1 citations · 1 across the 12 of their papers we have counts for
4 papers · 1 filter
A Tale of Two Variances: When Single-Seed Benchmarks Fail in Bayesian Deep Learning
Qishi Zhan, Minxuan Hu, Liang He +2
In limited-data settings, a single endpoint mean of an evaluation metric such as the Continuous Ranked Probability Score (CRPS) is itself a random variable, yet it is routinely rep…
Unstable Rankings in Bayesian Deep Learning Evaluation
Qishi Zhan, Minxuan Hu, Guansu Wang +2
Standard evaluations of Bayesian deep learning methods assume that metric estimates are reliable, but we show this assumption fails under data scarcity. Method rankings are not onl…
Discovering What You Can Control: Interventional Boundary Discovery for Reinforcement Learning
Jiaxin Liu, Anzhe Cheng, Paul Bogdan
When an RL agent's observations contain distractors driven by the same confounders as its true state, observational data alone cannot identify which dimensions the agent controls.…
Fair Algorithms with Probing for Multi-Agent Multi-Armed Bandits
Tianyi Xu, Jiaxin Liu, Nicholas Mattei +1
We propose a multi-agent multi-armed bandit (MA-MAB) framework aimed at ensuring fair outcomes across agents while maximizing overall system performance. A key challenge in this se…