AISTATS 2021

455 papers

Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models

Sasi Kumar Murakonda, Reza Shokri, George Theodorakopoulos

Aggregating Incomplete and Noisy Rankings

Dimitris Fotakis, Alkis Kalavasis, Konstantinos Stavropoulos

Implicit Regularization via Neural Feature Alignment

Aristide Baratin, Thomas George, César Laurent, R Devon Hjelm, Guillaume Lajoie

Clustering multilayer graphs with missing nodes

Guillaume Braun, Hemant Tyagi, Christophe Biernacki

Federated f-Differential Privacy

Qinqing Zheng, Shuxiao Chen, Qi Long, Weijie Su

Inductive Mutual Information Estimation: A Convex Maximum-Entropy Copula Approach

Yves-Laurent Kom Samo

Fractional moment-preserving initialization schemes for training deep neural networks

Mert Gurbuzbalaban, Yuanhan Hu

High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation

Kristjan Greenewald, Karthikeyan Shanmugam, Dmitriy Katz

List Learning with Attribute Noise

Mahdi Cheraghchi, Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie

Smooth Bandit Optimization: Generalization to Holder Space

Yusha Liu, Yining Wang, Aarti Singh

vqSGD: Vector Quantized Stochastic Gradient Descent

Venkata Gandikota, Daniel Kane, Raj Kumar Maity, Arya Mazumdar

Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning

Hitesh Sapkota, Yiming Ying, Feng Chen, Qi Yu

Selective Classification via One-Sided Prediction

Aditya Gangrade, Anil Kag, Venkatesh Saligrama

Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization

Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu

Misspecification in Prediction Problems and Robustness via Improper Learning

Annie Marsden, John Duchi, Gregory Valiant

Learning Contact Dynamics using Physically Structured Neural Networks

Andreas Hochlehnert, Alexander Terenin, Steindor Saemundsson, Marc Deisenroth

Stochastic Gradient Descent Meets Distribution Regression

Nicole Muecke

Learning Matching Representations for Individualized Organ Transplantation Allocation

Can Xu, Ahmed Alaa, Ioana Bica, Brent Ershoff, Maxime Cannesson

Context-Specific Likelihood Weighting

Nitesh Kumar, Ondrej Kuzelka

Corralling Stochastic Bandit Algorithms

Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri

Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference

Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe

On the Suboptimality of Negative Momentum for Minimax Optimization

Guodong Zhang, Yuanhao Wang

Localizing Changes in High-Dimensional Regression Models

Alessandro Rinaldo, Daren Wang, Qin Wen, Rebecca Willett, Yi Yu

Quantum Tensor Networks, Stochastic Processes, and Weighted Automata

Sandesh Adhikary, Siddarth Srinivasan, Jacob Miller, Guillaume Rabusseau, Byron Boots

The Teaching Dimension of Kernel Perceptron

Akash Kumar, Hanqi Zhang, Adish Singla, Yuxin Chen

No-Regret Algorithms for Private Gaussian Process Bandit Optimization

Abhimanyu Dubey

Active Online Learning with Hidden Shifting Domains

Yining Chen, Haipeng Luo, Tengyu Ma, Chicheng Zhang

A Variational Inference Approach to Learning Multivariate Wold Processes

Jalal Etesami, William Trouleau, Negar Kiyavash, Matthias Grossglauser, Patrick Thiran

Equitable and Optimal Transport with Multiple Agents

meyer scetbon, Laurent Meunier, Jamal Atif, Marco Cuturi

Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss

ZHENHUAN YANG, Yunwen Lei, Siwei Lyu, Yiming Ying

Budgeted and Non-Budgeted Causal Bandits

Vineet Nair, Vishakha Patil, Gaurav Sinha

Approximate Data Deletion from Machine Learning Models

Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou

Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders

Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi

Learning with Gradient Descent and Weakly Convex Losses

Dominic Richards, Mike Rabbat

Gradient Descent in RKHS with Importance Labeling

Tomoya Murata, Taiji Suzuki

Counterfactual Representation Learning with Balancing Weights

Serge Assaad, Shuxi Zeng, Chenyang Tao, Shounak Datta, Nikhil Mehta

High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding

Hannah Marienwald, Jean-Baptiste Fermanian, Gilles Blanchard

Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features

Shingo Yashima, Atsushi Nitanda, Taiji Suzuki

The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain

Fergus Simpson, Alexis Boukouvalas, Vaclav Cadek, Elvijs Sarkans, Nicolas Durrande

Adaptive wavelet pooling for convolutional neural networks

Moritz Wolter, Jochen Garcke

PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming

Alexander Lew, Monica Agrawal, David Sontag, Vikash Mansinghka

Moment-Based Variational Inference for Stochastic Differential Equations

Christian Wildner, Heinz Koeppl

Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning

Kai Cui, Heinz Koeppl

CONTRA: Contrarian statistics for controlled variable selection

Mukund Sudarshan, Aahlad Puli, Lakshmi Subramanian, Sriram Sankararaman, Rajesh Ranganath

Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization

Tianyi Liu, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao

Explicit Regularization of Stochastic Gradient Methods through Duality

Anant Raj, Francis Bach

No-regret Algorithms for Multi-task Bayesian Optimization

Sayak Ray Chowdhury, Aditya Gopalan

Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery

Mike Laszkiewicz, Asja Fischer, Johannes Lederer

Reinforcement Learning in Parametric MDPs with Exponential Families

Sayak Ray Chowdhury, Aditya Gopalan, Odalric-ambrym Maillard

Free-rider Attacks on Model Aggregation in Federated Learning

Yann Fraboni, Richard Vidal, Marco Lorenzi

Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations

Simone Rossi, Markus Heinonen, Edwin Bonilla, Zheyang Shen, Maurizio Filippone

Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings

Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang

DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation

Frederik Harder, Kamil Adamczewski, Mijung Park

Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms

Alicia Curth, Mihaela van der Schaar

Online probabilistic label trees

Kalina Jasinska-Kobus, Marek Wydmuch, Devanathan Thiruvenkatachari, Krzysztof Dembczynski

Understanding and Mitigating Exploding Inverses in Invertible Neural Networks

Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger Grosse, Joern-Henrik Jacobsen

γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator

Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama

All of the Fairness for Edge Prediction with Optimal Transport

Charlotte Laclau, Ievgen Redko, Manvi Choudhary, Christine Largeron

Deep Neural Networks Are Congestion Games: From Loss Landscape to Wardrop Equilibrium and Beyond

Nina Vesseron, Ievgen Redko, Charlotte Laclau

Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties

Lisa Schut, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu

Sparse Algorithms for Markovian Gaussian Processes

William Wilkinson, Arno Solin, Vincent ADAM

Improved Complexity Bounds in Wasserstein Barycenter Problem

Darina Dvinskikh, Daniil Tiapkin

Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects

Qiming Du, Gérard Biau, Francois Petit, Raphaël Porcher

On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow

Youssef Mroueh, Truyen Nguyen

Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation

Yuki Ohnishi, Jean Honorio

Stability and Risk Bounds of Iterative Hard Thresholding

Xiaotong Yuan, Ping Li

Competing AI: How does competition feedback affect machine learning?

Tony Ginart, Eva Zhang, Yongchan Kwon, James Zou

Iterative regularization for convex regularizers

Cesare Molinari, Mathurin Massias, Lorenzo Rosasco, Silvia Villa

Non-Volume Preserving Hamiltonian Monte Carlo and No-U-TurnSamplers

Hadi Mohasel Afshar, Rafael Oliveira, Sally Cripps

Why did the distribution change?

Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng

Maximizing Agreements for Ranking, Clustering and Hierarchical Clustering via MAX-CUT

Vaggos Chatziafratis, Mohammad Mahdian, Sara Ahmadian

Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation

Han Bao, Masashi Sugiyama

Collaborative Classification from Noisy Labels

Lucas Maystre, Nagarjuna Kumarappan, Judith Bütepage, Mounia Lalmas

Exploiting Equality Constraints in Causal Inference

Chi Zhang, Carlos Cinelli, Bryant Chen, Judea Pearl

On the Faster Alternating Least-Squares for CCA

Zhiqiang Xu, Ping Li

Minimax Model Learning

Cameron Voloshin, Nan Jiang, Yisong Yue

Robust and Private Learning of Halfspaces

Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen

Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization

Congliang Chen, Jiawei Zhang, Li Shen, Peilin Zhao, Zhiquan Luo

An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling

QIN DING, Cho-jui Hsieh, James Sharpnack

Q-learning with Logarithmic Regret

Kunhe Yang, Lin Yang, Simon Du

Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning

Ming Yin, Yu Bai, Yu-Xiang Wang

Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers

Lunjia Hu, Omer Reingold

Independent Innovation Analysis for Nonlinear Vector Autoregressive Process

Hiroshi Morioka, Hermanni Hälvä, Aapo Hyvärinen

Learning Complexity of Simulated Annealing

Avrim Blum, Chen Dan, Saeed Seddighin

Multitask Bandit Learning Through Heterogeneous Feedback Aggregation

Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel Riek, Kamalika Chaudhuri

On the Privacy Properties of GAN-generated Samples

Zinan Lin, Vyas Sekar, Giulia Fanti

A Variational Information Bottleneck Approach to Multi-Omics Data Integration

Changhee Lee, Mihaela van der Schaar

Understanding Gradient Clipping In Incremental Gradient Methods

Jiang Qian, Yuren Wu, Bojin Zhuang, Shaojun Wang, Jing Xiao

Identification of Matrix Joint Block Diagonalization

Yunfeng Cai, Ping Li

Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms

Yilun Zhou, Adithya Renduchintala, Xian Li, Sida Wang, Yashar Mehdad

Shadow Manifold Hamiltonian Monte Carlo

Chris van der Heide, Fred Roosta, Liam Hodgkinson, Dirk Kroese

Feedback Coding for Active Learning

Gregory Canal, Matthieu Bloch, Christopher Rozell

Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations.

Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath

Efficient Statistics for Sparse Graphical Models from Truncated Samples

Arnab Bhattacharyya, Rathin Desai, Sai Ganesh Nagarajan, Ioannis Panageas

Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes

Qi Lei, Sai Ganesh Nagarajan, Ioannis Panageas, Xiao Wang

Learning User Preferences in Non-Stationary Environments

Wasim Huleihel, Soumyabrata Pal, Ofer Shayevitz

Tractable contextual bandits beyond realizability

Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey

Amortized Bayesian Prototype Meta-learning: A New Probabilistic Meta-learning Approach to Few-shot Image Classification

Zhuo Sun, Jijie Wu, Xiaoxu Li, Wenming Yang, Jing-Hao Xue

On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression

Jeongyeol Kwon, nhật Hồ, Constantine Caramanis

Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits

Avishek Ghosh, Abishek Sankararaman, Ramchandran Kannan