2017},
}

@inproceedings{Long:2017:Deep_Transfer_Learning_With_Joint,
    author = {Mingsheng Long and Han Zhu and Jianmin Wang and Michael I. Jordan},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2208--2217},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Deep Transfer Learning With Joint Adaptation Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Munkhdalai:2017:Meta_Networks,
    author = {Tsendsuren Munkhdalai and Hong Yu},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2554--2563},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Meta Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Bachman:2017:Learning_Algorithms_for_Active_Learning,
    author = {Philip Bachman and Alessandro Sordoni and Adam Trischler},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {301--310},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Learning Algorithms for Active Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Chen:2017:Learning_to_Learn_Without_Gradient,
    author = {Yutian Chen and Matthew W. Hoffman and Sergio Gomez Colmenarejo and Misha Denil and Timothy P. Lillicrap and Matt Botvinick and Nando de Freitas},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {748--756},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Learning to Learn Without Gradient Descent by Gradient Descent},
    volume = {70},
    year = {2017},
}

@inproceedings{Lv:2017:Learning_Gradient_Descent_Better_Generalization,
    author = {Kaifeng Lv and Shunhua Jiang and Jian Li},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2247--2255},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Learning Gradient Descent: Better Generalization and Longer Horizons},
    volume = {70},
    year = {2017},
}

@inproceedings{Wichrowska:2017:Learned_Optimizers_That_Scale_and,
    author = {Olga Wichrowska and Niru Maheswaranathan and Matthew W. Hoffman and Sergio Gomez Colmenarejo and Misha Denil and Nando de Freitas and Jascha Sohl-Dickstein},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3751--3760},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Learned Optimizers That Scale and Generalize},
    volume = {70},
    year = {2017},
}

@inproceedings{Bolukbasi:2017:Adaptive_Neural_Networks_for_Efficient,
    author = {Tolga Bolukbasi and Joseph Wang and Ofer Dekel and Venkatesh Saligrama},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {527--536},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Adaptive Neural Networks for Efficient Inference},
    volume = {70},
    year = {2017},
}

@inproceedings{Luo:2017:Learning_Deep_Architectures_via_Generalized,
    author = {Ping Luo},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2238--2246},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Learning Deep Architectures via Generalized Whitened Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Sun:2017:Relative_Fisher_Information_and_Natural,
    author = {Ke Sun and Frank Nielsen},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3289--3298},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Relative {F}isher Information and Natural Gradient for Learning Large Modular Models},
    volume = {70},
    year = {2017},
}

@inproceedings{Yoon:2017:Combined_Group_and_Exclusive_Sparsity,
    author = {Jaehong Yoon and Sung Ju Hwang},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3958--3966},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Combined Group and Exclusive Sparsity for Deep Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Zenke:2017:Continual_Learning_Through_Synaptic_Intelligence,
    author = {Friedemann Zenke and Ben Poole and Surya Ganguli},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3987--3995},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Continual Learning Through Synaptic Intelligence},
    volume = {70},
    year = {2017},
}

@inproceedings{Amos:2017:Input_Convex_Neural_Networks,
    author = {Brandon Amos and Lei Xu and J. Zico Kolter},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {146--155},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Input Convex Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Amos:2017:OptNet_Differentiable_Optimization_as_a,
    author = {Brandon Amos and J. Zico Kolter},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {136--145},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {{O}pt{N}et: Differentiable Optimization as a Layer in Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Cisse:2017:Parseval_Networks_Improving_Robustness_to,
    author = {Moustapha Cisse and Piotr Bojanowski and Edouard Grave and Yann Dauphin and Nicolas Usunier},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {854--863},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Parseval Networks: Improving Robustness to Adversarial Examples},
    volume = {70},
    year = {2017},
}

@inproceedings{Mollaysa:2017:Regularising_Non-Linear_Models_Using_Feature,
    author = {Amina Mollaysa and Pablo Strasser and Alexandros Kalousis},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2508--2517},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Regularising Non-Linear Models Using Feature Side-Information},
    volume = {70},
    year = {2017},
}

@inproceedings{Arpit:2017:A_Closer_Look_at_Memorization,
    author = {Devansh Arpit and Stanis{\l}aw Jastrz{\k{e}}bski and Nicolas Ballas and David Krueger and Emmanuel Bengio and Maxinder S. Kanwal and Tegan Maharaj and Asja Fischer and Aaron Courville and Yoshua Bengio and Simon Lacoste-Julien},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {233--242},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {A Closer Look at Memorization in Deep Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Guo:2017:On_Calibration_of_Modern_Neural,
    author = {Chuan Guo and Geoff Pleiss and Yu Sun and Kilian Q. Weinberger},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1321--1330},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {On Calibration of Modern Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Nagamine:2017:Understanding_the_Representation_and_Computation,
    author = {Tasha Nagamine and Nima Mesgarani},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2564--2573},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Understanding the Representation and Computation of Multilayer Perceptrons: A Case Study in Speech Recognition},
    volume = {70},
    year = {2017},
}

@inproceedings{Ritter:2017:Cognitive_Psychology_for_Deep_Neural,
    author = {Samuel Ritter and David G. T. Barrett and Adam Santoro and Matt M. Botvinick},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2940--2949},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study},
    volume = {70},
    year = {2017},
}

@inproceedings{Sundararajan:2017:Axiomatic_Attribution_for_Deep_Networks,
    author = {Mukund Sundararajan and Ankur Taly and Qiqi Yan},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3319--3328},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Axiomatic Attribution for Deep Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Budden:2017:Deep_Tensor_Convolution_on_Multicores,
    author = {David Budden and Alexander Matveev and Shibani Santurkar and Shraman Ray Chaudhuri and Nir Shavit},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {615--624},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Deep Tensor Convolution on Multicores},
    volume = {70},
    year = {2017},
}

@inproceedings{Cho:2017:MEC_Memory-Efficient_Convolution_for_Deep,
    author = {Minsik Cho and Daniel Brand},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {815--824},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {{MEC}: Memory-Efficient Convolution for Deep Neural Network},
    volume = {70},
    year = {2017},
}

@inproceedings{Grave:2017:Efficient_Softmax_Approximation_for_GPUs,
    author = {Edouard Grave and Armand Joulin and Moustapha Cisse and David Grangier and Herve Jegou},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1302--1310},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Efficient Softmax Approximation for {GPU}s},
    volume = {70},
    year = {2017},
}

@inproceedings{Mirhoseini:2017:Device_Placement_Optimization_With_Reinforcement,
    author = {Azalia Mirhoseini and Hieu Pham and Quoc V. Le and Benoit Steiner and Rasmus Larsen and Yuefeng Zhou and Naveen Kumar and Mohammad Norouzi and Samy Bengio and Jeff Dean},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2430--2439},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Device Placement Optimization With Reinforcement Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Wang:2017:Beyond_Filters_Compact_Feature_Map,
    author = {Yunhe Wang and Chang Xu and Chao Xu and Dacheng Tao},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3703--3711},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Beyond Filters: Compact Feature Map for Portable Deep Model},
    volume = {70},
    year = {2017},
}

@inproceedings{Beckham:2017:Unimodal_Probability_Distributions_for_Deep,
    author = {Christopher Beckham and Christopher Pal},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {411--419},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Unimodal Probability Distributions for Deep Ordinal Classification},
    volume = {70},
    year = {2017},
}

@inproceedings{Li:2017:Dropout_Inference_in_Bayesian_Neural,
    author = {Yingzhen Li and Yarin Gal},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2052--2061},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Dropout Inference in {B}ayesian Neural Networks With Alpha-Divergences},
    volume = {70},
    year = {2017},
}

@inproceedings{Louizos:2017:Multiplicative_Normalizing_Flows_for_Variational,
    author = {Christos Louizos and Max Welling},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2218--2227},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Multiplicative Normalizing Flows for Variational {B}ayesian Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Molchanov:2017:Variational_Dropout_Sparsifies_Deep_Neural,
    author = {Dmitry Molchanov and Arsenii Ashukha and Dmitry Vetrov},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2498--2507},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Variational Dropout Sparsifies Deep Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Balduzzi:2017:Neural_Taylor_Approximations_Convergence_and,
    author = {David Balduzzi and Brian McWilliams and Tony Butler-Yeoman},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {351--360},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Balduzzi:2017:The_Shattered_Gradients_Problem_If,
    author = {David Balduzzi and Marcus Frean and Lennox Leary and J. P. Lewis and Kurt Wan-Duo Ma and Brian McWilliams},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {342--350},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {The Shattered Gradients Problem: If Resnets Are the Answer, Then What Is the Question?},
    volume = {70},
    year = {2017},
}

@inproceedings{Dinh:2017:Sharp_Minima_Can_Generalize_for,
    author = {Laurent Dinh and Razvan Pascanu and Samy Bengio and Yoshua Bengio},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1019--1028},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Sharp Minima Can Generalize for Deep Nets},
    volume = {70},
    year = {2017},
}

@inproceedings{Nguyen:2017:The_Loss_Surface_of_Deep,
    author = {Quynh Nguyen and Matthias Hein},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2603--2612},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {The Loss Surface of Deep and Wide Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Pennington:2017:Geometry_of_Neural_Network_Loss,
    author = {Jeffrey Pennington and Yasaman Bahri},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2798--2806},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Geometry of Neural Network Loss Surfaces via Random Matrix Theory},
    volume = {70},
    year = {2017},
}

@inproceedings{Sakr:2017:Analytical_Guarantees_on_Numerical_Precision,
    author = {Charbel Sakr and Yongjune Kim and Naresh Shanbhag},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3007--3016},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Analytical Guarantees on Numerical Precision of Deep Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Shalev-Shwartz:2017:Failures_of_Gradient-Based_Deep_Learning,
    author = {Shai Shalev-Shwartz and Ohad Shamir and Shaked Shammah},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3067--3075},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Failures of Gradient-Based Deep Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Zhao:2017:Theoretical_Properties_for_Neural_Networks,
    author = {Liang Zhao and Siyu Liao and Yanzhi Wang and Zhe Li and Jian Tang and Bo Yuan},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {4082--4090},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Theoretical Properties for Neural Networks With Weight Matrices of Low Displacement Rank},
    volume = {70},
    year = {2017},
}

@inproceedings{Zheng:2017:Follow_the_Moving_Leader_in,
    author = {Shuai Zheng and James T. Kwok},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {4110--4119},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Follow the Moving Leader in Deep Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Zhong:2017:Recovery_Guarantees_for_One-Hidden-Layer_Neural,
    author = {Kai Zhong and Zhao Song and Prateek Jain and Peter L. Bartlett and Inderjit S. Dhillon},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {4140--4149},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Recovery Guarantees for One-Hidden-Layer Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Brutzkus:2017:Globally_Optimal_Gradient_Descent_for,
    author = {Alon Brutzkus and Amir Globerson},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {605--614},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Globally Optimal Gradient Descent for a {C}onv{N}et With {G}aussian Inputs},
    volume = {70},
    year = {2017},
}

@inproceedings{Raghu:2017:On_the_Expressive_Power_of,
    author = {Maithra Raghu and Ben Poole and Jon Kleinberg and Surya Ganguli and Jascha Sohl-Dickstein},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2847--2854},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {On the Expressive Power of Deep Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Safran:2017:Depth-Width_Tradeoffs_in_Approximating_Natural,
    author = {Itay Safran and Ohad Shamir},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2979--2987},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Depth-Width Tradeoffs in Approximating Natural Functions With Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Zhang:2017:Convexified_Convolutional_Neural_Networks,
    author = {Yuchen Zhang and Percy Liang and Martin J. Wainwright},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {4044--4053},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Convexified Convolutional Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Anschel:2017:Averaged-DQN_Variance_Reduction_and_Stabilization,
    author = {Oron Anschel and Nir Baram and Nahum Shimkin},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {176--185},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Averaged-{DQN}: Variance Reduction and Stabilization for Deep Reinforcement Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Foerster:2017:Stabilising_Experience_Replay_for_Deep,
    author = {Jakob Foerster and Nantas Nardelli and Gregory Farquhar and Triantafyllos Afouras and Philip H. S. Torr and Pushmeet Kohli and Shimon Whiteson},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1146--1155},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Oh:2017:Zero-Shot_Task_Generalization_With_Multi-Task,
    author = {Junhyuk Oh and Satinder Singh and Honglak Lee and Pushmeet Kohli},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2661--2670},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Zero-Shot Task Generalization With Multi-Task Deep Reinforcement Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Ostrovski:2017:Count-Based_Exploration_With_Neural_Density,
    author = {Georg Ostrovski and Marc G. Bellemare and Aaron van den Oord and Remi Munos},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2721--2730},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Count-Based Exploration With Neural Density Models},
    volume = {70},
    year = {2017},
}

@inproceedings{Silver:2017:The_Predictron_End-To-End_Learning_and,
    author = {David Silver and Hado van Hasselt and Matteo Hessel and Tom Schaul and Arthur Guez and Tim Harley and Gabriel Dulac-Arnold and David Reichert and Neil Rabinowitz and Andre Barreto and Thomas Degris},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3191--3199},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {The Predictron: End-To-End Learning and Planning},
    volume = {70},
    year = {2017},
}

@inproceedings{Bello:2017:Neural_Optimizer_Search_With_Reinforcement,
    author = {Irwan Bello and Barret Zoph and Vijay Vasudevan and Quoc V. Le},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {459--468},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Neural Optimizer Search With Reinforcement Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{McGill:2017:Deciding_How_to_Decide_Dynamic,
    author = {Mason McGill and Pietro Perona},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2363--2372},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Deciding How to Decide: Dynamic Routing in Artificial Neural Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Pritzel:2017:Neural_Episodic_Control,
    author = {Alexander Pritzel and Benigno Uria and Sriram Srinivasan and Adria Puigdomenech Badia and Oriol Vinyals and Demis Hassabis and Daan Wierstra and Charles Blundell},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2827--2836},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Neural Episodic Control},
    volume = {70},
    year = {2017},
}

@inproceedings{Vezhnevets:2017:FeUdal_Networks_for_Hierarchical_Reinforcement,
    author = {Alexander Sasha Vezhnevets and Simon Osindero and Tom Schaul and Nicolas Heess and Max Jaderberg and David Silver and Koray Kavukcuoglu},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3540--3549},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {{F}e{U}dal Networks for Hierarchical Reinforcement Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Scaman:2017:Optimal_Algorithms_for_Smooth_and,
    author = {Kevin Scaman and Francis Bach and Sebastien Bubeck and Yin Tat Lee and Laurent Massoulie},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3027--3036},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Xu:2017:Adaptive_Consensus_ADMM_for_Distributed,
    author = {Zheng Xu and Gavin Taylor and Hao Li and Mario A. T. Figueiredo and Xiaoming Yuan and Tom Goldstein},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3841--3850},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Adaptive Consensus {ADMM} for Distributed Optimization},
    volume = {70},
    year = {2017},
}

@inproceedings{Zhang:2017:Projection-Free_Distributed_Online_Learning_in,
    author = {Wenpeng Zhang and Peilin Zhao and Wenwu Zhu and Steven C. H. Hoi and Tong Zhang},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {4054--4062},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Projection-Free Distributed Online Learning in Networks},
    volume = {70},
    year = {2017},
}

@inproceedings{Zheng:2017:Asynchronous_Stochastic_Gradient_Descent_With,
    author = {Shuxin Zheng and Qi Meng and Taifeng Wang and Wei Chen and Nenghai Yu and Zhi-Ming Ma and Tie-Yan Liu},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {4120--4129},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Asynchronous Stochastic Gradient Descent With Delay Compensation},
    volume = {70},
    year = {2017},
}

@inproceedings{Appel:2017:A_Simple_Multi-Class_Boosting_Framework,
    author = {Ron Appel and Pietro Perona},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {186--194},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {A Simple Multi-Class Boosting Framework With Theoretical Guarantees and Empirical Proficiency},
    volume = {70},
    year = {2017},
}

@inproceedings{Begon:2017:Globally_Induced_Forest_A_Prepruning,
    author = {Jean-Michel Begon and Arnaud Joly and Pierre Geurts},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {420--428},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Globally Induced Forest: A Prepruning Compression Scheme},
    volume = {70},
    year = {2017},
}

@inproceedings{Lee:2017:Confident_Multiple_Choice_Learning,
    author = {Kimin Lee and Changho Hwang and KyoungSoo Park and Jinwoo Shin},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2014--2023},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Confident Multiple Choice Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Li:2017:Forest-Type_Regression_With_General_Losses,
    author = {Alexander Hanbo Li and Andrew Martin},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2091--2100},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Forest-Type Regression With General Losses and Robust Forest},
    volume = {70},
    year = {2017},
}

@inproceedings{Si:2017:Gradient_Boosted_Decision_Trees_for,
    author = {Si Si and Huan Zhang and S. Sathiya Keerthi and Dhruv Mahajan and Inderjit S. Dhillon and Cho-Jui Hsieh},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3182--3190},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Gradient Boosted Decision Trees for High Dimensional Sparse Output},
    volume = {70},
    year = {2017},
}

@inproceedings{Balduzzi:2017:Strongly-Typed_Agents_Are_Guaranteed_to,
    author = {David Balduzzi},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {332--341},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Strongly-Typed Agents Are Guaranteed to Interact Safely},
    volume = {70},
    year = {2017},
}

@inproceedings{Brown:2017:Reduced_Space_and_Faster_Convergence,
    author = {Noam Brown and Tuomas Sandholm},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {596--604},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Reduced Space and Faster Convergence in Imperfect-Information Games via Pruning},
    volume = {70},
    year = {2017},
}

@inproceedings{Farina:2017:Regret_Minimization_in_Behaviorally-Constrained_Zero-Sum,
    author = {Gabriele Farina and Christian Kroer and Tuomas Sandholm},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1107--1116},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Regret Minimization in Behaviorally-Constrained Zero-Sum Games},
    volume = {70},
    year = {2017},
}

@inproceedings{Le:2017:Coordinated_Multi-Agent_Imitation_Learning,
    author = {Hoang M. Le and Yisong Yue and Peter Carr and Patrick Lucey},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {1995--2003},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Coordinated Multi-Agent Imitation Learning},
    volume = {70},
    year = {2017},
}

@inproceedings{Omidshafiei:2017:Deep_Decentralized_Multi-Task_Multi-Agent_Reinforcement,
    author = {Shayegan Omidshafiei and Jason Pazis and Christopher Amato and Jonathan P. How and John Vian},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2681--2690},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Deep Decentralized Multi-Task Multi-Agent Reinforcement Learning Under Partial Observability},
    volume = {70},
    year = {2017},
}

@inproceedings{Cutajar:2017:Random_Feature_Expansions_for_Deep,
    author = {Kurt Cutajar and Edwin V. Bonilla and Pietro Michiardi and Maurizio Filippone},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {884--893},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Random Feature Expansions for Deep {G}aussian Processes},
    volume = {70},
    year = {2017},
}

@inproceedings{Daxberger:2017:Distributed_Batch_Gaussian_Process_Optimization,
    author = {Erik A. Daxberger and Bryan Kian Hsiang Low},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {951--960},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Distributed Batch {G}aussian Process Optimization},
    volume = {70},
    year = {2017},
}

@inproceedings{Peng:2017:Asynchronous_Distributed_Variational_Gaussian_Process,
    author = {Hao Peng and Shandian Zhe and Xiao Zhang and Yuan Qi},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2788--2797},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Asynchronous Distributed Variational {G}aussian Process for Regression},
    volume = {70},
    year = {2017},
}

@inproceedings{Rana:2017:High_Dimensional_Bayesian_Optimization_With,
    author = {Santu Rana and Cheng Li and Sunil Gupta and Vu Nguyen and Svetha Venkatesh},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {2883--2891},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {High Dimensional {B}ayesian Optimization With Elastic {G}aussian Process},
    volume = {70},
    year = {2017},
}

@inproceedings{Villacampa-Calvo:2017:Scalable_Multi-Class_Gaussian_Process_Classification,
    author = {Carlos Villacampa-Calvo and Daniel Hernandez-Lobato},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {3550--3559},
    publisher = {PMLR},
    series = {Proceedings of Machine Learning Research},
    title = {Scalable Multi-Class {G}aussian Process Classification Using Expectation Propagation},
    volume = {70},
    year = {2017},
}

@inproceedings{Alaa:2017:Learning_From_Clinical_Judgments_Semi-Markov-Modulated,
    author = {Ahmed M. Alaa and Scott Hu and Mihaela van der Schaar},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    editor = {Doina Precup and Yee Whye Teh},
    month = {August},
    pages = {60--69},
    publisher = {PMLR},
    series

repository: wesselb/catalogue

repo_name: wesselb/catalogue