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ICML 2019 Papers with Code

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519 out of 774 ICML 2019 papers have code published. We list the code in the following table.

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TITLEAUTHORSCODE URLPAPER URL
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEsGabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause, Bernhard Sch�lkopf, Stefan Bauerhttps://github.com/gabb7/AReS-MaRShttp://proceedings.mlr.press/v97/abbati19a.html
Dynamic Weights in Multi-Objective Deep Reinforcement LearningAxel Abels, Diederik Roijers, Tom Lenaerts, Ann Now�, Denis Steckelmacherhttps://github.com/axelabels/DynMORLhttp://proceedings.mlr.press/v97/abels19a.html
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood MixingSami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyanhttps://github.com/samihaija/mixhophttp://proceedings.mlr.press/v97/abu-el-haija19a.html
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement LearningTameem Adel, Adrian Wellerhttps://github.com/tameemadel/tibgmhttp://proceedings.mlr.press/v97/adel19a.html
PAC Learnability of Node Functions in Networked Dynamical SystemsAbhijin Adiga, Chris J Kuhlman, Madhav Marathe, S Ravi, Anil Vullikantihttps://github.com/NSSAC/pac_learning_gds_ICML19.githttp://proceedings.mlr.press/v97/adiga19a.html
Static Automatic Batching In TensorFlowAshish Agarwalhttps://github.com/tensorflow/tensorflow/tree/f95701085f7f7eba21f4d49f96166e35558fa66e/tensorflow/python/ops/parallel_forhttp://proceedings.mlr.press/v97/agarwal19a.html
Efficient Full-Matrix Adaptive RegularizationNaman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhanghttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/opt/python/training/ggt.pyhttp://proceedings.mlr.press/v97/agarwal19b.html
Fair Regression: Quantitative Definitions and Reduction-Based AlgorithmsAlekh Agarwal, Miroslav Dudik, Zhiwei Steven Wuhttps://github.com/steven7woo/fair_regressionhttp://proceedings.mlr.press/v97/agarwal19d.html
Learning to Generalize from Sparse and Underspecified RewardsRishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzihttps://github.com/google-research/google-research/tree/master/meta_reward_learninghttp://proceedings.mlr.press/v97/agarwal19e.html
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High DimensionsRaj Agrawal, Brian Trippe, Jonathan Huggins, Tamara Broderickhttps://github.com/agrawalraj/skimhttp://proceedings.mlr.press/v97/agrawal19a.html
Understanding the Impact of Entropy on Policy OptimizationZafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmanshttps://github.com/google-research/policy-learning-landscapehttp://proceedings.mlr.press/v97/ahmed19a.html
Fairwashing: the risk of rationalizationUlrich Aivodji, Hiromi Arai, Olivier Fortineau, S�bastien Gambs, Satoshi Hara, Alain Tapphttps://github.com/aivodji/LaundryMLhttp://proceedings.mlr.press/v97/aivodji19a.html
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture SearchYouhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, Kouhei Nishidahttps://github.com/shirakawas/ASNG-NAShttp://proceedings.mlr.press/v97/akimoto19a.html
Projections for Approximate Policy Iteration AlgorithmsRiad Akrour, Joni Pajarinen, Jan Peters, Gerhard Neumannhttps://github.com/akrouriad/papihttp://proceedings.mlr.press/v97/akrour19a.html
Multi-objective training of Generative Adversarial Networks with multiple discriminatorsIsabela Albuquerque, Joao Monteiro, Thang Doan, Breandan Considine, Tiago Falk, Ioannis Mitliagkashttps://github.com/joaomonteirof/hGANhttp://proceedings.mlr.press/v97/albuquerque19a.html
Graph Element Networks: adaptive, structured computation and memoryFerran Alet, Adarsh Keshav Jeewajee, Maria Bauza Villalonga, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Kaelblinghttps://github.com/FerranAlet/graph_element_networkshttp://proceedings.mlr.press/v97/alet19a.html
Asynchronous Batch Bayesian Optimisation with Improved Local PenalisationAhsan Alvi, Binxin Ru, Jan-Peter Calliess, Stephen Roberts, Michael A. Osbornehttps://github.com/a5a/asynchronous-BOhttp://proceedings.mlr.press/v97/alvi19a.html
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value ApproximationMarco Ancona, Cengiz Oztireli, Markus Grosshttps://github.com/marcoancona/DASPhttp://proceedings.mlr.press/v97/ancona19a.html
Scaling Up Ordinal Embedding: A Landmark ApproachJesse Anderton, Javed Aslamhttps://github.com/jesand/lloehttp://proceedings.mlr.press/v97/anderton19a.html
Sorting Out Lipschitz Function ApproximationCem Anil, James Lucas, Roger Grossehttps://github.com/cemanil/LNetshttp://proceedings.mlr.press/v97/anil19a.html
Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous DataLuigi Antelmi, Nicholas Ayache, Philippe Robert, Marco Lorenzihttps://gitlab.inria.fr/epione_ML/mcvaehttp://proceedings.mlr.press/v97/antelmi19a.html
Unsupervised Label Noise Modeling and Loss CorrectionEric Arazo, Diego Ortego, Paul Albert, Noel O�Connor, Kevin Mcguinnesshttps://git.io/fjsvEhttp://proceedings.mlr.press/v97/arazo19a.html
Stochastic Gradient Push for Distributed Deep LearningMahmoud Assran, Nicolas Loizou, Nicolas Ballas, Mike Rabbathttps://github.com/facebookresearch/stochastic_gradient_pushhttp://proceedings.mlr.press/v97/assran19a.html
Bayesian Optimization of Composite FunctionsRaul Astudillo, Peter Frazierhttps://github.com/RaulAstudillo06/BOCFhttp://proceedings.mlr.press/v97/astudillo19a.html
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCAJordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra Slavkovichttps://github.com/amariakenney/ExpMechFPCAhttp://proceedings.mlr.press/v97/awan19a.html
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured DataSergul Aydore, Bertrand Thirion, Gael Varoquauxhttps://github.com/sergulaydore/Feature-Grouping-Regularizerhttp://proceedings.mlr.press/v97/aydore19a.html
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behaviorFadhel Ayed, Juho Lee, Francois Caronhttps://github.com/OxCSML-BayesNP/doublepowerlawhttp://proceedings.mlr.press/v97/ayed19a.html
Scalable Fair ClusteringArturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagnerhttps://github.com/talwagner/fair_clusteringhttp://proceedings.mlr.press/v97/backurs19a.html
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANsYogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizihttps://github.com/yogeshbalaji/EntropicGANs_meet_VAEshttp://proceedings.mlr.press/v97/balaji19a.html
Provable Guarantees for Gradient-Based Meta-LearningMaria-Florina Balcan, Mikhail Khodak, Ameet Talwalkarhttps://github.com/mkhodak/FMRLhttp://proceedings.mlr.press/v97/balcan19a.html
Concrete Autoencoders: Differentiable Feature Selection and ReconstructionMuhammed Fatih Balin, Abubakar Abid, James Zouhttps://github.com/mfbalin/Concrete-Autoencodershttp://proceedings.mlr.press/v97/balin19a.html
HOList: An Environment for Machine Learning of Higher Order Logic Theorem ProvingKshitij Bansal, Sarah Loos, Markus Rabe, Christian Szegedy, Stewart Wilcoxhttps://github.com/tensorflow/deepmathhttp://proceedings.mlr.press/v97/bansal19a.html
Learning to Route in Similarity GraphsDmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin, Artem Babenkohttps://github.com/dbaranchuk/learning-to-routehttp://proceedings.mlr.press/v97/baranchuk19a.html
A Personalized Affective Memory Model for Improving Emotion RecognitionPablo Barros, German Parisi, Stefan Wermterhttps://github.com/pablovin/P-AffMemhttp://proceedings.mlr.press/v97/barros19a.html
Noise2Self: Blind Denoising by Self-SupervisionJoshua Batson, Loic Royerhttps://github.com/czbiohub/noise2selfhttp://proceedings.mlr.press/v97/batson19a.html
Efficient optimization of loops and limits with randomized telescoping sumsAlex Beatson, Ryan P Adamshttps://github.com/PrincetonLIPS/randomized_telescopeshttp://proceedings.mlr.press/v97/beatson19a.html
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature SpacesPhilipp Becker, Harit Pandya, Gregor Gebhardt, Cheng Zhao, C. James Taylor, Gerhard Neumannhttps://github.com/LCAS/RKNhttp://proceedings.mlr.press/v97/becker19a.html
Active Learning for Probabilistic Structured Prediction of Cuts and MatchingsSima Behpour, Anqi Liu, Brian Ziebarthttps://github.com/sima111b/ActiveStructuredPredictionhttp://proceedings.mlr.press/v97/behpour19a.html
Invertible Residual NetworksJens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Joern-Henrik Jacobsenhttps://github.com/jhjacobsen/invertible-resnethttp://proceedings.mlr.press/v97/behrmann19a.html
Greedy Layerwise Learning Can Scale To ImageNetEugene Belilovsky, Michael Eickenberg, Edouard Oyallonhttps://github.com/eugenium/layerCNNhttp://proceedings.mlr.press/v97/belilovsky19a.html
Overcoming Multi-model ForgettingYassine Benyahia, Kaicheng Yu, Kamil Bennani Smires, Martin Jaggi, Anthony C. Davison, Mathieu Salzmann, Claudiu Musathttps://github.com/kcyu2014/multi-model-forgettinghttp://proceedings.mlr.press/v97/benyahia19a.html
Optimal Kronecker-Sum Approximation of Real Time Recurrent LearningFrederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Stegerhttps://github.com/marcelomatheusgauy/optimal_kronecker_approximationhttp://proceedings.mlr.press/v97/benzing19a.html
Adversarially Learned Representations for Information Obfuscation and InferenceMartin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues, Galen Reeves, Guillermo Sapirohttps://github.com/MartinBertran/AIOIhttp://proceedings.mlr.press/v97/bertran19a.html
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable CaseAlina Beygelzimer, David Pal, Balazs Szorenyi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhanghttps://github.com/bahh723/separable-bandit-classification.githttp://proceedings.mlr.press/v97/beygelzimer19a.html
Analyzing Federated Learning through an Adversarial LensArjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin Calohttps://github.com/inspire-group/ModelPoisoninghttp://proceedings.mlr.press/v97/bhagoji19a.html
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field InferenceYatao Bian, Joachim Buhmann, Andreas Krausehttps://github.com/bianan/optimal-dr-submodular-maxhttp://proceedings.mlr.press/v97/bian19a.html
More Efficient Off-Policy Evaluation through Regularized Targeted LearningAurelien Bibaut, Ivana Malenica, Nikos Vlassis, Mark Van Der Laanhttps://github.com/aurelienbibaut2/LTMLE_OPEhttp://proceedings.mlr.press/v97/bibaut19a.html
A Kernel Perspective for Regularizing Deep Neural NetworksAlberto Bietti, Gr�goire Mialon, Dexiong Chen, Julien Mairalhttps://github.com/albietz/kernel_reghttp://proceedings.mlr.press/v97/bietti19a.html
Adversarial Attacks on Node Embeddings via Graph PoisoningAleksandar Bojchevski, Stephan G�nnemannhttps://github.com/abojchevski/node_embedding_attackhttp://proceedings.mlr.press/v97/bojchevski19a.html
Online Variance Reduction with MixturesZal�n Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krausehttps://github.com/zalanborsos/variance-reduction-mixtureshttp://proceedings.mlr.press/v97/borsos19a.html
Compositional Fairness Constraints for Graph EmbeddingsAvishek Bose, William Hamiltonhttps://github.com/joeybose/Flexible-Fairness-Constraintshttp://proceedings.mlr.press/v97/bose19a.html
Unreproducible Research is ReproducibleXavier Bouthillier, C�sar Laurent, Pascal Vincenthttps://github.com/bouthilx/repro-icml-2019http://proceedings.mlr.press/v97/bouthillier19a.html
Blended Conditonal GradientsG�bor Braun, Sebastian Pokutta, Dan Tu, Stephen Wrighthttps://github.com/pokutta/bcghttp://proceedings.mlr.press/v97/braun19a.html
Coresets for Ordered Weighted ClusteringVladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wuhttps://github.com/sfjiang1990/Coresets-OKMhttp://proceedings.mlr.press/v97/braverman19a.html
Active Manifolds: A non-linear analogue to Active SubspacesRobert Bridges, Anthony Gruber, Christopher Felder, Miki Verma, Chelsey Hoffhttps://github.com/bridgesra/active-manifold-icml2019-codehttp://proceedings.mlr.press/v97/bridges19a.html
Conditioning by adaptive sampling for robust designDavid Brookes, Hahnbeom Park, Jennifer Listgartenhttps://github.com/dhbrookes/CbAShttp://proceedings.mlr.press/v97/brookes19a.html
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from ObservationsDaniel Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekumhttps://github.com/hiwonjoon/ICML2019-TREXhttp://proceedings.mlr.press/v97/brown19a.html
Understanding the Origins of Bias in Word EmbeddingsMarc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard Zemelhttps://github.com/mebrunet/understanding-biashttp://proceedings.mlr.press/v97/brunet19a.html
Learning Generative Models across Incomparable SpacesCharlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelkahttps://github.com/bunnech/gw_ganhttp://proceedings.mlr.press/v97/bunne19a.html
Rates of Convergence for Sparse Variational Gaussian Process RegressionDavid Burt, Carl Edward Rasmussen, Mark Van Der Wilkhttps://github.com/DavidBurt2/Rates-of-Convergence-SGPRhttp://proceedings.mlr.press/v97/burt19a.html
A Quantitative Analysis of the Effect of Batch Normalization on Gradient DescentYongqiang Cai, Qianxiao Li, Zuowei Shenhttps://github.com/Lightmann/BatchNormGD.githttp://proceedings.mlr.press/v97/cai19a.html
Active Embedding Search via Noisy Paired ComparisonsGregory Canal, Andy Massimino, Mark Davenport, Christopher Rozellhttps://github.com/siplab-gt/pairsearchhttp://proceedings.mlr.press/v97/canal19a.html
Dynamic Measurement Scheduling for Event Forecasting using Deep RLChun-Hao Chang, Mingjie Mai, Anna Goldenberghttps://github.com/zzzace2000/autodiagnosishttp://proceedings.mlr.press/v97/chang19a.html
On Symmetric Losses for Learning from Corrupted LabelsNontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyamahttps://github.com/nolfwin/symloss-ber-auchttp://proceedings.mlr.press/v97/charoenphakdee19a.html
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency RatesGeorge Chenhttps://github.com/georgehc/npsurvivalhttp://proceedings.mlr.press/v97/chen19a.html
Stein Point Markov Chain Monte CarloWilson Ye Chen, Alessandro Barp, Francois-Xavier Briol, Jackson Gorham, Mark Girolami, Lester Mackey, Chris Oateshttps://github.com/wilson-ye-chen/sp-mcmchttp://proceedings.mlr.press/v97/chen19b.html
Particle Flow Bayes� RuleXinshi Chen, Hanjun Dai, Le Songhttps://github.com/xinshi-chen/ParticleFlowBayesRulehttp://proceedings.mlr.press/v97/chen19c.html
Proportionally Fair ClusteringXingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagalahttps://github.com/DukeXY/Proportionally-Fair-Clusteringhttp://proceedings.mlr.press/v97/chen19d.html
Generative Adversarial User Model for Reinforcement Learning Based Recommendation SystemXinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Songhttps://github.com/xinshi-chen/GenerativeAdversarialUserModelhttp://proceedings.mlr.press/v97/chen19f.html
Understanding and Utilizing Deep Neural Networks Trained with Noisy LabelsPengfei Chen, Ben Ben Liao, Guangyong Chen, Shengyu Zhanghttps://github.com/chenpf1025/noisy_label_understanding_utilizinghttp://proceedings.mlr.press/v97/chen19g.html
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein MinimizationYucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Penghttps://github.com/chen0706/EWMhttp://proceedings.mlr.press/v97/chen19h.html
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain AdaptationXinyang Chen, Sinan Wang, Mingsheng Long, Jianmin Wanghttps://github.com/github.com/thuml/Batch-Spectral-Penalizationhttp://proceedings.mlr.press/v97/chen19i.html
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and ApplicationsPin-Yu Chen, Lingfei Wu, Sijia Liu, Indika Rajapaksehttps://github.com/pinyuchen/FINGERhttp://proceedings.mlr.press/v97/chen19j.html
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution MatchingZiliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan, Guanbin Li, Liang Linhttps://github.com/MintYiqingchen/MMI-ALIhttp://proceedings.mlr.press/v97/chen19l.html
Robust Decision Trees Against Adversarial ExamplesHongge Chen, Huan Zhang, Duane Boning, Cho-Jui Hsiehhttps://github.com/chenhongge/RobustTreeshttp://proceedings.mlr.press/v97/chen19m.html
RaFM: Rank-Aware Factorization MachinesXiaoshuang Chen, Yin Zheng, Jiaxing Wang, Wenye Ma, Junzhou Huanghttps://github.com/cxsmarkchan/RaFMhttp://proceedings.mlr.press/v97/chen19n.html
Control Regularization for Reduced Variance Reinforcement LearningRichard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdickhttps://github.com/rcheng805/CORE-RLhttp://proceedings.mlr.press/v97/cheng19a.html
Predictor-Corrector Policy OptimizationChing-An Cheng, Xinyan Yan, Nathan Ratliff, Byron Bootshttps://github.com/gtrll/rlfamilyhttp://proceedings.mlr.press/v97/cheng19b.html
Variational Inference for sparse network reconstruction from count dataJulien Chiquet, Stephane Robin, Mahendra Mariadassouhttps://github.com/jchiquet/PLNmodels/http://proceedings.mlr.press/v97/chiquet19a.html
Random Walks on Hypergraphs with Edge-Dependent Vertex WeightsUthsav Chitra, Benjamin Raphaelhttps://github.com/uthsavc/hypergraph-halo-rankinghttp://proceedings.mlr.press/v97/chitra19a.html
Neural Joint Source-Channel CodingKristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermonhttps://github.com/ermongroup/necsthttp://proceedings.mlr.press/v97/choi19a.html
Beyond Backprop: Online Alternating Minimization with Auxiliary VariablesAnna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffoufhttps://github.com/irinarish/StochasticAMhttp://proceedings.mlr.press/v97/choromanska19a.html
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive SummarizationEric Chu, Peter Liuhttps://github.com/sosuperic/MeanSumhttp://proceedings.mlr.press/v97/chu19b.html
New results on information theoretic clusteringFerdinando Cicalese, Eduardo Laber, Lucas Murtinhohttps://github.com/lmurtinho/RatioGreedyClustering/tree/ICML_submissionhttp://proceedings.mlr.press/v97/cicalese19a.html
Quantifying Generalization in Reinforcement LearningKarl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, John Schulmanhttps://github.com/openai/coinrunhttp://proceedings.mlr.press/v97/cobbe19a.html
Certified Adversarial Robustness via Randomized SmoothingJeremy Cohen, Elan Rosenfeld, Zico Kolterhttp://github.com/locuslab/smoothinghttp://proceedings.mlr.press/v97/cohen19c.html
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement LearningC�dric Colas, Pierre-Yves Oudeyer, Olivier Sigaud, Pierre Fournier, Mohamed Chetouanihttps://github.com/flowersteam/curioushttp://proceedings.mlr.press/v97/colas19a.html
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large DatasetsRob Cornish, Paul Vanetti, Alexandre Bouchard-Cote, George Deligiannidis, Arnaud Doucethttps://github.com/12qu/smhhttp://proceedings.mlr.press/v97/cornish19a.html
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent ConstraintsAndrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil Youhttps://github.com/google-research/tensorflow_constrained_optimizationhttp://proceedings.mlr.press/v97/cotter19b.html
Monge blunts Bayes: Hardness Results for Adversarial TrainingZac Cranko, Aditya Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walderhttps://gitlab.com/machlearn/monge_image_examplehttp://proceedings.mlr.press/v97/cranko19a.html
Boosted Density Estimation RemasteredZac Cranko, Richard Nockhttps://github.com/ZacCranko/BoostedDensities.jlhttp://proceedings.mlr.press/v97/cranko19b.html
Matrix-Free Preconditioning in Online LearningAshok Cutkosky, Tamas Sarloshttps://github.com/google-research/google-research/tree/master/recursive_optimizerhttp://proceedings.mlr.press/v97/cutkosky19b.html
Minimal Achievable Sufficient Statistic LearningMilan Cvitkovic, G�nther Kolianderhttps://github.com/mwcvitkovic/MASS-Learninghttp://proceedings.mlr.press/v97/cvitkovic19a.html
Open Vocabulary Learning on Source Code with a Graph-Structured CacheMilan Cvitkovic, Badal Singh, Animashree Anandkumarhttps://github.com/mwcvitkovic/Open-Vocabulary-Learning-on-Source-Code-with-a-Graph-Structured-Cachehttp://proceedings.mlr.press/v97/cvitkovic19b.html
The Value Function Polytope in Reinforcement LearningRobert Dadashi, Marc G. Bellemare, Adrien Ali Taiga, Nicolas Le Roux, Dale Schuurmanshttps://github.com/google-research/google-research/tree/master/value_function_polytopehttp://proceedings.mlr.press/v97/dadashi19a.html
Bayesian Optimization Meets Bayesian Optimal StoppingZhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillethttps://github.com/daizhongxiang/Bayesian-Optimization-Meets-Bayesian-Optimal-Stoppinghttp://proceedings.mlr.press/v97/dai19a.html
Learning Fast Algorithms for Linear Transforms Using Butterfly FactorizationsTri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Rehttps://github.com/HazyResearch/learning-circuitshttp://proceedings.mlr.press/v97/dao19a.html
A Kernel Theory of Modern Data AugmentationTri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Rehttps://github.com/HazyResearch/augmentation_codehttp://proceedings.mlr.press/v97/dao19b.html
TarMAC: Targeted Multi-Agent CommunicationAbhishek Das, Th�ophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineauhttps://github.com/facebookresearch/tarmachttp://proceedings.mlr.press/v97/das19a.html
Stochastic Deep NetworksGwendoline De Bie, Gabriel Peyr�, Marco Cuturihttps://github.com/gdebie/stochastic-deep-networkshttp://proceedings.mlr.press/v97/de-bie19a.html
Learning-to-Learn Stochastic Gradient Descent with Biased RegularizationGiulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontilhttps://github.com/prolearner/onlineLTLhttp://proceedings.mlr.press/v97/denevi19a.html
A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer BiologyOnur Dereli, Ceyda Oguz, Mehmet G�nenhttps://github.com/mehmetgonen/path2msurvhttp://proceedings.mlr.press/v97/dereli19a.html
Learning to Convolve: A Generalized Weight-Tying ApproachNichita Diaconu, Daniel Worrallhttps://github.com/NichitaDiaconu/Learning-to-Convolvehttp://proceedings.mlr.press/v97/diaconu19a.html
Sever: A Robust Meta-Algorithm for Stochastic OptimizationIlias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewarthttps://github.com/hoonose/severhttp://proceedings.mlr.press/v97/diakonikolas19a.html
Approximated Oracle Filter Pruning for Destructive CNN Width OptimizationXiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han, Chenggang Yanhttps://github.com/ShawnDing1994/AOFPhttp://proceedings.mlr.press/v97/ding19a.html
Noisy Dual Principal Component PursuitTianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Daniel Robinson, Manolis Tsakiris, Rene Vidalhttps://github.com/tding1/Noisy-DPCPhttp://proceedings.mlr.press/v97/ding19b.html
Trajectory-Based Off-Policy Deep Reinforcement LearningAndreas Doerr, Michael Volpp, Marc Toussaint, Trimpe Sebastian, Christian Danielhttps://github.com/boschresearch/DD_OPGhttp://proceedings.mlr.press/v97/doerr19a.html
Generalized No Free Lunch Theorem for Adversarial RobustnessElvis Dohmatobhttps://github.com/dohmatob/StrongNoFreeLunchForARhttp://proceedings.mlr.press/v97/dohmatob19a.html
Provably efficient RL with Rich Observations via Latent State DecodingSimon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, John Langfordhttps://github.com/Microsoft/StateDecodinghttp://proceedings.mlr.press/v97/du19b.html
Optimal Auctions through Deep LearningPaul Duetting, Zhe Feng, Harikrishna Narasimhan, David Parkes, Sai Srivatsa Ravindranathhttps://github.com/saisrivatsan/deep-opt-auctionshttp://proceedings.mlr.press/v97/duetting19a.html
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Contextual Multi-armed Bandit Algorithm for Semiparametric Reward ModelGi-Soo Kim, Myunghee Cho Paikhttps://github.com/gisoo1989/semiparametric-MABhttp://proceedings.mlr.press/v97/kim19d.html
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Training Neural Networks with Local Error SignalsArild N�kland, Lars Hiller Eidneshttps://github.com/anokland/local-losshttp://proceedings.mlr.press/v97/nokland19a.html
Remember and Forget for Experience ReplayGuido Novati, Petros Koumoutsakoshttps://github.com/cselab/smartieshttp://proceedings.mlr.press/v97/novati19a.html
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Domain Agnostic Learning with Disentangled RepresentationsXingchao Peng, Zijun Huang, Ximeng Sun, Kate Saenkohttps://github.com/VisionLearningGroup/DALhttp://proceedings.mlr.press/v97/peng19b.html
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Moment-Based Variational Inference for Markov Jump ProcessesChristian Wildner, Heinz Koepplhttps://github.com/saevus1991/mbvihttp://proceedings.mlr.press/v97/wildner19a.html
End-to-End Probabilistic Inference for Nonstationary Audio AnalysisWilliam Wilkinson, Michael Andersen, Joshua D. Reiss, Dan Stowell, Arno Solinhttps://github.com/AaltoML/nonstationary-audio-gphttp://proceedings.mlr.press/v97/wilkinson19a.html
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian ComputationSamuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsenhttps://github.com/SamuelWiqvist/PENs-and-ABChttp://proceedings.mlr.press/v97/wiqvist19a.html
Wasserstein Adversarial Examples via Projected Sinkhorn IterationsEric Wong, Frank Schmidt, Zico Kolterhttps://github.com/locuslab/projected_sinkhornhttp://proceedings.mlr.press/v97/wong19a.html
Imitation Learning from Imperfect DemonstrationYueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyamahttps://github.com/kristery/Imitation-Learning-from-Imperfect-Demonstrationhttp://proceedings.mlr.press/v97/wu19a.html
Learning a Compressed Sensing Measurement Matrix via Gradient UnrollingShanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumarhttps://github.com/wushanshan/L1AEhttp://proceedings.mlr.press/v97/wu19b.html
Heterogeneous Model Reuse via Optimizing Multiparty Multiclass MarginXi-Zhu Wu, Song Liu, Zhi-Hua Zhouhttps://github.com/YuriWu/HMR/http://proceedings.mlr.press/v97/wu19c.html
Deep Compressed SensingYan Wu, Mihaela Rosca, Timothy Lillicraphttps://github.com/deepmind/deep-compressed-sensinghttp://proceedings.mlr.press/v97/wu19d.html
Simplifying Graph Convolutional NetworksFelix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, Kilian Weinbergerhttps://github.com/Tiiiger/SGChttp://proceedings.mlr.press/v97/wu19e.html
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-toleranceCong Xie, Sanmi Koyejo, Indranil Guptahttps://github.com/xcgoner/icml2019_zenohttp://proceedings.mlr.press/v97/xie19b.html
Differentiable Linearized ADMMXingyu Xie, Jianlong Wu, Guangcan Liu, Zhisheng Zhong, Zhouchen Linhttps://github.com/zzs1994/D-LADMMhttp://proceedings.mlr.press/v97/xie19c.html
Calibrated Approximate Bayesian InferenceHanwen Xing, Geoff Nicholls, Jeong Leehttps://github.com/hwxing3259/coverage_exampleshttp://proceedings.mlr.press/v97/xing19a.html
Power k-Means ClusteringJason Xu, Kenneth Langehttps://github.com/jasonxu90/powermeanshttp://proceedings.mlr.press/v97/xu19a.html
Gromov-Wasserstein Learning for Graph Matching and Node EmbeddingHongteng Xu, Dixin Luo, Hongyuan Zha, Lawrence Carin Dukehttps://github.com/HongtengXu/gwlhttp://proceedings.mlr.press/v97/xu19b.html
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic ConvergenceYi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yanghttps://github.com/yxu71/SSDChttp://proceedings.mlr.press/v97/xu19c.html
Variational Russian Roulette for Deep Bayesian NonparametricsKai Xu, Akash Srivastava, Charles Suttonhttps://github.com/xukai92/RAVE.jlhttp://proceedings.mlr.press/v97/xu19e.html
Supervised Hierarchical Clustering with Exponential LinkageNishant Yadav, Ari Kobren, Nicholas Monath, Andrew Mccallumhttps://github.com/iesl/expLinkagehttp://proceedings.mlr.press/v97/yadav19a.html
Learning to Prove Theorems via Interacting with Proof AssistantsKaiyu Yang, Jia Denghttps://github.com/princeton-vl/CoqGymhttp://proceedings.mlr.press/v97/yang19a.html
LegoNet: Efficient Convolutional Neural Networks with Lego FiltersZhaohui Yang, Yunhe Wang, Chuanjian Liu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xuhttps://github.com/zhaohui-yang/LegoNet_pytorchhttp://proceedings.mlr.press/v97/yang19c.html
SWALP : Stochastic Weight Averaging in Low Precision TrainingGuandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Chris De Sahttps://github.com/stevenygd/SWALPhttp://proceedings.mlr.press/v97/yang19d.html
ME-Net: Towards Effective Adversarial Robustness with Matrix EstimationYuzhe Yang, Guo Zhang, Zhi Xu, Dina Katabihttps://github.com/YyzHarry/ME-Nethttp://proceedings.mlr.press/v97/yang19e.html
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Hierarchically Structured Meta-learningHuaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Lihttps://github.com/huaxiuyao/hsml_2019http://proceedings.mlr.press/v97/yao19b.html
Rademacher Complexity for Adversarially Robust GeneralizationDong Yin, Ramchandran Kannan, Peter Bartletthttps://github.com/dongyin92/adversarially-robust-generalizationhttp://proceedings.mlr.press/v97/yin19b.html
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical VariablesMingzhang Yin, Yuguang Yue, Mingyuan Zhouhttps://github.com/ARM-gradient/ARSMhttp://proceedings.mlr.press/v97/yin19c.html
NAS-Bench-101: Towards Reproducible Neural Architecture SearchChris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutterhttps://github.com/google-research/nasbenchhttp://proceedings.mlr.press/v97/ying19a.html
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot LearningSung Whan Yoon, Jun Seo, Jaekyun Moonhttps://github.com/istarjun/TapNethttp://proceedings.mlr.press/v97/yoon19a.html
Towards Accurate Model Selection in Deep Unsupervised Domain AdaptationKaichao You, Ximei Wang, Mingsheng Long, Michael Jordanhttps://github.com/thuml/Deep-Embedded-Validationhttp://proceedings.mlr.press/v97/you19a.html
Position-aware Graph Neural NetworksJiaxuan You, Rex Ying, Jure Leskovechttps://github.com/JiaxuanYou/P-GNNhttp://proceedings.mlr.press/v97/you19b.html
DAG-GNN: DAG Structure Learning with Graph Neural NetworksYue Yu, Jie Chen, Tian Gao, Mo Yuhttps://github.com/fishmoon1234/DAG-GNNhttp://proceedings.mlr.press/v97/yu19a.html
Multi-Agent Adversarial Inverse Reinforcement LearningLantao Yu, Jiaming Song, Stefano Ermonhttps://github.com/ermongroup/MA-AIRLhttp://proceedings.mlr.press/v97/yu19e.html
Online Adaptive Principal Component Analysis and Its extensionsJianjun Yuan, Andrew Lamperskihttps://github.com/yuanx270/online-adaptive-PCAhttp://proceedings.mlr.press/v97/yuan19a.html
Generative Modeling of Infinite Occluded Objects for Compositional Scene RepresentationJinyang Yuan, Bin Li, Xiangyang Xuehttps://github.com/jinyangyuan/infinite-occluded-objectshttp://proceedings.mlr.press/v97/yuan19b.html
Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep LearningJihun Yun, Peng Zheng, Eunho Yang, Aurelie Lozano, Aleksandr Aravkinhttps://github.com/abcdxyzpqrst/Trimmed_Penaltyhttp://proceedings.mlr.press/v97/yun19a.html
Bayesian Nonparametric Federated Learning of Neural NetworksMikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, Yasaman Khazaenihttps://github.com/IBM/probabilistic-federated-neural-matchinghttp://proceedings.mlr.press/v97/yurochkin19a.html
Dirichlet Simplex Nest and Geometric InferenceMikhail Yurochkin, Aritra Guha, Yuekai Sun, Xuanlong Nguyenhttps://github.com/moonfolk/VLADhttp://proceedings.mlr.press/v97/yurochkin19b.html
Global Convergence of Block Coordinate Descent in Deep LearningJinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yaohttps://github.com/timlautk/BCD-for-DNNs-PyTorchhttp://proceedings.mlr.press/v97/zeng19a.html
Making Convolutional Networks Shift-Invariant AgainRichard Zhanghttps://richzhang.github.io/antialiased-cnns/http://proceedings.mlr.press/v97/zhang19a.html
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit FeedbackChicheng Zhang, Alekh Agarwal, Hal Daum� Iii, John Langford, Sahand Negahbanhttps://github.com/zcc1307/warmcb_scriptshttp://proceedings.mlr.press/v97/zhang19b.html
Self-Attention Generative Adversarial NetworksHan Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odenahttps://github.com/brain-research/self-attention-ganhttp://proceedings.mlr.press/v97/zhang19d.html
Circuit-GNN: Graph Neural Networks for Distributed Circuit DesignGuo Zhang, Hao He, Dina Katabihttps://github.com/hehaodele/circuit-gnnhttp://proceedings.mlr.press/v97/zhang19e.html
LatentGNN: Learning Efficient Non-local Relations for Visual RecognitionSongyang Zhang, Xuming He, Shipeng Yanhttps://github.com/latentgnn/LatentGNN-V1-PyTorchhttp://proceedings.mlr.press/v97/zhang19f.html
Bridging Theory and Algorithm for Domain AdaptationYuchen Zhang, Tianle Liu, Mingsheng Long, Michael Jordanhttp://github.com/thuml/MDDhttp://proceedings.mlr.press/v97/zhang19i.html
Random Function Priors for Correlation ModelingAonan Zhang, John Paisleyhttps://github.com/zan12/prmehttp://proceedings.mlr.press/v97/zhang19k.html
Co-Representation Network for Generalized Zero-Shot LearningFei Zhang, Guangming Shihttps://github.com/Fezaries/CRnethttp://proceedings.mlr.press/v97/zhang19l.html
SOLAR: Deep Structured Representations for Model-Based Reinforcement LearningMarvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, Sergey Levinehttps://github.com/sharadmv/parasolhttp://proceedings.mlr.press/v97/zhang19m.html
Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial ModelsChenyang Zhang, Guosheng Yinhttps://github.com/CYZhangHKU/Stable-Weaverhttp://proceedings.mlr.press/v97/zhang19o.html
Theoretically Principled Trade-off between Robustness and AccuracyHongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, Michael Jordanhttps://github.com/yaodongyu/TRADEShttp://proceedings.mlr.press/v97/zhang19p.html
Learning Novel Policies For TasksYunbo Zhang, Wenhao Yu, Greg Turkhttps://github.com/DragonMyth/TNB_Codehttp://proceedings.mlr.press/v97/zhang19q.html
Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix FactorizationKai Zhang, Sheng Zhang, Jun Liu, Jun Wang, Jie Zhanghttps://github.com/kzhang980/ORNMFhttp://proceedings.mlr.press/v97/zhang19r.html
Interpreting Adversarially Trained Convolutional Neural NetworksTianyuan Zhang, Zhanxing Zhuhttps://github.com/PKUAI26/AT-CNNhttp://proceedings.mlr.press/v97/zhang19s.html
Adaptive Monte Carlo Multiple Testing via Multi-Armed BanditsMartin Zhang, James Zou, David Tsehttps://github.com/martinjzhang/AMThttp://proceedings.mlr.press/v97/zhang19t.html
On Learning Invariant Representations for Domain AdaptationHan Zhao, Remi Tachet Des Combes, Kun Zhang, Geoffrey Gordonhttps://github.com/KeiraZhao/On-Learning-Invariant-Representations-for-Domain-Adaptation.githttp://proceedings.mlr.press/v97/zhao19a.html
Metric-Optimized Example WeightsSen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya Guptahttps://github.com/google-research/google-research/tree/master/moewhttp://proceedings.mlr.press/v97/zhao19b.html
Improving Neural Network Quantization without Retraining using Outlier Channel SplittingRitchie Zhao, Yuwei Hu, Jordan Dotzel, Chris De Sa, Zhiru Zhanghttps://github.com/cornell-zhang/dnn-quant-ocshttp://proceedings.mlr.press/v97/zhao19c.html
Maximum Entropy-Regularized Multi-Goal Reinforcement LearningRui Zhao, Xudong Sun, Volker Tresphttps://github.com/ruizhaogit/mep.githttp://proceedings.mlr.press/v97/zhao19d.html
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity OptimizationBaojian Zhou, Feng Chen, Yiming Yinghttps://github.com/baojianzhou/graph-sto-ihthttp://proceedings.mlr.press/v97/zhou19a.html
Lipschitz Generative Adversarial NetsZhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhanghttps://github.com/ZhimingZhou/Lipschitz_GANs_Codehttp://proceedings.mlr.press/v97/zhou19c.html
BayesNAS: A Bayesian Approach for Neural Architecture SearchHongpeng Zhou, Minghao Yang, Jun Wang, Wei Panhttps://github.com/BayesNAShttp://proceedings.mlr.press/v97/zhou19e.html
Transferable Clean-Label Poisoning Attacks on Deep Neural NetsChen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldsteinhttps://github.com/zhuchen03/ConvexPolytopePosioning.githttp://proceedings.mlr.press/v97/zhu19a.html
Improved Dynamic Graph Learning through Fault-Tolerant SparsificationChunjiang Zhu, Sabine Storandt, Kam-Yiu Lam, Song Han, Jinbo Bihttps://github.com/chunjiangzhu/ftshttp://proceedings.mlr.press/v97/zhu19b.html
Poission Subsampled R�nyi Differential PrivacyYuqing Zhu, Yu-Xiang Wanghttps://github.com/yuxiangw/autodphttp://proceedings.mlr.press/v97/zhu19c.html
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization EffectsZhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Mahttps://github.com/uuujf/SGDNoisehttp://proceedings.mlr.press/v97/zhu19e.html
Latent Normalizing Flows for Discrete SequencesZachary Ziegler, Alexander Rushhttps://github.com/harvardnlp/TextFlowhttp://proceedings.mlr.press/v97/ziegler19a.html
Beating Stochastic and Adversarial Semi-bandits Optimally and SimultaneouslyJulian Zimmert, Haipeng Luo, Chen-Yu Weihttps://github.com/diku-dk/CombSemiBanditshttp://proceedings.mlr.press/v97/zimmert19a.html
Fast Context Adaptation via Meta-LearningLuisa Zintgraf, Kyriacos Shiarli, Vitaly Kurin, Katja Hofmann, Shimon Whitesonhttps://github.com/lmzintgraf/caviahttp://proceedings.mlr.press/v97/zintgraf19a.html