1 citations · 1 across the 7 of their papers we have counts for
7 papers
Representative Action Selection for Large Action Space: From Bandits to MDPs
Quan Zhou, Shie Mannor
We study the problem of selecting a small, representative action subset from an extremely large action space shared across a family of reinforcement learning (RL) environments -- a…
Explore and Establish Synergistic Effects Between Weight Pruning and Coreset Selection in Neural Network Training
Weilin Wan, Fan Yi, Weizhong Zhang +2
Modern deep neural networks rely heavily on massive model weights and training samples, incurring substantial computational costs. Weight pruning and coreset selection are two emer…
Uniform ergodicity and spectral gap of importance-tempered MCMC with heavy-tailed targets
Quan Zhou
We study the importance-tempered random-walk Metropolis (RWM) algorithm on , which targets the tempered density with and corrects…
Representative Action Selection for Large Action Space Bandit Families
Quan Zhou, Mark Kozdoba, Shie Mannor
We study the problem of selecting a subset from a large action space shared by a family of bandits. In many natural situations, while the nominal set of actions is large, actions a…
Optimisation Strategies for Ensuring Fairness in Machine Learning: With and Without Demographics
Quan Zhou
Ensuring fairness has emerged as one of the primary concerns in AI and its related algorithms. Over time, the field of machine learning fairness has evolved to address these issues…
Out-of-Distribution Detection using Neural Activation Prior
Weilin Wan, Weizhong Zhang, Quan Zhou +2
Out-of-distribution detection (OOD) is a crucial technique for deploying machine learning models in the real world to handle the unseen scenarios. In this paper, we first propose a…
Importance is Important: Generalized Markov Chain Importance Sampling Methods
Guanxun Li, Aaron Smith, Quan Zhou
We show that for any multiple-try Metropolis algorithm, one can always accept the proposal and evaluate the importance weight that is needed to correct for the bias without extra c…