39 citations · 79 across the 20 of their papers we have counts for
4 papers · 1 filter
Fair Contextual Multi-Armed Bandits: Theory and Experiments
Yifang Chen, Alex Cuellar, Haipeng Luo +3
When an AI system interacts with multiple users, it frequently needs to make allocation decisions. For instance, a virtual agent decides whom to pay attention to in a group setting…
Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization
Abhishek Roy, Yifang Chen, Krishnakumar Balasubramanian +1
Saddle-point optimization problems are an important class of optimization problems with applications to game theory, multi-agent reinforcement learning and machine learning. A majo…
Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates
Houston Claure, Yifang Chen, Jignesh Modi +2
How should a robot that collaborates with multiple people decide upon the distribution of resources (e.g. social attention, or parts needed for an assembly)? People are uniquely at…
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free
Yifang Chen, Chung-Wei Lee, Haipeng Luo +1
We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal in terms of dynamic regret. Specifically, our algorithm achieves dynamic regret $\ma…