AISTATS 2020

423 papers

Robust Stackelberg buyers in repeated auctions

Thomas Nedelec, Clément Calauzènes, Vianney Perchet, Noureddine El Karoui

A nonasymptotic law of iterated logarithm for general M-estimators

Nicolas Schreuder, Victor-Emmanuel Brunel, Arnak Dalalyan

Sparse and Low-rank Tensor Estimation via Cubic Sketchings

Botao Hao, Anru R. Zhang, Guang Cheng

Deep Active Learning: Unified and Principled Method for Query and Training

Changjian Shui, Fan Zhou, Christian Gagné, Boyu Wang

A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization

Foivos Alimisis, Antonio Orvieto, Gary Becigneul, Aurelien Lucchi

Explaining the Explainer: A First Theoretical Analysis of LIME

Damien Garreau, Ulrike Luxburg

Fast and Bayes-consistent nearest neighbors

Klim Efremenko, Aryeh Kontorovich, Moshe Noivirt

A Locally Adaptive Bayesian Cubature Method

Matthew Fisher, Chris Oates, Catherine Powell, Aretha Teckentrup

Convergence Rates of Gradient Descent and MM Algorithms for Bradley-Terry Models

Milan Vojnovic, Se-Young Yun, Kaifang Zhou

Simulator Calibration under Covariate Shift with Kernels

Keiichi Kisamori, Motonobu Kanagawa, Keisuke Yamazaki

On Generalization Bounds of a Family of Recurrent Neural Networks

Minshuo Chen, Xingguo Li, Tuo Zhao

Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport

François-Pierre Paty, Alexandre d’Aspremont, Marco Cuturi

A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players

Abbas Mehrabian, Etienne Boursier, Emilie Kaufmann, Vianney Perchet

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations

Jan Stuehmer, Richard Turner, Sebastian Nowozin

Balancing Learning Speed and Stability in Policy Gradient via Adaptive Exploration

Matteo Papini, Andrea Battistello, Marcello Restelli

Risk Bounds for Learning Multiple Components with Permutation-Invariant Losses

Fabien Lauer

Stochastic Bandits with Delay-Dependent Payoffs

Leonardo Cella, Nicolò Cesa-bianchi

Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method

Pengzhou Wu, Kenji Fukumizu

Neural Topic Model with Attention for Supervised Learning

Xinyi Wang, Yi Yang

Stable behaviour of infinitely wide deep neural networks

Stefano Peluchetti, Stefano Favaro, Sandra Fortini

Infinitely deep neural networks as diffusion processes

Stefano Peluchetti, Stefano Favaro

Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach

Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama

Learning Gaussian Graphical Models via Multiplicative Weights

Anamay Chaturvedi, Jonathan Scarlett

Alternating Minimization Converges Super-Linearly for Mixed Linear Regression

Avishek Ghosh, Ramchandran Kannan

On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms

Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar

Corruption-Tolerant Gaussian Process Bandit Optimization

Ilija Bogunovic, Andreas Krause, Jonathan Scarlett

Black Box Submodular Maximization: Discrete and Continuous Settings

Lin Chen, Mingrui Zhang, Hamed Hassani, Amin Karbasi

A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization

Zhize Li, Jian Li

The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions

Feras Saad, Cameron Freer, Martin Rinard, Vikash Mansinghka

Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms

Ping Ma, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael Mahoney

Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization

Xingchen Ma, Matthew Blaschko

Learning Overlapping Representations for the Estimation of Individualized Treatment Effects

Yao Zhang, Alexis Bellot, Mihaela Schaar

Kernel Conditional Density Operators

Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet

Hermitian matrices for clustering directed graphs: insights and applications

Mihai Cucuringu, Huan Li, He Sun, Luca Zanetti

Robust Learning from Discriminative Feature Feedback

Sanjoy Dasgupta, Sivan Sabato

Sequential no-Substitution k-Median-Clustering

Tom Hess, Sivan Sabato

Contextual Online False Discovery Rate Control

Shiyun Chen, Shiva Kasiviswanathan

Robustness for Non-Parametric Classification: A Generic Attack and Defense

Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri

Low-rank regularization and solution uniqueness in over-parameterized matrix sensing

Kelly Geyer, Anastasios Kyrillidis, Amir Kalev

Linear Dynamics: Clustering without identification

Chloe Hsu, Michaela Hardt, Moritz Hardt

Obfuscation via Information Density Estimation

Hsiang Hsu, Shahab Asoodeh, Flavio Calmon

Model-Agnostic Counterfactual Explanations for Consequential Decisions

Amir-Hossein Karimi, Gilles Barthe, Borja Balle, Isabel Valera

Scalable Feature Selection for (Multitask) Gradient Boosted Trees

Cuize Han, Nikhil Rao, Daria Sorokina, Karthik Subbian

Formal Limitations on the Measurement of Mutual Information

David Mcallester, Karl Stratos

Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs

Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi

Elimination of All Bad Local Minima in Deep Learning

Kenji Kawaguchi, Leslie Kaelbling

Wasserstein Style Transfer

Youssef Mroueh

Imputation estimators for unnormalized models with missing data

Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim

More Powerful Selective Kernel Tests for Feature Selection

Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui

A Unified Statistically Efficient Estimation Framework for Unnormalized Models

Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda

Modular Block-diagonal Curvature Approximations for Feedforward Architectures

Felix Dangel, Stefan Harmeling, Philipp Hennig

Characterization of Overlap in Observational Studies

Michael Oberst, Fredrik Johansson, Dennis Wei, Tian Gao, Gabriel Brat

Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering

Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-jui Hsieh

Efficient Spectrum-Revealing CUR Matrix Decomposition

Cheng Chen, Ming Gu, Zhihua Zhang, Weinan Zhang, Yong Yu

Distributionally Robust Formulation and Model Selection for the Graphical Lasso

Pedro Cisneros-Velarde, Alexander Petersen, Sang-Yun Oh

A Simple Approach for Non-stationary Linear Bandits

Peng Zhao, Lijun Zhang, Yuan Jiang, Zhi-Hua Zhou

RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders

Takashi Nicholas Maeda, Shohei Shimizu

Active Community Detection with Maximal Expected Model Change

Dan Kushnir, Benjamin Mirabelli

AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity

Yibo Zeng, Fei Feng, Wotao Yin

Identifying and Correcting Label Bias in Machine Learning

Heinrich Jiang, Ofir Nachum

Entropy Weighted Power k-Means Clustering

Saptarshi Chakraborty, Debolina Paul, Swagatam Das, Jason Xu

A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent

Eduard Gorbunov, Filip Hanzely, Peter Richtarik

Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization

Kenji Kawaguchi, Haihao Lu

Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning

Zhenzhang Ye, Thomas Möllenhoff, Tao Wu, Daniel Cremers

Variance Reduction for Evolution Strategies via Structured Control Variates

Yunhao Tang, Krzysztof Choromanski, Alp Kucukelbir

Accelerated Bayesian Optimisation through Weight-Prior Tuning

Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Cheng Li

Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions

Lars Buesing, Nicolas Heess, Theophane Weber

Interpretable Deep Gaussian Processes with Moments

Chi-Ken Lu, Scott Cheng-Hsin Yang, Xiaoran Hao, Patrick Shafto

A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case

Maxime Laborde, Adam Oberman

Utility/Privacy Trade-off through the lens of Optimal Transport

Etienne Boursier, Vianney Perchet

Diameter-based Interactive Structure Discovery

Christopher Tosh, Daniel Hsu

The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth measure

Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémen\con

Optimal sampling in unbiased active learning

Henrik Imberg, Johan Jonasson, Marina Axelson-Fisk

Sharp Thresholds of the Information Cascade Fragility Under a Mismatched Model

Wasim Huleihel, Ofer Shayevitz

Sparse Hilbert-Schmidt Independence Criterion Regression

Benjamin Poignard, Makoto Yamada

Online Binary Space Partitioning Forests

Xuhui Fan, Bin Li, Scott SIsson

Accelerating Gradient Boosting Machines

Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab Mirrokni

Understanding Generalization in Deep Learning via Tensor Methods

Jingling Li, Yanchao Sun, Jiahao Su, Taiji Suzuki, Furong Huang

ASAP: Architecture Search, Anneal and Prune

Asaf Noy, Niv Nayman, Tal Ridnik, Nadav Zamir, Sivan Doveh

Unconditional Coresets for Regularized Loss Minimization

Alireza Samadian, Kirk Pruhs, Benjamin Moseley, Sungjin Im, Ryan Curtin

Sketching Transformed Matrices with Applications to Natural Language Processing

Yingyu Liang, Zhao Song, Mengdi Wang, Lin Yang, Xin Yang

Fast Noise Removal for k-Means Clustering

Sungjin Im, Mahshid Montazer Qaem, Benjamin Moseley, Xiaorui Sun, Rudy Zhou

Recommendation on a Budget: Column Space Recovery from Partially Observed Entries with Random or Active Sampling

Carolyn Kim, Mohsen Bayati

Greed Meets Sparsity: Understanding and Improving Greedy Coordinate Descent for Sparse Optimization

Huang Fang, Zhenan Fan, Yifan Sun, Michael Friedlander

Nested-Wasserstein Self-Imitation Learning for Sequence Generation

Ruiyi Zhang, Changyou Chen, Zhe Gan, Zheng Wen, Wenlin Wang

High Dimensional Robust Sparse Regression

Liu Liu, Yanyao Shen, Tianyang Li, Constantine Caramanis

Ivy: Instrumental Variable Synthesis for Causal Inference

Zhaobin Kuang, Frederic Sala, Nimit Sohoni, Sen Wu, Aldo Córdova-Palomera

Stopping criterion for active learning based on deterministic generalization bounds

Hideaki Ishibashi, Hideitsu Hino

A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning

Nhan Pham, Lam Nguyen, Dzung Phan, PHUONG HA NGUYEN, Marten Dijk

Adaptive Online Kernel Sampling for Vertex Classification

Peng Yang, Ping Li

Robust Importance Weighting for Covariate Shift

Fengpei Li, Henry Lam, Siddharth Prusty

Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data

Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Andersen

Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel

Taeeon Park, Taesup Moon

A Stein Goodness-of-fit Test for Directional Distributions

Wenkai Xu, Takeru Matsuda

Minimizing Dynamic Regret and Adaptive Regret Simultaneously

Lijun Zhang, Shiyin Lu, Tianbao Yang

Uncertainty Quantification for Sparse Deep Learning

Yuexi Wang, Veronika Ročková

A Characterization of Mean Squared Error for Estimator with Bagging

Martin Mihelich, Charles Dognin, Yan Shu, Michael Blot

Fair Decisions Despite Imperfect Predictions

Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera

Neighborhood Growth Determines Geometric Priors for Relational Representation Learning

Melanie Weber

Marginal Densities, Factor Graph Duality, and High-Temperature Series Expansions

Mehdi Molkaraie