AISTATS - RankMe

424 papers

Year	Title / Authors
2020	"Bring Your Own Greedy"+Max: Near-Optimal 1/2-Approximations for Submodular Knapsack. Grigory Yaroslavtsev, Samson Zhou, Dmitrii Avdiukhin
2020	A Characterization of Mean Squared Error for Estimator with Bagging. Martin Mihelich, Charles Dognin, Yan Shu, Michael Blot
2020	A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization. Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi
2020	A Deep Generative Model for Fragment-Based Molecule Generation. Marco Podda, Davide Bacciu, Alessio Micheli
2020	A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare
2020	A Diversity-aware Model for Majority Vote Ensemble Accuracy. Robert John Durrant, Nick Jin Sean Lim
2020	A Double Residual Compression Algorithm for Efficient Distributed Learning. Xiaorui Liu, Yao Li, Jiliang Tang, Ming Yan
2020	A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option. Pon Kumar Sharoff, Nishant A. Mehta, Ravi Ganti
2020	A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization. Zhize Li, Jian Li
2020	A Framework for Sample Efficient Interval Estimation with Control Variates. Shengjia Zhao, Christopher Yeh, Stefano Ermon
2020	A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning. Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk, Quoc Tran-Dinh
2020	A Linear-time Independence Criterion Based on a Finite Basis Approximation. Longfei Yan, W. Bastiaan Kleijn, Thushara D. Abhayapala
2020	A Locally Adaptive Bayesian Cubature Method. Matthew Fisher, Chris J. Oates, Catherine E. Powell, Aretha L. Teckentrup
2020	A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case. Maxime Laborde, Adam M. Oberman
2020	A Multiclass Classification Approach to Label Ranking. Robin Vogel, Stéphan Clémençon
2020	A Nonparametric Off-Policy Policy Gradient. Samuele Tosatto, João Carvalho, Hany Abdulsamad, Jan Peters
2020	A Novel Confidence-Based Algorithm for Structured Bandits. Andrea Tirinzoni, Alessandro Lazaric, Marcello Restelli
2020	A PTAS for the Bayesian Thresholding Bandit Problem. Yue Qin, Jian Peng, Yuan Zhou
2020	A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players. Abbas Mehrabian, Etienne Boursier, Emilie Kaufmann, Vianney Perchet
2020	A Primal-Dual Solver for Large-Scale Tracking-by-Assignment. Stefan Haller, Mangal Prakash, Lisa Hutschenreiter, Tobias Pietzsch, Carsten Rother, Florian Jug, Paul Swoboda, Bogdan Savchynskyy
2020	A Reduction from Reinforcement Learning to No-Regret Online Learning. Ching-An Cheng, Remi Tachet des Combes, Byron Boots, Geoffrey J. Gordon
2020	A Robust Univariate Mean Estimator is All You Need. Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
2020	A Rule for Gradient Estimator Selection, with an Application to Variational Inference. Tomas Geffner, Justin Domke
2020	A Simple Approach for Non-stationary Linear Bandits. Peng Zhao, Lijun Zhang, Yuan Jiang, Zhi-Hua Zhou
2020	A Stein Goodness-of-fit Test for Directional Distributions. Wenkai Xu, Takeru Matsuda
2020	A Theoretical Case Study of Structured Variational Inference for Community Detection. Mingzhang Yin, Y. X. Rachel Wang, Purnamrita Sarkar
2020	A Theoretical and Practical Framework for Regression and Classification from Truncated Samples. Andrew Ilyas, Emmanouil Zampetakis, Constantinos Daskalakis
2020	A Three Sample Hypothesis Test for Evaluating Generative Models. Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta
2020	A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games. Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel
2020	A Topology Layer for Machine Learning. Rickard Brüel Gabrielsson, Bradley J. Nelson, Anjan Dwaraknath, Primoz Skraba
2020	A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach. Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil
2020	A Unified Statistically Efficient Estimation Framework for Unnormalized Models. Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda
2020	A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth
2020	A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. Eduard Gorbunov, Filip Hanzely, Peter Richtárik
2020	A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models. Ziyu Wang, Shuyu Cheng, Yueru Li, Jun Zhu, Bo Zhang
2020	A nonasymptotic law of iterated logarithm for general M-estimators. Arnak S. Dalalyan, Nicolas Schreuder, Victor-Emmanuel Brunel
2020	A principled approach for generating adversarial images under non-smooth dissimilarity metrics. Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel, Adam M. Oberman
2020	A single algorithm for both restless and rested rotting bandits. Julien Seznec, Pierre Ménard, Alessandro Lazaric, Michal Valko
2020	AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. Ruqi Zhang, A. Feder Cooper, Christopher De Sa
2020	AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning. Rizal Fathony, J. Zico Kolter
2020	ASAP: Architecture Search, Anneal and Prune. Asaf Noy, Niv Nayman, Tal Ridnik, Nadav Zamir, Sivan Doveh, Itamar Friedman, Raja Giryes, Lihi Zelnik
2020	Accelerated Bayesian Optimisation through Weight-Prior Tuning. Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Cheng Li, Svetha Venkatesh, Laurence Park, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height, Teo Slezak
2020	Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. Dongruo Zhou, Yuan Cao, Quanquan Gu
2020	Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks. Jinming Xu, Ye Tian, Ying Sun, Gesualdo Scutari
2020	Accelerating Gradient Boosting Machines. Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni
2020	Accelerating Smooth Games by Manipulating Spectral Shapes. Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel
2020	Active Community Detection with Maximal Expected Model Change. Dan Kushnir, Benjamin Mirabelli
2020	Adaptive Discretization for Evaluation of Probabilistic Cost Functions. Christoph Zimmer, Danny Driess, Mona Meister, Duy Nguyen-Tuong
2020	Adaptive Exploration in Linear Contextual Bandit. Botao Hao, Tor Lattimore, Csaba Szepesvári
2020	Adaptive Online Kernel Sampling for Vertex Classification. Peng Yang, Ping Li
2020	Adaptive Trade-Offs in Off-Policy Learning. Mark Rowland, Will Dabney, Rémi Munos
2020	Adaptive multi-fidelity optimization with fast learning rates. Côme Fiegel, Victor Gabillon, Michal Valko
2020	Adaptive, Distribution-Free Prediction Intervals for Deep Networks. Danijel Kivaranovic, Kory D. Johnson, Hannes Leeb
2020	Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization. Xingchen Ma, Matthew B. Blaschko
2020	Adversarial Risk Bounds through Sparsity based Compression. Emilio Rafael Balda, Niklas Koep, Arash Behboodi, Rudolf Mathar
2020	Adversarial Robustness Guarantees for Classification with Gaussian Processes. Arno Blaas, Andrea Patane, Luca Laurenti, Luca Cardelli, Marta Kwiatkowska, Stephen J. Roberts
2020	Adversarial Robustness of Flow-Based Generative Models. Phillip Pope, Yogesh Balaji, Soheil Feizi
2020	Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2020	Alternating Minimization Converges Super-Linearly for Mixed Linear Regression. Avishek Ghosh, Kannan Ramchandran
2020	Amortized Inference of Variational Bounds for Learning Noisy-OR. Yiming Yan, Melissa Ailem, Fei Sha
2020	An Asymptotic Rate for the LASSO Loss. Cynthia Rush
2020	An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise. Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba
2020	An Inverse-free Truncated Rayleigh-Ritz Method for Sparse Generalized Eigenvalue Problem. Yunfeng Cai, Ping Li
2020	An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays. Julian Zimmert, Yevgeny Seldin
2020	An Optimal Algorithm for Bandit Convex Optimization with Strongly-Convex and Smooth Loss. Shinji Ito
2020	An approximate KLD based experimental design for models with intractable likelihoods. Ziqiao Ao, Jinglai Li
2020	Approximate Cross-Validation in High Dimensions with Guarantees. William T. Stephenson, Tamara Broderick
2020	Approximate Cross-validation: Guarantees for Model Assessment and Selection. Ashia C. Wilson, Maximilian Kasy, Lester Mackey
2020	Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. Lars Buesing, Nicolas Heess, Theophane Weber
2020	Approximate Inference with Wasserstein Gradient Flows. Charlie Frogner, Tomaso A. Poggio
2020	Assessing Local Generalization Capability in Deep Models. Huan Wang, Nitish Shirish Keskar, Caiming Xiong, Richard Socher
2020	Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms. Ping Ma, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney
2020	Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning. Ming Yin, Yu-Xiang Wang
2020	AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity. Yibo Zeng, Fei Feng, Wotao Yin
2020	Asynchronous Gibbs Sampling. Alexander Terenin, Daniel Simpson, David Draper
2020	Auditing ML Models for Individual Bias and Unfairness. Songkai Xue, Mikhail Yurochkin, Yuekai Sun
2020	Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. Théo Galy-Fajou, Florian Wenzel, Manfred Opper
2020	Automatic Differentiation of Sketched Regression. Hang Liao, Barak A. Pearlmutter, Vamsi K. Potluru, David P. Woodruff
2020	Automatic Differentiation of Some First-Order Methods in Parametric Optimization. Sheheryar Mehmood, Peter Ochs
2020	Balanced Off-Policy Evaluation in General Action Spaces. Arjun Sondhi, David Arbour, Drew Dimmery
2020	Balancing Learning Speed and Stability in Policy Gradient via Adaptive Exploration. Matteo Papini, Andrea Battistello, Marcello Restelli
2020	Bandit Convex Optimization in Non-stationary Environments. Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou
2020	Bandit optimisation of functions in the Matérn kernel RKHS. David Janz, David R. Burt, Javier González
2020	BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders. Kaspar Märtens, Christopher Yau
2020	Bayesian Image Classification with Deep Convolutional Gaussian Processes. Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman
2020	Bayesian Reinforcement Learning via Deep, Sparse Sampling. Divya Grover, Debabrota Basu, Christos Dimitrakakis
2020	Bayesian experimental design using regularized determinantal point processes. Michal Derezinski, Feynman T. Liang, Michael W. Mahoney
2020	Best-item Learning in Random Utility Models with Subset Choices. Aadirupa Saha, Aditya Gopalan
2020	Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer. Yanshuai Cao, Peng Xu
2020	Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness. Antônio H. Ribeiro, Koen Tiels, Luis Antonio Aguirre, Thomas B. Schön
2020	Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection. Vaggos Chatziafratis, Grigory Yaroslavtsev, Euiwoong Lee, Konstantin Makarychev, Sara Ahmadian, Alessandro Epasto, Mohammad Mahdian
2020	Black Box Submodular Maximization: Discrete and Continuous Settings. Lin Chen, Mingrui Zhang, Hamed Hassani, Amin Karbasi
2020	Black-Box Inference for Non-Linear Latent Force Models. Wil O. C. Ward, Tom Ryder, Dennis Prangle, Mauricio A. Álvarez
2020	Budget Learning via Bracketing. Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama
2020	Budget-Constrained Bandits over General Cost and Reward Distributions. Semih Cayci, Atilla Eryilmaz, R. Srikant
2020	Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation. Sangdon Park, Osbert Bastani, James Weimer, Insup Lee
2020	Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. Han Bao, Masashi Sugiyama
2020	Causal Bayesian Optimization. Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González
2020	Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method. Pengzhou Wu, Kenji Fukumizu
2020	Causal inference in degenerate systems: An impossibility result. Yue Wang, Linbo Wang
2020	Censored Quantile Regression Forest. Alexander Hanbo Li, Jelena Bradic
2020	Characterization of Overlap in Observational Studies. Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney
2020	ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2020	Choosing the Sample with Lowest Loss makes SGD Robust. Vatsal Shah, Xiaoxia Wu, Sujay Sanghavi
2020	Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls. Jiacheng Zhuo, Qi Lei, Alex Dimakis, Constantine Caramanis
2020	Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction. Boyue Li, Shicong Cen, Yuxin Chen, Yuejie Chi
2020	Competing Bandits in Matching Markets. Lydia T. Liu, Horia Mania, Michael I. Jordan
2020	Computing Tight Differential Privacy Guarantees Using FFT. Antti Koskela, Joonas Jälkö, Antti Honkela
2020	Conditional Importance Sampling for Off-Policy Learning. Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
2020	Conditional Linear Regression. Diego Calderon, Brendan Juba, Sirui Li, Zongyi Li, Lisa Ruan
2020	Conservative Exploration in Reinforcement Learning. Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta
2020	Constructing a provably adversarially-robust classifier from a high accuracy one. Grzegorz Gluch, Rüdiger L. Urbanke
2020	Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations. Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi
2020	Contextual Combinatorial Volatile Multi-armed Bandit with Adaptive Discretization. Andi Nika, Sepehr Elahi, Cem Tekin
2020	Contextual Constrained Learning for Dose-Finding Clinical Trials. Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela van der Schaar
2020	Contextual Online False Discovery Rate Control. Shiyun Chen, Shiva Prasad Kasiviswanathan
2020	Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling. Mojmir Mutny, Michal Derezinski, Andreas Krause
2020	Convergence Rates of Gradient Descent and MM Algorithms for Bradley-Terry Models. Milan Vojnovic, Se-Young Yun, Kaifang Zhou
2020	Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan
2020	Convex Geometry of Two-Layer ReLU Networks: Implicit Autoencoding and Interpretable Models. Tolga Ergen, Mert Pilanci
2020	Coping With Simulators That Don't Always Return. Andrew Warrington, Frank Wood, Saeid Naderiparizi
2020	Corruption-Tolerant Gaussian Process Bandit Optimization. Ilija Bogunovic, Andreas Krause, Jonathan Scarlett
2020	DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate. Saeed Soori, Konstantin Mishchenko, Aryan Mokhtari, Maryam Mehri Dehnavi, Mert Gürbüzbalaban
2020	DYNOTEARS: Structure Learning from Time-Series Data. Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, Bryon Aragam
2020	Data Generation for Neural Programming by Example. Judith Clymo, Adrià Gascón, Brooks Paige, Nathanaël Fijalkow, Haik Manukian
2020	Decentralized Multi-player Multi-armed Bandits with No Collision Information. Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang
2020	Decentralized gradient methods: does topology matter? Giovanni Neglia, Chuan Xu, Don Towsley, Gianmarco Calbi
2020	Deep Active Learning: Unified and Principled Method for Query and Training. Changjian Shui, Fan Zhou, Christian Gagné, Boyu Wang
2020	Deep Structured Mixtures of Gaussian Processes. Martin Trapp, Robert Peharz, Franz Pernkopf, Carl Edward Rasmussen
2020	Deontological Ethics By Monotonicity Shape Constraints. Serena Lutong Wang, Maya R. Gupta
2020	Dependent randomized rounding for clustering and partition systems with knapsack constraints. David G. Harris, Thomas W. Pensyl, Aravind Srinivasan, Khoa Trinh
2020	Derivative-Free & Order-Robust Optimisation. Haitham Ammar, Victor Gabillon, Rasul Tutunov, Michal Valko
2020	Deterministic Decoding for Discrete Data in Variational Autoencoders. Daniil Polykovskiy, Dmitry P. Vetrov
2020	Diameter-based Interactive Structure Discovery. Christopher Tosh, Daniel Hsu
2020	Differentiable Causal Backdoor Discovery. Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva
2020	Differentiable Feature Selection by Discrete Relaxation. Rishit Sheth, Nicoló Fusi
2020	Discrete Action On-Policy Learning with Action-Value Critic. Yuguang Yue, Yunhao Tang, Mingzhang Yin, Mingyuan Zhou
2020	Distributed, partially collapsed MCMC for Bayesian Nonparametrics. Kumar Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead Williamson
2020	Distributionally Robust Bayesian Optimization. Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause
2020	Distributionally Robust Bayesian Quadrature Optimization. Thanh Tang Nguyen, Sunil Gupta, Huong Ha, Santu Rana, Svetha Venkatesh
2020	Distributionally Robust Formulation and Model Selection for the Graphical Lasso. Pedro Cisneros-Velarde, Alexander Petersen, Sang-Yun Oh
2020	Domain-Liftability of Relational Marginal Polytopes. Ondrej Kuzelka, Yuyi Wang
2020	Doubly Sparse Variational Gaussian Processes. Vincent Adam, Stefanos Eleftheriadis, Artem Artemev, Nicolas Durrande, James Hensman
2020	Dynamic content based ranking. Seppo Virtanen, Mark Girolami
2020	Dynamical Systems Theory for Causal Inference with Application to Synthetic Control Methods. Yi Ding, Panos Toulis
2020	EM Converges for a Mixture of Many Linear Regressions. Jeongyeol Kwon, Constantine Caramanis
2020	Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác
2020	Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning. Tianyu Li, Bogdan Mazoure, Doina Precup, Guillaume Rabusseau
2020	Efficient Spectrum-Revealing CUR Matrix Decomposition. Cheng Chen, Ming Gu, Zhihua Zhang, Weinan Zhang, Yong Yu
2020	Elimination of All Bad Local Minima in Deep Learning. Kenji Kawaguchi, Leslie Pack Kaelbling
2020	Enriched mixtures of generalised Gaussian process experts. Charles W. L. Gadd, Sara Wade, Alexis Boukouvalas
2020	Ensemble Gaussian Processes with Spectral Features for Online Interactive Learning with Scalability. Qin Lu, Georgios Vasileios Karanikolas, Yanning Shen, Georgios B. Giannakis
2020	Entropy Weighted Power k-Means Clustering. Saptarshi Chakraborty, Debolina Paul, Swagatam Das, Jason Q. Xu
2020	Equalized odds postprocessing under imperfect group information. Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
2020	Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions. Kamiar Rahnama Rad, Wenda Zhou, Arian Maleki
2020	Explaining the Explainer: A First Theoretical Analysis of LIME. Damien Garreau, Ulrike von Luxburg
2020	Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation. Shuhang Chen, Adithya M. Devraj, Ana Busic, Sean P. Meyn
2020	Exploiting Categorical Structure Using Tree-Based Methods. Brian Lucena
2020	Expressiveness and Learning of Hidden Quantum Markov Models. Sandesh Adhikary, Siddarth Srinivasan, Geoffrey J. Gordon, Byron Boots
2020	Fair Correlation Clustering. Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian
2020	Fair Decisions Despite Imperfect Predictions. Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
2020	Fairness Evaluation in Presence of Biased Noisy Labels. Riccardo Fogliato, Alexandra Chouldechova, Max G'Sell
2020	Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter. Wenshuo Guo, Nhat Ho, Michael I. Jordan
2020	Fast Markov chain Monte Carlo algorithms via Lie groups. Steve Huntsman
2020	Fast Noise Removal for k-Means Clustering. Sungjin Im, Mahshid Montazer Qaem, Benjamin Moseley, Xiaorui Sun, Rudy Zhou
2020	Fast and Accurate Ranking Regression. Ilkay Yildiz, Jennifer G. Dy, Deniz Erdogmus, Jayashree Kalpathy-Cramer, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis
2020	Fast and Bayes-consistent nearest neighbors. Klim Efremenko, Aryeh Kontorovich, Moshe Noivirt
2020	Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. Si Yi Meng, Sharan Vaswani, Issam Hadj Laradji, Mark Schmidt, Simon Lacoste-Julien
2020	Feature relevance quantification in explainable AI: A causal problem. Dominik Janzing, Lenon Minorics, Patrick Blöbaum
2020	FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, Ramtin Pedarsani
2020	Federated Heavy Hitters Discovery with Differential Privacy. Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li
2020	Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training. Fangda Gu, Armin Askari, Laurent El Ghaoui
2020	Finite-Time Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation. Jun Sun, Gang Wang, Georgios B. Giannakis, Qinmin Yang, Zaiyue Yang
2020	Finite-Time Error Bounds for Biased Stochastic Approximation with Applications to Q-Learning. Gang Wang, Georgios B. Giannakis
2020	Fixed-confidence guarantees for Bayesian best-arm identification. Xuedong Shang, Rianne de Heide, Pierre Ménard, Emilie Kaufmann, Michal Valko
2020	Flexible distribution-free conditional predictive bands using density estimators. Rafael Izbicki, Gilson Y. Shimizu, Rafael Bassi Stern
2020	Formal Limitations on the Measurement of Mutual Information. David McAllester, Karl Stratos
2020	Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric
2020	Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs. Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi
2020	Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees. Atsushi Nitanda, Taiji Suzuki
2020	GAIT: A Geometric Approach to Information Theory. Jose Gallego-Posada, Ankit Vani, Max Schwarzer, Simon Lacoste-Julien
2020	GP-VAE: Deep Probabilistic Time Series Imputation. Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt
2020	Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling. Nicholas Sterge, Bharath K. Sriperumbudur, Lorenzo Rosasco, Alessandro Rudi
2020	Gaussian Sketching yields a J-L Lemma in RKHS. Samory Kpotufe, Bharath K. Sriperumbudur
2020	Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency. Ziv Goldfeld, Kristjan H. Greenewald
2020	Gaussianization Flows. Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon
2020	General Identification of Dynamic Treatment Regimes Under Interference. Eli Sherman, David Arbour, Ilya Shpitser
2020	Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks. Mingchen Li, Mahdi Soltanolkotabi, Samet Oymak
2020	Graph Coarsening with Preserved Spectral Properties. Yu Jin, Andreas Loukas, Joseph F. JáJá
2020	Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering. Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh
2020	Greed Meets Sparsity: Understanding and Improving Greedy Coordinate Descent for Sparse Optimization. Huang Fang, Zhenan Fan, Yifan Sun, Michael P. Friedlander
2020	Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth
2020	Guarantees of Stochastic Greedy Algorithms for Non-monotone Submodular Maximization with Cardinality Constraint. Shinsaku Sakaue
2020	Hamiltonian Monte Carlo Swindles. Dan Piponi, Matthew D. Hoffman, Pavel Sountsov
2020	Hermitian matrices for clustering directed graphs: insights and applications. Mihai Cucuringu, Huan Li, He Sun, Luca Zanetti
2020	High Dimensional Robust Sparse Regression. Liu Liu, Yanyao Shen, Tianyang Li, Constantine Caramanis
2020	How To Backdoor Federated Learning. Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, Vitaly Shmatikov
2020	How fine can fine-tuning be? Learning efficient language models. Evani Radiya-Dixit, Xin Wang
2020	Hyperbolic Manifold Regression. Gian Maria Marconi, Carlo Ciliberto, Lorenzo Rosasco
2020	Hypothesis Testing Interpretations and Renyi Differential Privacy. Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, Tetsuya Sato
2020	Identifying and Correcting Label Bias in Machine Learning. Heinrich Jiang, Ofir Nachum
2020	Importance Sampling via Local Sensitivity. Anant Raj, Cameron Musco, Lester Mackey
2020	Improved Regret Bounds for Projection-free Bandit Convex Optimization. Dan Garber, Ben Kretzu
2020	Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation. Yuxuan Song, Ning Miao, Hao Zhou, Lantao Yu, Mingxuan Wang, Lei Li
2020	Imputation estimators for unnormalized models with missing data. Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim
2020	Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations. Jan Stuehmer, Richard E. Turner, Sebastian Nowozin
2020	Inference of Dynamic Graph Changes for Functional Connectome. Dingjue Ji, Junwei Lu, Yiliang Zhang, Siyuan Gao, Hongyu Zhao
2020	Infinitely deep neural networks as diffusion processes. Stefano Peluchetti, Stefano Favaro
2020	Integrals over Gaussians under Linear Domain Constraints. Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig
2020	Interpretable Companions for Black-Box Models. Danqing Pan, Tong Wang, Satoshi Hara
2020	Interpretable Deep Gaussian Processes with Moments. Chi-Ken Lu, Scott Cheng-Hsin Yang, Xiaoran Hao, Patrick Shafto
2020	Invertible Generative Modeling using Linear Rational Splines. Hadi Mohaghegh Dolatabadi, Sarah M. Erfani, Christopher Leckie
2020	Ivy: Instrumental Variable Synthesis for Causal Inference. Zhaobin Kuang, Frederic Sala, Nimit Sharad Sohoni, Sen Wu, Aldo Córdova-Palomera, Jared Dunnmon, James Priest, Christopher Ré
2020	Kernel Conditional Density Operators. Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet
2020	Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization. Poompol Buathong, David Ginsbourger, Tipaluck Krityakierne
2020	LIBRE: Learning Interpretable Boolean Rule Ensembles. Graziano Mita, Paolo Papotti, Maurizio Filippone, Pietro Michiardi
2020	Langevin Monte Carlo without smoothness. Niladri S. Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter L. Bartlett
2020	Laplacian-Regularized Graph Bandits: Algorithms and Theoretical Analysis. Kaige Yang, Laura Toni, Xiaowen Dong
2020	LdSM: Logarithm-depth Streaming Multi-label Decision Trees. Maryam Majzoubi, Anna Choromanska
2020	Learnable Bernoulli Dropout for Bayesian Deep Learning. Shahin Boluki, Randy Ardywibowo, Siamak Zamani Dadaneh, Mingyuan Zhou, Xiaoning Qian
2020	Learning Dynamic Hierarchical Topic Graph with Graph Convolutional Network for Document Classification. Zhengjue Wang, Chaojie Wang, Hao Zhang, Zhibin Duan, Mingyuan Zhou, Bo Chen
2020	Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes. Zhaozhi Qian, Ahmed M. Alaa, Alexis Bellot, Mihaela van der Schaar, Jem Rashbass
2020	Learning Entangled Single-Sample Distributions via Iterative Trimming. Hui Yuan, Yingyu Liang
2020	Learning Fair Representations for Kernel Models. Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar
2020	Learning Gaussian Graphical Models via Multiplicative Weights. Anamay Chaturvedi, Jonathan Scarlett
2020	Learning Hierarchical Interactions at Scale: A Convex Optimization Approach. Hussein Hazimeh, Rahul Mazumder
2020	Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters. Yuhao Wang, Uma Roy, Caroline Uhler
2020	Learning Ising and Potts Models with Latent Variables. Surbhi Goel
2020	Learning Overlapping Representations for the Estimation of Individualized Treatment Effects. Yao Zhang, Alexis Bellot, Mihaela van der Schaar
2020	Learning Rate Adaptation for Differentially Private Learning. Antti Koskela, Antti Honkela
2020	Learning Sparse Nonparametric DAGs. Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
2020	Learning in Gated Neural Networks. Ashok Vardhan Makkuva, Sewoong Oh, Sreeram Kannan, Pramod Viswanath
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2020	Learning spectrograms with convolutional spectral kernels. Zheyang Shen, Markus Heinonen, Samuel Kaski
2020	Learning with minibatch Wasserstein : asymptotic and gradient properties. Kilian Fatras, Younes Zine, Rémi Flamary, Rémi Gribonval, Nicolas Courty
2020	Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data. Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Riis Andersen
2020	Linear Convergence of Adaptive Stochastic Gradient Descent. Yuege Xie, Xiaoxia Wu, Rachel A. Ward
2020	Linear Dynamics: Clustering without identification. Chloe Ching-Yun Hsu, Michaela Hardt, Moritz Hardt
2020	Linear predictor on linearly-generated data with missing values: non consistency and solutions. Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gaël Varoquaux
2020	Linearly Convergent Frank-Wolfe without Line-Search. Fabian Pedregosa, Geoffrey Négiar, Armin Askari, Martin Jaggi
2020	Lipschitz Continuous Autoencoders in Application to Anomaly Detection. Young-geun Kim, Yongchan Kwon, Hyunwoong Chang, Myunghee Cho Paik
2020	Local Differential Privacy for Sampling. Hisham Husain, Borja Balle, Zac Cranko, Richard Nock
2020	Locally Accelerated Conditional Gradients. Jelena Diakonikolas, Alejandro Carderera, Sebastian Pokutta
2020	Logistic regression with peer-group effects via inference in higher-order Ising models. Constantinos Daskalakis, Nishanth Dikkala, Ioannis Panageas
2020	Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs. Guy Uziel, Ran El-Yaniv
2020	Low-rank regularization and solution uniqueness in over-parameterized matrix sensing. Kelly Geyer, Anastasios Kyrillidis, Amir Kalev
2020	MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search. Insu Han, Jennifer Gillenwater
2020	Marginal Densities, Factor Graph Duality, and High-Temperature Series Expansions. Mehdi Molkaraie
2020	Measuring Mutual Information Between All Pairs of Variables in Subquadratic Complexity. Mohsen Ferdosi, Arash Gholami Davoodi, Hosein Mohimani
2020	Minimax Bounds for Structured Prediction Based on Factor Graphs. Kevin Bello, Asish Ghoshal, Jean Honorio
2020	Minimax Rank-$1$ Matrix Factorization. Venkatesh Saligrama, Alexander Olshevsky, Julien M. Hendrickx
2020	Minimax Testing of Identity to a Reference Ergodic Markov Chain. Geoffrey Wolfer, Aryeh Kontorovich
2020	Minimizing Dynamic Regret and Adaptive Regret Simultaneously. Lijun Zhang, Shiyin Lu, Tianbao Yang
2020	Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama
2020	Mixed Strategies for Robust Optimization of Unknown Objectives. Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause
2020	Model-Agnostic Counterfactual Explanations for Consequential Decisions. Amir-Hossein Karimi, Gilles Barthe, Borja Balle, Isabel Valera
2020	Modular Block-diagonal Curvature Approximations for Feedforward Architectures. Felix Dangel, Stefan Harmeling, Philipp Hennig
2020	Momentum in Reinforcement Learning. Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist
2020	Monotonic Gaussian Process Flows. Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell
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2020	Multi-attribute Bayesian optimization with interactive preference learning. Raul Astudillo, Peter I. Frazier
2020	Multi-level Gaussian Graphical Models Conditional on Covariates. Gi-Bum Kim, Seyoung Kim
2020	Multiplicative Gaussian Particle Filter. Xuan Su, Wee Sun Lee, Zhen Zhang
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2020	Neural Decomposition: Functional ANOVA with Variational Autoencoders. Kaspar Märtens, Christopher Yau
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2020	Online Batch Decision-Making with High-Dimensional Covariates. Chi-hua Wang, Guang Cheng
2020	Online Binary Space Partitioning Forests. Xuhui Fan, Bin Li, Scott A. Sisson
2020	Online Continuous DR-Submodular Maximization with Long-Term Budget Constraints. Omid Sadeghi, Maryam Fazel
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2020	Online Learning Using Only Peer Prediction. Yang Liu, David P. Helmbold
2020	Online Learning with Continuous Variations: Dynamic Regret and Reductions. Ching-An Cheng, Jonathan Lee, Ken Goldberg, Byron Boots
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2020	Optimal Approximation of Doubly Stochastic Matrices. Nikitas Rontsis, Paul Goulart
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2020	Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning. Zhenzhang Ye, Thomas Möllenhoff, Tao Wu, Daniel Cremers
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2020	Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization. Kenji Kawaguchi, Haihao Lu
2020	Ordering-Based Causal Structure Learning in the Presence of Latent Variables. Daniel Irving Bernstein, Basil Saeed, Chandler Squires, Caroline Uhler
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