27 citations · 41 across the 10 of their papers we have counts for
17 papers · 1 filter
Nearly Minimax Algorithms for Linear Bandits with Shared Representation
Jiaqi Yang, Qi Lei, Jason D. Lee +1
We give novel algorithms for multi-task and lifelong linear bandits with shared representation. Specifically, we consider the setting where we play linear bandits with dimensio…
Provable Hierarchy-Based Meta-Reinforcement Learning
Kurtland Chua, Qi Lei, Jason D. Lee
Hierarchical reinforcement learning (HRL) has seen widespread interest as an approach to tractable learning of complex modular behaviors. However, existing work either assume acces…
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
Baihe Huang, Kaixuan Huang, Sham M. Kakade +4
Bandit problems with linear or concave reward have been extensively studied, but relatively few works have studied bandits with non-concave reward. This work considers a large fami…
A Short Note on the Relationship of Information Gain and Eluder Dimension
Kaixuan Huang, Sham M. Kakade, Jason D. Lee +1
Eluder dimension and information gain are two widely used methods of complexity measures in bandit and reinforcement learning. Eluder dimension was originally proposed as a general…
Near-Optimal Linear Regression under Distribution Shift
Qi Lei, Wei Hu, Jason D. Lee
Transfer learning is essential when sufficient data comes from the source domain, with scarce labeled data from the target domain. We develop estimators that achieve minimax linear…
How Fine-Tuning Allows for Effective Meta-Learning
Kurtland Chua, Qi Lei, Jason D. Lee
Representation learning has been widely studied in the context of meta-learning, enabling rapid learning of new tasks through shared representations. Recent works such as MAML have…