Optimization
ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence Ben Dai
Empirical risk minimization (ERM) is a crucial framework that offers a general approach to handling a broad range of machine learning tasks.
Two-Stage Predict+Optimize for Mixed Integer Linear Programs with Unknown Parameters in Constraints
We also give a training algorithm usable for all mixed integer linear programs, vastly generalizing the applicability of the framework.
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods
Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems.
Optimal and Fair Encouragement Policy Evaluation and Learning
We study identification and improved/robust estimation under potential violations of positivity.
A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning
To make VFL practical, privacy security and communication efficiency should both be satisfied.
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm Qi Wang
Meta learning is a promising paradigm to enable skill transfer across tasks.
Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption
We study the infinite-horizon restless bandit problem with the average reward criterion, in both discrete-time and continuous-time settings.
