【第27期】(第36屆) ICML2019 Accept-paper List(770篇)

來源：ICML會議

作者：圖靈助手

[1]. AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs.

作者: Gabriele Abbati,Philippe Wenk,Michael A. Osborne,Andreas Krause,Bernhard Schölkopf,Stefan Bauer,

連結: http://proceedings.mlr.press/v97/abbati19a.html

[2]. Dynamic Weights in Multi-Objective Deep Reinforcement Learning.

作者: Axel Abels,Diederik M. Roijers,Tom Lenaerts,Ann Nowé,Denis Steckelmacher,

連結: http://proceedings.mlr.press/v97/abels19a.html

[3]. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing.

作者: Sami Abu-El-Haija,Bryan Perozzi,Amol Kapoor,Nazanin Alipourfard,Kristina Lerman,Hrayr Harutyunyan,Greg Ver Steeg,Aram Galstyan,

連結: http://proceedings.mlr.press/v97/abu-el-haija19a.html

[4]. Communication-Constrained Inference and the Role of Shared Randomness.

作者: Jayadev Acharya,Clément L. Canonne,Himanshu Tyagi,

連結: http://proceedings.mlr.press/v97/acharya19a.html

[5]. Distributed Learning with Sublinear Communication.

作者: Jayadev Acharya,Chris De Sa,Dylan J. Foster,Karthik Sridharan,

連結: http://proceedings.mlr.press/v97/acharya19b.html

[6]. Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters.

作者: Jayadev Acharya,Ziteng Sun,

連結: http://proceedings.mlr.press/v97/acharya19c.html

[7]. Learning Models from Data with Measurement Error: Tackling Underreporting.

作者: Roy Adams,Yuelong Ji,Xiaobin Wang,Suchi Saria,

連結: http://proceedings.mlr.press/v97/adams19a.html

[8]. TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning.

作者: Tameem Adel,Adrian Weller,

連結: http://proceedings.mlr.press/v97/adel19a.html

[9]. PAC Learnability of Node Functions in Networked Dynamical Systems.

作者: Abhijin Adiga,Chris J. Kuhlman,Madhav Marathe,S. S. Ravi,Anil Vullikanti,

連結: http://proceedings.mlr.press/v97/adiga19a.html

[10]. Static Automatic Batching In TensorFlow.

作者: Ashish Agarwal,

連結: http://proceedings.mlr.press/v97/agarwal19a.html

[11]. Efficient Full-Matrix Adaptive Regularization.

作者: Naman Agarwal,Brian Bullins,Xinyi Chen,Elad Hazan,Karan Singh,Cyril Zhang,Yi Zhang,

連結: http://proceedings.mlr.press/v97/agarwal19b.html

[12]. Online Control with Adversarial Disturbances.

作者: Naman Agarwal,Brian Bullins,Elad Hazan,Sham M. Kakade,Karan Singh,

連結: http://proceedings.mlr.press/v97/agarwal19c.html

[13]. Fair Regression: Quantitative Definitions and Reduction-Based Algorithms.

作者: Alekh Agarwal,Miroslav Dudík,Zhiwei Steven Wu,

連結: http://proceedings.mlr.press/v97/agarwal19d.html

[14]. Learning to Generalize from Sparse and Underspecified Rewards.

作者: Rishabh Agarwal,Chen Liang,Dale Schuurmans,Mohammad Norouzi,

連結: http://proceedings.mlr.press/v97/agarwal19e.html

[15]. The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions.

作者: Raj Agrawal,Brian L. Trippe,Jonathan H. Huggins,Tamara Broderick,

連結: http://proceedings.mlr.press/v97/agrawal19a.html

[16]. Understanding the Impact of Entropy on Policy Optimization.

作者: Zafarali Ahmed,Nicolas Le Roux,Mohammad Norouzi,Dale Schuurmans,

連結: http://proceedings.mlr.press/v97/ahmed19a.html

[17]. Fairwashing: the risk of rationalization.

作者: Ulrich Aïvodji,Hiromi Arai,Olivier Fortineau,Sébastien Gambs,Satoshi Hara,Alain Tapp,

連結: http://proceedings.mlr.press/v97/aivodji19a.html

[18]. Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search.

作者: Youhei Akimoto,Shinichi Shirakawa,Nozomu Yoshinari,Kento Uchida,Shota Saito,Kouhei Nishida,

連結: http://proceedings.mlr.press/v97/akimoto19a.html

[19]. Projections for Approximate Policy Iteration Algorithms.

作者: Riad Akrour,Joni Pajarinen,Jan Peters,Gerhard Neumann,

連結: http://proceedings.mlr.press/v97/akrour19a.html

[20]. Validating Causal Inference Models via Influence Functions.

作者: Ahmed M. Alaa,Mihaela van der Schaar,

連結: http://proceedings.mlr.press/v97/alaa19a.html

[21]. Multi-objective training of Generative Adversarial Networks with multiple discriminators.

作者: Isabela Albuquerque,João Monteiro,Thang Doan,Breandan Considine,Tiago H. Falk,Ioannis Mitliagkas,

連結: http://proceedings.mlr.press/v97/albuquerque19a.html

[22]. Graph Element Networks: adaptive, structured computation and memory.

作者: Ferran Alet,Adarsh Keshav Jeewajee,Maria Bauzá Villalonga,Alberto Rodriguez,Tomás Lozano-Pérez,Leslie Pack Kaelbling,

連結: http://proceedings.mlr.press/v97/alet19a.html

[23]. Analogies Explained: Towards Understanding Word Embeddings.

作者: Carl Allen,Timothy M. Hospedales,

連結: http://proceedings.mlr.press/v97/allen19a.html

[24]. Infinite Mixture Prototypes for Few-shot Learning.

作者: Kelsey R. Allen,Evan Shelhamer,Hanul Shin,Joshua B. Tenenbaum,

連結: http://proceedings.mlr.press/v97/allen19b.html

[25]. A Convergence Theory for Deep Learning via Over-Parameterization.

作者: Zeyuan Allen-Zhu,Yuanzhi Li,Zhao Song,

連結: http://proceedings.mlr.press/v97/allen-zhu19a.html

[26]. Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation.

作者: Ahsan S. Alvi,Bin Xin Ru,Jan-Peter Calliess,Stephen J. Roberts,Michael A. Osborne,

連結: http://proceedings.mlr.press/v97/alvi19a.html

[27]. Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy.

作者: Kareem Amin,Alex Kulesza,Andres Muñoz Medina,Sergei Vassilvitskii,

連結: http://proceedings.mlr.press/v97/amin19a.html

[28]. Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation.

作者: Marco Ancona,Cengiz Öztireli,Markus H. Gross,

連結: http://proceedings.mlr.press/v97/ancona19a.html

[29]. Scaling Up Ordinal Embedding: A Landmark Approach.

作者: Jesse Anderton,Javed A. Aslam,

連結: http://proceedings.mlr.press/v97/anderton19a.html

[30]. Sorting Out Lipschitz Function Approximation.

作者: Cem Anil,James Lucas,Roger B. Grosse,

連結: http://proceedings.mlr.press/v97/anil19a.html

[31]. Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data.

作者: Luigi Antelmi,Nicholas Ayache,Philippe Robert,Marco Lorenzi,

連結: http://proceedings.mlr.press/v97/antelmi19a.html

[32]. Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks.

作者: Sanjeev Arora,Simon S. Du,Wei Hu,Zhiyuan Li,Ruosong Wang,

連結: http://proceedings.mlr.press/v97/arora19a.html

[33]. Distributed Weighted Matching via Randomized Composable Coresets.

作者: Sepehr Assadi,MohammadHossein Bateni,Vahab S. Mirrokni,

連結: http://proceedings.mlr.press/v97/assadi19a.html

[34]. Stochastic Gradient Push for Distributed Deep Learning.

作者: Mahmoud Assran,Nicolas Loizou,Nicolas Ballas,Michael Rabbat,

連結: http://proceedings.mlr.press/v97/assran19a.html

[35]. Bayesian Optimization of Composite Functions.

作者: Raul Astudillo,Peter I. Frazier,

連結: http://proceedings.mlr.press/v97/astudillo19a.html

[36]. Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA.

作者: Jordan Awan,Ana Kenney,Matthew Reimherr,Aleksandra B. Slavkovic,

連結: http://proceedings.mlr.press/v97/awan19a.html

[37]. Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data.

作者: Sergül Aydöre,Bertrand Thirion,Gaël Varoquaux,

連結: http://proceedings.mlr.press/v97/aydore19a.html

[38]. Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior.

作者: Fadhel Ayed,Juho Lee,Francois Caron,

連結: http://proceedings.mlr.press/v97/ayed19a.html

[39]. Scalable Fair Clustering.

作者: Arturs Backurs,Piotr Indyk,Krzysztof Onak,Baruch Schieber,Ali Vakilian,Tal Wagner,

連結: http://proceedings.mlr.press/v97/backurs19a.html

[40]. Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs.

作者: Yogesh Balaji,Hamed Hassani,Rama Chellappa,Soheil Feizi,

連結: http://proceedings.mlr.press/v97/balaji19a.html

[41]. Provable Guarantees for Gradient-Based Meta-Learning.

作者: Maria-Florina Balcan,Mikhail Khodak,Ameet Talwalkar,

連結: http://proceedings.mlr.press/v97/balcan19a.html

[42]. Open-ended learning in symmetric zero-sum games.

作者: David Balduzzi,Marta Garnelo,Yoram Bachrach,Wojciech Czarnecki,Julien Pérolat,Max Jaderberg,Thore Graepel,

連結: http://proceedings.mlr.press/v97/balduzzi19a.html

[43]. Concrete Autoencoders: Differentiable Feature Selection and Reconstruction.

作者: Muhammed Fatih Balin,Abubakar Abid,James Y. Zou,

連結: http://proceedings.mlr.press/v97/balin19a.html

[44]. HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving.

作者: Kshitij Bansal,Sarah M. Loos,Markus N. Rabe,Christian Szegedy,Stewart Wilcox,

連結: http://proceedings.mlr.press/v97/bansal19a.html

[45]. Structured agents for physical construction.

作者: Victor Bapst,Alvaro Sanchez-Gonzalez,Carl Doersch,Kimberly L. Stachenfeld,Pushmeet Kohli,Peter W. Battaglia,Jessica B. Hamrick,

連結: http://proceedings.mlr.press/v97/bapst19a.html

[46]. Learning to Route in Similarity Graphs.

作者: Dmitry Baranchuk,Dmitry Persiyanov,Anton Sinitsin,Artem Babenko,

連結: http://proceedings.mlr.press/v97/baranchuk19a.html

[47]. A Personalized Affective Memory Model for Improving Emotion Recognition.

作者: Pablo V. A. Barros,German Ignacio Parisi,Stefan Wermter,

連結: http://proceedings.mlr.press/v97/barros19a.html

[48]. Scale-free adaptive planning for deterministic dynamics & discounted rewards.

作者: Peter L. Bartlett,Victor Gabillon,Jennifer Healey,Michal Valko,

連結: http://proceedings.mlr.press/v97/bartlett19a.html

[49]. Pareto Optimal Streaming Unsupervised Classification.

作者: Soumya Basu,Steven Gutstein,Brent Lance,Sanjay Shakkottai,

連結: http://proceedings.mlr.press/v97/basu19a.html

[50]. Categorical Feature Compression via Submodular Optimization.

作者: MohammadHossein Bateni,Lin Chen,Hossein Esfandiari,Thomas Fu,Vahab S. Mirrokni,Afshin Rostamizadeh,

連結: http://proceedings.mlr.press/v97/bateni19a.html

[51]. Noise2Self: Blind Denoising by Self-Supervision.

作者: Joshua Batson,Loïc Royer,

連結: http://proceedings.mlr.press/v97/batson19a.html

[52]. Efficient optimization of loops and limits with randomized telescoping sums.

作者: Alex Beatson,Ryan P. Adams,

連結: http://proceedings.mlr.press/v97/beatson19a.html

[53]. Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces.

作者: Philipp Becker,Harit Pandya,Gregor H. W. Gebhardt,Cheng Zhao,C. James Taylor,Gerhard Neumann,

連結: http://proceedings.mlr.press/v97/becker19a.html

[54]. Switching Linear Dynamics for Variational Bayes Filtering.

作者: Philip Becker-Ehmck,Jan Peters,Patrick van der Smagt,

連結: http://proceedings.mlr.press/v97/becker-ehmck19a.html

[55]. Active Learning for Probabilistic Structured Prediction of Cuts and Matchings.

作者: Sima Behpour,Anqi Liu,Brian D. Ziebart,

連結: http://proceedings.mlr.press/v97/behpour19a.html

[56]. Invertible Residual Networks.

作者: Jens Behrmann,Will Grathwohl,Ricky T. Q. Chen,David Duvenaud,Jörn-Henrik Jacobsen,

連結: http://proceedings.mlr.press/v97/behrmann19a.html

[57]. Greedy Layerwise Learning Can Scale To ImageNet.

作者: Eugene Belilovsky,Michael Eickenberg,Edouard Oyallon,

連結: http://proceedings.mlr.press/v97/belilovsky19a.html

[58]. Overcoming Multi-model Forgetting.

作者: Yassine Benyahia,Kaicheng Yu,Kamil Bennani-Smires,Martin Jaggi,Anthony C. Davison,Mathieu Salzmann,Claudiu Musat,

連結: http://proceedings.mlr.press/v97/benyahia19a.html

[59]. Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning.

作者: Frederik Benzing,Marcelo Matheus Gauy,Asier Mujika,Anders Martinsson,Angelika Steger,

連結: http://proceedings.mlr.press/v97/benzing19a.html

[60]. Adversarially Learned Representations for Information Obfuscation and Inference.

作者: Martín Bertrán,Natalia Martínez,Afroditi Papadaki,Qiang Qiu,Miguel R. D. Rodrigues,Galen Reeves,Guillermo Sapiro,

連結: http://proceedings.mlr.press/v97/bertran19a.html

[61]. Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case.

作者: Alina Beygelzimer,Dávid Pál,Balázs Szörényi,Devanathan Thiruvenkatachari,Chen-Yu Wei,Chicheng Zhang,

連結: http://proceedings.mlr.press/v97/beygelzimer19a.html

[62]. Analyzing Federated Learning through an Adversarial Lens.

作者: Arjun Nitin Bhagoji,Supriyo Chakraborty,Prateek Mittal,Seraphin B. Calo,

連結: http://proceedings.mlr.press/v97/bhagoji19a.html

[63]. Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference.

作者: Yatao An Bian,Joachim M. Buhmann,Andreas Krause,

連結: http://proceedings.mlr.press/v97/bian19a.html

[64]. More Efficient Off-Policy Evaluation through Regularized Targeted Learning.

作者: Aurélien Bibaut,Ivana Malenica,Nikos Vlassis,Mark J. van der Laan,

連結: http://proceedings.mlr.press/v97/bibaut19a.html

[65]. A Kernel Perspective for Regularizing Deep Neural Networks.

作者: Alberto Bietti,Grégoire Mialon,Dexiong Chen,Julien Mairal,

連結: http://proceedings.mlr.press/v97/bietti19a.html

[66]. Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff.

作者: Yochai Blau,Tomer Michaeli,

連結: http://proceedings.mlr.press/v97/blau19a.html

[67]. Correlated bandits or: How to minimize mean-squared error online.

作者: Vinay Praneeth Boda,Prashanth L. A.,

連結: http://proceedings.mlr.press/v97/boda19a.html

[68]. Adversarial Attacks on Node Embeddings via Graph Poisoning.

作者: Aleksandar Bojchevski,Stephan Günnemann,

連結: http://proceedings.mlr.press/v97/bojchevski19a.html

[69]. Online Variance Reduction with Mixtures.

作者: Zalán Borsos,Sebastian Curi,Kfir Yehuda Levy,Andreas Krause,

連結: http://proceedings.mlr.press/v97/borsos19a.html

[70]. Compositional Fairness Constraints for Graph Embeddings.

作者: Avishek Joey Bose,William L. Hamilton,

連結: http://proceedings.mlr.press/v97/bose19a.html

[71]. Unreproducible Research is Reproducible.

作者: Xavier Bouthillier,César Laurent,Pascal Vincent,

連結: http://proceedings.mlr.press/v97/bouthillier19a.html

[72]. Blended Conditonal Gradients.

作者: Gábor Braun,Sebastian Pokutta,Dan Tu,Stephen Wright,

連結: http://proceedings.mlr.press/v97/braun19a.html

[73]. Coresets for Ordered Weighted Clustering.

作者: Vladimir Braverman,Shaofeng H.-C. Jiang,Robert Krauthgamer,Xuan Wu,

連結: http://proceedings.mlr.press/v97/braverman19a.html

[74]. Target Tracking for Contextual Bandits: Application to Demand Side Management.

作者: Margaux Brégère,Pierre Gaillard,Yannig Goude,Gilles Stoltz,

連結: http://proceedings.mlr.press/v97/bregere19a.html

[75]. Active Manifolds: A non-linear analogue to Active Subspaces.

作者: Robert A. Bridges,Anthony D. Gruber,Christopher Felder,Miki E. Verma,Chelsey Hoff,

連結: http://proceedings.mlr.press/v97/bridges19a.html

[76]. Conditioning by adaptive sampling for robust design.

作者: David H. Brookes,Hahnbeom Park,Jennifer Listgarten,

連結: http://proceedings.mlr.press/v97/brookes19a.html

[77]. Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations.

作者: Daniel S. Brown,Wonjoon Goo,Prabhat Nagarajan,Scott Niekum,

連結: http://proceedings.mlr.press/v97/brown19a.html

[78]. Deep Counterfactual Regret Minimization.

作者: Noam Brown,Adam Lerer,Sam Gross,Tuomas Sandholm,

連結: http://proceedings.mlr.press/v97/brown19b.html

[79]. Understanding the Origins of Bias in Word Embeddings.

作者: Marc-Etienne Brunet,Colleen Alkalay-Houlihan,Ashton Anderson,Richard S. Zemel,

連結: http://proceedings.mlr.press/v97/brunet19a.html

[80]. Low Latency Privacy Preserving Inference.

作者: Alon Brutzkus,Ran Gilad-Bachrach,Oren Elisha,

連結: http://proceedings.mlr.press/v97/brutzkus19a.html

[81]. Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem.

作者: Alon Brutzkus,Amir Globerson,

連結: http://proceedings.mlr.press/v97/brutzkus19b.html

[82]. Adversarial examples from computational constraints.

作者: Sébastien Bubeck,Yin Tat Lee,Eric Price,Ilya P. Razenshteyn,

連結: http://proceedings.mlr.press/v97/bubeck19a.html

[83]. Self-similar Epochs: Value in arrangement.

作者: Eliav Buchnik,Edith Cohen,Avinatan Hassidim,Yossi Matias,

連結: http://proceedings.mlr.press/v97/buchnik19a.html

[84]. Learning Generative Models across Incomparable Spaces.

作者: Charlotte Bunne,David Alvarez-Melis,Andreas Krause,Stefanie Jegelka,

連結: http://proceedings.mlr.press/v97/bunne19a.html

[85]. Rates of Convergence for Sparse Variational Gaussian Process Regression.

作者: David R. Burt,Carl Edward Rasmussen,Mark van der Wilk,

連結: http://proceedings.mlr.press/v97/burt19a.html

[86]. What is the Effect of Importance Weighting in Deep Learning?

作者: Jonathon Byrd,Zachary Chase Lipton,

連結: http://proceedings.mlr.press/v97/byrd19a.html

[87]. A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent.

作者: Yongqiang Cai,Qianxiao Li,Zuowei Shen,

連結: http://proceedings.mlr.press/v97/cai19a.html

[88]. Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances.

作者: Bugra Can,Mert Gürbüzbalaban,Lingjiong Zhu,

連結: http://proceedings.mlr.press/v97/can19a.html

[89]. Active Embedding Search via Noisy Paired Comparisons.

作者: Gregory Canal,Andrew K. Massimino,Mark A. Davenport,Christopher J. Rozell,

連結: http://proceedings.mlr.press/v97/canal19a.html

[90]. Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem.

作者: Junyu Cao,Wei Sun,

連結: http://proceedings.mlr.press/v97/cao19a.html

[91]. Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games.

作者: Adrian Rivera Cardoso,Jacob D. Abernethy,He Wang,Huan Xu,

連結: http://proceedings.mlr.press/v97/cardoso19a.html

[92]. Automated Model Selection with Bayesian Quadrature.

作者: Henry Chai,Jean-Francois Ton,Michael A. Osborne,Roman Garnett,

連結: http://proceedings.mlr.press/v97/chai19a.html

[93]. Learning Action Representations for Reinforcement Learning.

作者: Yash Chandak,Georgios Theocharous,James Kostas,Scott M. Jordan,Philip S. Thomas,

連結: http://proceedings.mlr.press/v97/chandak19a.html

[94]. Dynamic Measurement Scheduling for Event Forecasting using Deep RL.

作者: Chun-Hao Chang,Mingjie Mai,Anna Goldenberg,

連結: http://proceedings.mlr.press/v97/chang19a.html

[95]. On Symmetric Losses for Learning from Corrupted Labels.

作者: Nontawat Charoenphakdee,Jongyeong Lee,Masashi Sugiyama,

連結: http://proceedings.mlr.press/v97/charoenphakdee19a.html

[96]. Online learning with kernel losses.

作者: Niladri S. Chatterji,Aldo Pacchiano,Peter L. Bartlett,

連結: http://proceedings.mlr.press/v97/chatterji19a.html

[97]. Neural Network Attributions: A Causal Perspective.

作者: Aditya Chattopadhyay,Piyushi Manupriya,Anirban Sarkar,Vineeth N. Balasubramanian,

連結: http://proceedings.mlr.press/v97/chattopadhyay19a.html

[98]. PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits.

作者: Arghya Roy Chaudhuri,Shivaram Kalyanakrishnan,

連結: http://proceedings.mlr.press/v97/chaudhuri19a.html

[99]. Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates.

作者: George H. Chen,

連結: http://proceedings.mlr.press/v97/chen19a.html

[100]. Stein Point Markov Chain Monte Carlo.

作者: Wilson Ye Chen,Alessandro Barp,François-Xavier Briol,Jackson Gorham,Mark A. Girolami,Lester W. Mackey,Chris J. Oates,

連結: http://proceedings.mlr.press/v97/chen19b.html

[101]. Proportionally Fair Clustering.

作者: Xingyu Chen,Brandon Fain,Liang Lyu,Kamesh Munagala,

連結: http://proceedings.mlr.press/v97/chen19d.html

[102]. Information-Theoretic Considerations in Batch Reinforcement Learning.

作者: Jinglin Chen,Nan Jiang,

連結: http://proceedings.mlr.press/v97/chen19e.html

[103]. Generative Adversarial User Model for Reinforcement Learning Based Recommendation System.

作者: Xinshi Chen,Shuang Li,Hui Li,Shaohua Jiang,Yuan Qi,Le Song,

連結: http://proceedings.mlr.press/v97/chen19f.html

[104]. Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels.

作者: Pengfei Chen,Benben Liao,Guangyong Chen,Shengyu Zhang,

連結: http://proceedings.mlr.press/v97/chen19g.html

[105]. A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization.

作者: Yucheng Chen,Matus Telgarsky,Chao Zhang,Bolton Bailey,Daniel Hsu,Jian Peng,

連結: http://proceedings.mlr.press/v97/chen19h.html

[106]. Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation.

作者: Xinyang Chen,Sinan Wang,Mingsheng Long,Jianmin Wang,

連結: http://proceedings.mlr.press/v97/chen19i.html

[107]. Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications.

作者: Pin-Yu Chen,Lingfei Wu,Sijia Liu,Indika Rajapakse,

連結: http://proceedings.mlr.press/v97/chen19j.html

[108]. Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number.

作者: Zaiyi Chen,Yi Xu,Haoyuan Hu,Tianbao Yang,

連結: http://proceedings.mlr.press/v97/chen19k.html

[109]. Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching.

作者: Ziliang Chen,Zhanfu Yang,Xiaoxi Wang,Xiaodan Liang,Xiaopeng Yan,Guanbin Li,Liang Lin,

連結: http://proceedings.mlr.press/v97/chen19l.html

[110]. Robust Decision Trees Against Adversarial Examples.

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[115]. Random Walks on Hypergraphs with Edge-Dependent Vertex Weights.

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[117]. Beyond Backprop: Online Alternating Minimization with Auxiliary Variables.

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[118]. Unifying Orthogonal Monte Carlo Methods.

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[119]. Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning.

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[120]. MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization.

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[127]. Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models.

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[143]. Anytime Online-to-Batch, Optimism and Acceleration.

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[156]. A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology.

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[157]. Learning to Convolve: A Generalized Weight-Tying Approach.

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[159]. Approximated Oracle Filter Pruning for Destructive CNN Width Optimization.

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[162]. Trajectory-Based Off-Policy Deep Reinforcement Learning.

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[163]. Generalized No Free Lunch Theorem for Adversarial Robustness.

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[164]. Width Provably Matters in Optimization for Deep Linear Neural Networks.

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[167]. Incorporating Grouping Information into Bayesian Decision Tree Ensembles.

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[172]. Autoregressive Energy Machines.

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[174]. Imitating Latent Policies from Observation.

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[176]. GDPP: Learning Diverse Generations using Determinantal Point Processes.

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[177]. Sequential Facility Location: Approximate Submodularity and Greedy Algorithm.

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[180]. Cross-Domain 3D Equivariant Image Embeddings.

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[181]. On the Connection Between Adversarial Robustness and Saliency Map Interpretability.

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[182]. Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity.

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[183]. Multi-Frequency Vector Diffusion Maps.

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[185]. Regret Circuits: Composability of Regret Minimizers.

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[187]. Invariant-Equivariant Representation Learning for Multi-Class Data.

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[190]. Almost surely constrained convex optimization.

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[193]. Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning.

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[194]. Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap.

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[197]. Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN.

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[198]. Approximating Orthogonal Matrices with Effective Givens Factorization.

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[199]. Fast and Flexible Inference of Joint Distributions from their Marginals.

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[200]. Analyzing and Improving Representations with the Soft Nearest Neighbor Loss.

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[201]. Diagnosing Bottlenecks in Deep Q-learning Algorithms.

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[202]. MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement.

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[203]. Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio.

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[204]. Off-Policy Deep Reinforcement Learning without Exploration.

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[205]. Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation.

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[206]. Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities.

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[207]. Graph U-Nets.

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[209]. Rate Distortion For Model Compression: From Theory To Practice.

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[210]. Demystifying Dropout.

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[211]. Geometric Scattering for Graph Data Analysis.

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[214]. SelectiveNet: A Deep Neural Network with an Integrated Reject Option.

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[216]. DeepMDP: Learning Continuous Latent Space Models for Representation Learning.

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[217]. Partially Linear Additive Gaussian Graphical Models.

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[218]. Learning and Data Selection in Big Datasets.

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[219]. Improved Parallel Algorithms for Density-Based Network Clustering.

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[220]. Recursive Sketches for Modular Deep Learning.

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[221]. An Instability in Variational Inference for Topic Models.

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[222]. An Investigation into Neural Net Optimization via Hessian Eigenvalue Density.

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[223]. Data Shapley: Equitable Valuation of Data for Machine Learning.

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[225]. A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes.

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[226]. Learning to Groove with Inverse Sequence Transformations.

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[227]. Adversarial Examples Are a Natural Consequence of Test Error in Noise.

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[228]. Discovering Conditionally Salient Features with Statistical Guarantees.

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[229]. Estimating Information Flow in Deep Neural Networks.

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[230]. Amortized Monte Carlo Integration.

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[231]. Online Algorithms for Rent-Or-Buy with Expert Advice.

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[232]. The information-theoretic value of unlabeled data in semi-supervised learning.

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[233]. Efficient Training of BERT by Progressively Stacking.

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[234]. Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization.

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[235]. Obtaining Fairness using Optimal Transport Theory.

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[236]. Combining parametric and nonparametric models for off-policy evaluation.

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[237]. Counterfactual Visual Explanations.

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[238]. Adaptive Sensor Placement for Continuous Spaces.

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[239]. A Statistical Investigation of Long Memory in Language and Music.

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[241]. Learning to Optimize Multigrid PDE Solvers.

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[242]. Multi-Object Representation Learning with Iterative Variational Inference.

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[244]. Fast Algorithm for Generalized Multinomial Models with Ranking Data.

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[254]. Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization.

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[257]. Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI.

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[259]. Complexity of Linear Regions in Deep Networks.

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[264]. Per-Decision Option Discounting.

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[268]. Provably Efficient Maximum Entropy Exploration.

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[269]. On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning.

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[270]. Graph Resistance and Learning from Pairwise Comparisons.

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[271]. Using Pre-Training Can Improve Model Robustness and Uncertainty.

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[272]. Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design.

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[274]. Collective Model Fusion for Multiple Black-Box Experts.

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[277]. Emerging Convolutions for Generative Normalizing Flows.

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[278]. Nonconvex Variance Reduced Optimization with Arbitrary Sampling.

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[279]. Parameter-Efficient Transfer Learning for NLP.

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[280]. Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging.

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[281]. Finding Mixed Nash Equilibria of Generative Adversarial Networks.

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[282]. Classification from Positive, Unlabeled and Biased Negative Data.

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[283]. Bayesian Deconditional Kernel Mean Embeddings.

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[284]. Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization.

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[291]. Composing Entropic Policies using Divergence Correction.

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[294]. Learning Structured Decision Problems with Unawareness.

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[300]. Differentially Private Fair Learning.

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[303]. Learning What and Where to Transfer.

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[305]. A Deep Reinforcement Learning Perspective on Internet Congestion Control.

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[306]. Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance.

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[309]. Learning Discrete and Continuous Factors of Data via Alternating Disentanglement.

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[310]. Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization.

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[312]. Finding Options that Minimize Planning Time.

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[313]. Discovering Options for Exploration by Minimizing Cover Time.

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[314]. Kernel Mean Matching for Content Addressability of GANs.

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[315]. GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver.

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[316]. Bilinear Bandits with Low-rank Structure.

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[317]. Statistical Foundations of Virtual Democracy.

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[318]. Molecular Hypergraph Grammar with Its Application to Molecular Optimization.

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[320]. Classifying Treatment Responders Under Causal Effect Monotonicity.

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[323]. Differentially Private Learning of Geometric Concepts.

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[325]. Error Feedback Fixes SignSGD and other Gradient Compression Schemes.

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[327]. Neural Inverse Knitting: From Images to Manufacturing Instructions.

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[333]. CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network.

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[334]. Collaborative Evolutionary Reinforcement Learning.

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[427]. Data Poisoning Attacks in Multi-Party Learning.

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[428]. Traditional and Heavy Tailed Self Regularization in Neural Network Models.

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[429]. Curvature-Exploiting Acceleration of Elastic Net Computations.

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[430]. Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms.

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[431]. Calibrated Model-Based Deep Reinforcement Learning.

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[432]. Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems.

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[435]. Adversarial Generation of Time-Frequency Features with application in audio synthesis.

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[436]. On the Universality of Invariant Networks.

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[439]. Optimal Minimal Margin Maximization with Boosting.

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[442]. Distributional Reinforcement Learning for Efficient Exploration.

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[446]. Stochastic Blockmodels meet Graph Neural Networks.

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[447]. Imputing Missing Events in Continuous-Time Event Streams.

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[449]. The Wasserstein Transform.

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[454]. Reinforcement Learning in Configurable Continuous Environments.

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[456]. Optimality Implies Kernel Sum Classifiers are Statistically Efficient.

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[458]. Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography.

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[459]. Formal Privacy for Functional Data with Gaussian Perturbations.

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[463]. Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization.

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[530]. On the Spectral Bias of Neural Networks.

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[537]. Efficient On-Device Models using Neural Projections.

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[545]. A Persistent Weisfeiler-Lehman Procedure for Graph Classification.

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[552]. Iterative Linearized Control: Stable Algorithms and Complexity Guarantees.

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[562]. Breaking Inter-Layer Co-Adaptation by Classifier Anonymization.

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[563]. A Theoretical Analysis of Contrastive Unsupervised Representation Learning.

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[564]. Locally Private Bayesian Inference for Count Models.

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[566]. Discovering Context Effects from Raw Choice Data.

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[567]. On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference.

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[568]. Exploration Conscious Reinforcement Learning Revisited.

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[569]. Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data.

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[570]. Conditional Independence in Testing Bayesian Networks.

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[571]. Learning to Clear the Market.

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[572]. Hessian Aided Policy Gradient.

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[573]. Learning with Bad Training Data via Iterative Trimmed Loss Minimization.

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[575]. Scalable Training of Inference Networks for Gaussian-Process Models.

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[576]. Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning.

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[578]. Rehashing Kernel Evaluation in High Dimensions.

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[581]. Refined Complexity of PCA with Outliers.

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[583]. Non-Parametric Priors For Generative Adversarial Networks.

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[588]. QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning.

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[589]. Distribution calibration for regression.

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[655]. SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver.

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[656]. Improving Neural Language Modeling via Adversarial Training.

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[657]. EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis.

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[658]. Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models.

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[660]. State-Regularized Recurrent Neural Networks.

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[661]. Deep Factors for Forecasting.

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[662]. Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions.

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[663]. On Sparse Linear Regression in the Local Differential Privacy Model.

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[664]. Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random.

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[665]. On the Generalization Gap in Reparameterizable Reinforcement Learning.

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[666]. Bias Also Matters: Bias Attribution for Deep Neural Network Explanation.

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[667]. Jumpout : Improved Dropout for Deep Neural Networks with ReLUs.

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[668]. AdaGrad stepsizes: sharp convergence over nonconvex landscapes.

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[669]. Generalized Linear Rule Models.

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[672]. Non-Monotonic Sequential Text Generation.

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[673]. PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach.

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[674]. Learning deep kernels for exponential family densities.

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[675]. Improving Model Selection by Employing the Test Data.

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[676]. Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth.

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[749]. Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization.

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[751]. Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits.

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[753]. Metric-Optimized Example Weights.

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[754]. Improving Neural Network Quantization without Retraining using Outlier Channel Splitting.

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[755]. Maximum Entropy-Regularized Multi-Goal Reinforcement Learning.

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[756]. Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization.

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[757]. Lower Bounds for Smooth Nonconvex Finite-Sum Optimization.

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[758]. Lipschitz Generative Adversarial Nets.

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[759]. Toward Understanding the Importance of Noise in Training Neural Networks.

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[760]. BayesNAS: A Bayesian Approach for Neural Architecture Search.

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[761]. Transferable Clean-Label Poisoning Attacks on Deep Neural Nets.

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[762]. Improved Dynamic Graph Learning through Fault-Tolerant Sparsification.

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[763]. Poission Subsampled Rényi Differential Privacy.

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[764]. Learning Classifiers for Target Domain with Limited or No Labels.

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[765]. The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects.

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[766]. Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization.

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[767]. Latent Normalizing Flows for Discrete Sequences.

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[768]. Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously.

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[769]. Fast Context Adaptation via Meta-Learning.

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[770]. Natural Analysts in Adaptive Data Analysis.

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