1 citations · 4 across the 9 of their papers we have counts for
9 papers
Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processes
Yu Chen, Yuhao Liu, Jiatai Huang +2
We investigate episodic Markov Decision Processes with heavy-tailed losses (HTMDPs). Existing approaches for HTMDPs are conservative in stochastic environments and lack adaptivity…
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs
Yu Chen, Jiatai Huang, Yan Dai +1
In this paper, we present a novel algorithm, uniINF, for the Heavy-Tailed Multi-Armed Bandits (HTMAB) problem, demonstrating robustness and adaptability in both stochastic and adve…
Improving Generalization and Data Efficiency with Diffusion in Offline Multi-agent RL
Zhuoran Li, Ling Pan, Jiatai Huang +1
We present a novel Diffusion Offline Multi-agent Model (DOM2) for offline Multi-Agent Reinforcement Learning (MARL). Different from existing algorithms that rely mainly on conserva…
Queue Scheduling with Adversarial Bandit Learning
Jiatai Huang, Leana Golubchik, Longbo Huang
In this paper, we study scheduling of a queueing system with zero knowledge of instantaneous network conditions. We consider a one-hop single-server queueing system consisting of $…
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning
Jiatai Huang, Yan Dai, Longbo Huang
We propose Banker Online Mirror Descent (Banker-OMD), a novel framework generalizing the classical Online Mirror Descent (OMD) technique in the online learning literature. The Bank…
RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
Yiqin Tan, Pihe Hu, Ling Pan +2
Training deep reinforcement learning (DRL) models usually requires high computation costs. Therefore, compressing DRL models possesses immense potential for training acceleration a…
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits
Jiatai Huang, Yan Dai, Longbo Huang
In this paper, we generalize the concept of heavy-tailed multi-armed bandits to adversarial environments, and develop robust best-of-both-worlds algorithms for heavy-tailed multi-a…
Scale-Free Adversarial Multi-Armed Bandit with Arbitrary Feedback Delays
Jiatai Huang, Yan Dai, Longbo Huang
We consider the Scale-Free Adversarial Multi-Armed Bandit (MAB) problem with unrestricted feedback delays. In contrast to the standard assumption that all losses are -bounde…
Banker Online Mirror Descent
Jiatai Huang, Longbo Huang
We propose Banker-OMD, a novel framework generalizing the classical Online Mirror Descent (OMD) technique in online learning algorithm design. Banker-OMD allows algorithms to robus…