4 papers
DAL: A Practical Prior-Free Black-Box Framework for Piecewise Stationary Bandits
Argyrios Gerogiannis, Yu-Han Huang, Subhonmesh Bose +1
We introduce a practical, black-box framework termed Detection Augmented Learning (DAL) for the problem of piecewise stationary bandits without knowledge of the underlying non-stat…
LEAF: Growing Trees Without Branching for Speech-Aware Large Language Model Post-Training
Argyrios Gerogiannis, Yekaterina Yegorova, Mark Hasegawa-Johnson +1
State-of-the-art GRPO-style methods for speech-aware large language model post-training suffer from coarse credit assignment, broadcasting the same terminal-reward advantage to eve…
DARLING: Detection Augmented Reinforcement Learning with Non-Stationary Guarantees
Argyrios Gerogiannis, Yu-Han Huang, Venugopal V. Veeravalli
We study model-free reinforcement learning (RL) in non-stationary finite-horizon episodic Markov decision processes (MDPs) without prior knowledge of the non-stationarity. We focus…
Learning Where to Look: UCB-Driven Controlled Sensing for Quickest Change Detection
Yu-Han Huang, Argyrios Gerogiannis, Subhonmesh Bose +1
We study the multichannel quickest change detection problem with bandit feedback and controlled sensing, in which an agent sequentially selects one of the data streams to observe a…