Timezone: »
Many machine learning tasks that involve predicting an output response can be solved by training a weighted regression model. Unfortunately, the predictive power of this type of models may severely deteriorate under low sample sizes or under covariate perturbations. Reweighting the training samples has aroused as an effective mitigation strategy to these problems. In this paper, we propose a novel and coherent scheme for kernel-reweighted regression by reparametrizing the sample weights using a doubly non-negative matrix. When the weighting matrix is confined in an uncertainty set using either the log-determinant divergence or the Bures-Wasserstein distance, we show that the adversarially reweighted estimate can be solved efficiently using first-order methods. Numerical experiments show that our reweighting strategy delivers promising results on numerous datasets.
Author Information
Tam Le (Kyoto University)
Truyen Nguyen (University of Akron)
Makoto Yamada (Kyoto University / RIKEN AIP)
Jose Blanchet (Stanford University)
Viet Anh Nguyen (VinAI Artificial Intelligence Application and Research JSC)
More from the Same Authors
-
2022 : Minimax Optimal Kernel Operator Learning via Multilevel Training »
Jikai Jin · Yiping Lu · Jose Blanchet · Lexing Ying -
2022 : Synthetic Principle Component Design: Fast Covariate Balancing with Synthetic Controls »
Yiping Lu · Jiajin Li · Lexing Ying · Jose Blanchet -
2023 : Representation Learning for Extremes »
Ali Hasan · Yuting Ng · Jose Blanchet · Vahid Tarokh -
2023 : Accelerated Sampling of Rare Events using a Neural Network Bias Potential »
Xinru Hua · Rasool Ahmad · Jose Blanchet · Wei Cai -
2023 Poster: Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage »
Jose Blanchet · Miao Lu · Tong Zhang · Han Zhong -
2023 Poster: When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality »
Jose Blanchet · Haoxuan Chen · Yiping Lu · Lexing Ying -
2023 Poster: Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games »
Kyriakos Lotidis · Panayotis Mertikopoulos · Nicholas Bambos · Jose Blanchet -
2023 Poster: Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization »
Taoli Zheng · Linglingzhi Zhu · Anthony Man-Cho So · Jose Blanchet · Jiajin Li -
2022 Poster: Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent »
Yiping Lu · Jose Blanchet · Lexing Ying -
2022 Poster: Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints »
Jiajin Li · Sirui Lin · Jose Blanchet · Viet Anh Nguyen -
2021 : Statistical Numerical PDE : Fast Rate, Neural Scaling Law and When it’s Optimal »
Yiping Lu · Haoxuan Chen · Jianfeng Lu · Lexing Ying · Jose Blanchet -
2021 Poster: Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares »
Hiroaki Yamada · Makoto Yamada -
2021 Poster: Modified Frank Wolfe in Probability Space »
Carson Kent · Jiajin Li · Jose Blanchet · Peter W Glynn -
2020 Poster: Distributionally Robust Parametric Maximum Likelihood Estimation »
Viet Anh Nguyen · Xuhui Zhang · Jose Blanchet · Angelos Georghiou -
2020 Poster: Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality »
Nian Si · Jose Blanchet · Soumyadip Ghosh · Mark Squillante -
2020 Spotlight: Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality »
Nian Si · Jose Blanchet · Soumyadip Ghosh · Mark Squillante -
2020 Poster: Distributionally Robust Local Non-parametric Conditional Estimation »
Viet Anh Nguyen · Fan Zhang · Jose Blanchet · Erick Delage · Yinyu Ye -
2019 Poster: Learning in Generalized Linear Contextual Bandits with Stochastic Delays »
Zhengyuan Zhou · Renyuan Xu · Jose Blanchet -
2019 Spotlight: Learning in Generalized Linear Contextual Bandits with Stochastic Delays »
Zhengyuan Zhou · Renyuan Xu · Jose Blanchet -
2019 Poster: Online EXP3 Learning in Adversarial Bandits with Delayed Feedback »
Ilai Bistritz · Zhengyuan Zhou · Xi Chen · Nicholas Bambos · Jose Blanchet -
2019 Poster: Multivariate Distributionally Robust Convex Regression under Absolute Error Loss »
Jose Blanchet · Peter W Glynn · Jun Yan · Zhengqing Zhou -
2019 Poster: Kernel Stein Tests for Multiple Model Comparison »
Jen Ning Lim · Makoto Yamada · Bernhard Schölkopf · Wittawat Jitkrittum -
2019 Poster: Semi-Parametric Dynamic Contextual Pricing »
Virag Shah · Ramesh Johari · Jose Blanchet -
2019 Poster: Tree-Sliced Variants of Wasserstein Distances »
Tam Le · Makoto Yamada · Kenji Fukumizu · Marco Cuturi -
2018 Poster: Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams »
Tam Le · Makoto Yamada