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ORCID23rd AISTATS 2020: Online [Palermo, Sicily, Italy]
- Silvia Chiappa, Roberto Calandra:
The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy]. Proceedings of Machine Learning Research 108, PMLR 2020
- Fabian Pedregosa, Geoffrey Négiar, Armin Askari, Martin Jaggi:
Linearly Convergent Frank-Wolfe without Line-Search. 1-10 - Shinsaku Sakaue:
Guarantees of Stochastic Greedy Algorithms for Non-monotone Submodular Maximization with Cardinality Constraint. 11-21 - Shinsaku Sakaue:
On Maximization of Weakly Modular Functions: Guarantees of Multi-stage Algorithms, Tractability, and Hardness. 22-33 - Mark Rowland, Will Dabney, Rémi Munos:
Adaptive Trade-Offs in Off-Policy Learning. 34-44 - Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney:
Conditional Importance Sampling for Off-Policy Learning. 45-55 - Xuan Su, Wee Sun Lee, Zhen Zhang:
Multiplicative Gaussian Particle Filter. 56-65 - Jingyan Wang, Nihar B. Shah, R. Ravi:
Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons. 66-76 - Ilkay Yildiz, Jennifer G. Dy, Deniz Erdogmus, Jayashree Kalpathy-Cramer, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis:
Fast and Accurate Ranking Regression. 77-88 - Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar:
Tight Analysis of Privacy and Utility Tradeoff in Approximate Differential Privacy. 89-99 - Guy Uziel, Ran El-Yaniv:
Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs. 100-110 - Guy Uziel:
Nonparametric Sequential Prediction While Deep Learning the Kernel. 111-121 - Yuxuan Song, Ning Miao, Hao Zhou, Lantao Yu, Mingxuan Wang, Lei Li:
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation. 122-132 - Xiaorui Liu, Yao Li
, Jiliang Tang, Ming Yan:
A Double Residual Compression Algorithm for Efficient Distributed Learning. 133-143 - Alexander Terenin, Daniel Simpson, David Draper:
Asynchronous Gibbs Sampling. 144-154 - Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar:
Learning Fair Representations for Kernel Models. 155-166 - Samuele Tosatto, João Carvalho, Hany Abdulsamad, Jan Peters:
A Nonparametric Off-Policy Policy Gradient. 167-177 - Jonathan Wenger, Hedvig Kjellström, Rudolph Triebel:
Non-Parametric Calibration for Classification. 178-190 - Geoffrey Wolfer, Aryeh Kontorovich:
Minimax Testing of Identity to a Reference Ergodic Markov Chain. 191-201 - Longfei Yan, W. Bastiaan Kleijn, Thushara D. Abhayapala:
A Linear-time Independence Criterion Based on a Finite Basis Approximation. 202-212 - Kevin Bello, Asish Ghoshal, Jean Honorio:
Minimax Bounds for Structured Prediction Based on Factor Graphs. 213-222 - Bingcong Li, Meng Ma, Georgios B. Giannakis:
On the Convergence of SARAH and Beyond. 223-233 - Tim Pearce, Felix Leibfried, Alexandra Brintrup:
Uncertainty in Neural Networks: Approximately Bayesian Ensembling. 234-244 - Graziano Mita, Paolo Papotti, Maurizio Filippone, Pietro Michiardi:
LIBRE: Learning Interpretable Boolean Rule Ensembles. 245-255 - Mehdi Molkaraie:
Marginal Densities, Factor Graph Duality, and High-Temperature Series Expansions. 256-265 - Melanie Weber:
Neighborhood Growth Determines Geometric Priors for Relational Representation Learning. 266-276 - Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera:
Fair Decisions Despite Imperfect Predictions. 277-287 - Martin Mihelich, Charles Dognin, Yan Shu, Michael Blot:
A Characterization of Mean Squared Error for Estimator with Bagging. 288-297 - Yuexi Wang, Veronika Rocková:
Uncertainty Quantification for Sparse Deep Learning. 298-308 - Lijun Zhang, Shiyin Lu, Tianbao Yang:
Minimizing Dynamic Regret and Adaptive Regret Simultaneously. 309-319 - Wenkai Xu, Takeru Matsuda:
A Stein Goodness-of-fit Test for Directional Distributions. 320-330 - Taeeon Park, Taesup Moon:
Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel. 331-340 - Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Riis Andersen:
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data. 341-351 - Fengpei Li, Henry Lam, Siddharth Prusty:
Robust Importance Weighting for Covariate Shift. 352-362 - Peng Yang, Ping Li:
Adaptive Online Kernel Sampling for Vertex Classification. 363-373 - Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Phuong Ha Nguyen, Marten van Dijk, Quoc Tran-Dinh:
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning. 374-385 - Hideaki Ishibashi, Hideitsu Hino:
Stopping criterion for active learning based on deterministic generalization bounds. 386-397 - Zhaobin Kuang, Frederic Sala, Nimit Sharad Sohoni, Sen Wu, Aldo Córdova-Palomera, Jared Dunnmon, James Priest, Christopher Ré:
Ivy: Instrumental Variable Synthesis for Causal Inference. 398-410 - Liu Liu, Yanyao Shen, Tianyang Li, Constantine Caramanis:
High Dimensional Robust Sparse Regression. 411-421 - Ruiyi Zhang, Changyou Chen, Zhe Gan, Zheng Wen, Wenlin Wang, Lawrence Carin:
Nested-Wasserstein Self-Imitation Learning for Sequence Generation. 422-433 - Huang Fang, Zhenan Fan, Yifan Sun, Michael P. Friedlander:
Greed Meets Sparsity: Understanding and Improving Greedy Coordinate Descent for Sparse Optimization. 434-444 - Carolyn Kim, Mohsen Bayati:
Recommendation on a Budget: Column Space Recovery from Partially Observed Entries with Random or Active Sampling. 445-455 - Sungjin Im, Mahshid Montazer Qaem, Benjamin Moseley, Xiaorui Sun, Rudy Zhou:
Fast Noise Removal for k-Means Clustering. 456-466 - Yingyu Liang, Zhao Song, Mengdi Wang, Lin Yang, Xin Yang:
Sketching Transformed Matrices with Applications to Natural Language Processing. 467-481 - Alireza Samadian, Kirk Pruhs, Benjamin Moseley, Sungjin Im, Ryan R. Curtin:
Unconditional Coresets for Regularized Loss Minimization. 482-492 - Asaf Noy, Niv Nayman, Tal Ridnik, Nadav Zamir, Sivan Doveh, Itamar Friedman, Raja Giryes, Lihi Zelnik:
ASAP: Architecture Search, Anneal and Prune. 493-503 - Jingling Li, Yanchao Sun, Jiahao Su, Taiji Suzuki, Furong Huang:
Understanding Generalization in Deep Learning via Tensor Methods. 504-515 - Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni:
Accelerating Gradient Boosting Machines. 516-526 - Xuhui Fan, Bin Li, Scott A. Sisson:
Online Binary Space Partitioning Forests. 527-537 - Benjamin Poignard, Makoto Yamada:
Sparse Hilbert-Schmidt Independence Criterion Regression. 538-548 - Wasim Huleihel, Ofer Shayevitz:
Sharp Thresholds of the Information Cascade Fragility Under a Mismatched Model. 549-558 - Henrik Imberg, Johan Jonasson, Marina Axelson-Fisk:
Optimal sampling in unbiased active learning. 559-569 - Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon:
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth measure. 570-579 - Christopher Tosh, Daniel Hsu:
Diameter-based Interactive Structure Discovery. 580-590 - Etienne Boursier, Vianney Perchet:
Utility/Privacy Trade-off through the lens of Optimal Transport. 591-601 - Maxime Laborde, Adam M. Oberman:
A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case. 602-612 - Chi-Ken Lu, Scott Cheng-Hsin Yang, Xiaoran Hao, Patrick Shafto:
Interpretable Deep Gaussian Processes with Moments. 613-623 - Lars Buesing, Nicolas Heess, Theophane Weber:
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. 624-634 - 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:
Accelerated Bayesian Optimisation through Weight-Prior Tuning. 635-645 - Yunhao Tang, Krzysztof Choromanski, Alp Kucukelbir:
Variance Reduction for Evolution Strategies via Structured Control Variates. 646-656 - Zhenzhang Ye, Thomas Möllenhoff, Tao Wu, Daniel Cremers:
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning. 657-668 - Kenji Kawaguchi, Haihao Lu:
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization. 669-679 - Eduard Gorbunov, Filip Hanzely, Peter Richtárik:
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent. 680-690 - Saptarshi Chakraborty, Debolina Paul, Swagatam Das, Jason Q. Xu:
Entropy Weighted Power k-Means Clustering. 691-701 - Heinrich Jiang, Ofir Nachum:
Identifying and Correcting Label Bias in Machine Learning. 702-712 - Yibo Zeng, Fei Feng, Wotao Yin:
AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity. 713-723 - Dan Kushnir, Benjamin Mirabelli:
Active Community Detection with Maximal Expected Model Change. 724-734 - Takashi Nicholas Maeda, Shohei Shimizu:
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. 735-745 - Peng Zhao, Lijun Zhang, Yuan Jiang, Zhi-Hua Zhou:
A Simple Approach for Non-stationary Linear Bandits. 746-755 - Pedro Cisneros-Velarde, Alexander Petersen, Sang-Yun Oh:
Distributionally Robust Formulation and Model Selection for the Graphical Lasso. 756-765 - Cheng Chen, Ming Gu, Zhihua Zhang, Weinan Zhang, Yong Yu:
Efficient Spectrum-Revealing CUR Matrix Decomposition. 766-775 - Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh:
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering. 776-787 - Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney:
Characterization of Overlap in Observational Studies. 788-798 - Felix Dangel, Stefan Harmeling, Philipp Hennig:
Modular Block-diagonal Curvature Approximations for Feedforward Architectures. 799-808 - Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda:
A Unified Statistically Efficient Estimation Framework for Unnormalized Models. 809-819 - Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira:
More Powerful Selective Kernel Tests for Feature Selection. 820-830 - Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim:
Imputation estimators for unnormalized models with missing data. 831-841 - Youssef Mroueh:
Wasserstein Style Transfer. 842-852 - Kenji Kawaguchi, Leslie Pack Kaelbling:
Elimination of All Bad Local Minima in Deep Learning. 853-863 - Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi:
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs. 864-874 - David McAllester, Karl Stratos:
Formal Limitations on the Measurement of Mutual Information. 875-884 - Cuize Han, Nikhil Rao, Daria Sorokina, Karthik Subbian:
Scalable Feature Selection for (Multitask) Gradient Boosted Trees. 885-894 - Amir-Hossein Karimi, Gilles Barthe, Borja Balle, Isabel Valera:
Model-Agnostic Counterfactual Explanations for Consequential Decisions. 895-905 - Hsiang Hsu, Shahab Asoodeh, Flávio P. Calmon:
Obfuscation via Information Density Estimation. 906-917 - Chloe Ching-Yun Hsu, Michaela Hardt, Moritz Hardt:
Linear Dynamics: Clustering without identification. 918-929 - Kelly Geyer, Anastasios Kyrillidis, Amir Kalev:
Low-rank regularization and solution uniqueness in over-parameterized matrix sensing. 930-940 - Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri:
Robustness for Non-Parametric Classification: A Generic Attack and Defense. 941-951 - Shiyun Chen, Shiva Prasad Kasiviswanathan:
Contextual Online False Discovery Rate Control. 952-961 - Tom Hess, Sivan Sabato:
Sequential no-Substitution k-Median-Clustering. 962-972 - Sanjoy Dasgupta, Sivan Sabato:
Robust Learning from Discriminative Feature Feedback. 973-982 - Mihai Cucuringu, Huan Li, He Sun, Luca Zanetti:
Hermitian matrices for clustering directed graphs: insights and applications. i - Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet:
Kernel Conditional Density Operators. 993-1004 - Yao Zhang, Alexis Bellot, Mihaela van der Schaar:
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects. 1005-1014 - Xingchen Ma, Matthew B. Blaschko:
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization. 1015-1025 - Ping Ma, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney:
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms. 1026-1035 - Feras Saad, Cameron E. Freer, Martin C. Rinard, Vikash Mansinghka:
The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions. 1036-1046 - Zhize Li, Jian Li:
A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization. 1047-1057 - Lin Chen, Mingrui Zhang, Hamed Hassani, Amin Karbasi:
Black Box Submodular Maximization: Discrete and Continuous Settings. 1058-1070 - Ilija Bogunovic, Andreas Krause, Jonathan Scarlett:
Corruption-Tolerant Gaussian Process Bandit Optimization. 1071-1081 - Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar:
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms. 1082-1092 - Avishek Ghosh, Kannan Ramchandran:
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression. 1093-1103 - Anamay Chaturvedi, Jonathan Scarlett:
Learning Gaussian Graphical Models via Multiplicative Weights. 1104-1114 - Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama:
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. 1115-1125 - Stefano Peluchetti, Stefano Favaro:
Infinitely deep neural networks as diffusion processes. 1126-1136 - Stefano Peluchetti, Stefano Favaro, Sandra Fortini:
Stable behaviour of infinitely wide deep neural networks. 1137-1146 - Xinyi Wang, Yi Yang:
Neural Topic Model with Attention for Supervised Learning. 1147-1156 - Pengzhou Wu, Kenji Fukumizu:
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method. 1157-1167 - Leonardo Cella, Nicolò Cesa-Bianchi:
Stochastic Bandits with Delay-Dependent Payoffs. 1168-1177 - Fabien Lauer:
Risk Bounds for Learning Multiple Components with Permutation-Invariant Losses. 1178-1187 - Matteo Papini, Andrea Battistello, Marcello Restelli:
Balancing Learning Speed and Stability in Policy Gradient via Adaptive Exploration. 1188-1199 - Jan Stuehmer, Richard E. Turner, Sebastian Nowozin:
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations. 1200-1210 - Abbas Mehrabian, Etienne Boursier, Emilie Kaufmann, Vianney Perchet:
A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players. 1211-1221 - François-Pierre Paty, Alexandre d'Aspremont, Marco Cuturi:
Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport. 1222-1232 - Minshuo Chen, Xingguo Li, Tuo Zhao:
On Generalization Bounds of a Family of Recurrent Neural Networks. 1233-1243 - Keiichi Kisamori, Motonobu Kanagawa, Keisuke Yamazaki:
Simulator Calibration under Covariate Shift with Kernels. 1244-1253 - Milan Vojnovic, Se-Young Yun, Kaifang Zhou:
Convergence Rates of Gradient Descent and MM Algorithms for Bradley-Terry Models. 1254-1264 - Matthew Fisher, Chris J. Oates, Catherine E. Powell, Aretha L. Teckentrup:
A Locally Adaptive Bayesian Cubature Method. 1265-1275 - Klim Efremenko, Aryeh Kontorovich, Moshe Noivirt:
Fast and Bayes-consistent nearest neighbors. 1276-1286 - Damien Garreau, Ulrike von Luxburg:
Explaining the Explainer: A First Theoretical Analysis of LIME. 1287-1296 - Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi:
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization. 1297-1307 - Changjian Shui, Fan Zhou, Christian Gagné, Boyu Wang:
Deep Active Learning: Unified and Principled Method for Query and Training. 1308-1318 - Botao Hao, Anru R. Zhang, Guang Cheng:
Sparse and Low-rank Tensor Estimation via Cubic Sketchings. 1319-1330 - Arnak S. Dalalyan, Nicolas Schreuder, Victor-Emmanuel Brunel:
A nonasymptotic law of iterated logarithm for general M-estimators. 1331-1341 - Thomas Nedelec, Clément Calauzènes, Vianney Perchet, Noureddine El Karoui:
Robust Stackelberg buyers in repeated auctions. 1342-1351 - Sebastian Farquhar, Michael A. Osborne, Yarin Gal:
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning. 1352-1362 - Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang:
Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes. 1363-1374 - Si Yi Meng, Sharan Vaswani, Issam Hadj Laradji, Mark Schmidt, Simon Lacoste-Julien:
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. 1375-1386 - Matthias Kirchler, Shahryar Khorasani, Marius Kloft, Christoph Lippert:
Two-sample Testing Using Deep Learning. 1387-1398 - Prathamesh Mayekar, Himanshu Tyagi:
RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization. 1399-1409 - Konstantinos Skianis, Giannis Nikolentzos, Stratis Limnios, Michalis Vazirgiannis:
Rep the Set: Neural Networks for Learning Set Representations. 1410-1420 - Robin Vogel, Stéphan Clémençon:
A Multiclass Classification Approach to Label Ranking. 1421-1430 - Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta:
Conservative Exploration in Reinforcement Learning. 1431-1441 - Aram-Alexand