39 citations · 77 across the 10 of their papers we have counts for
8 papers · 1 filter
Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition
Liyu Chen, Haipeng Luo, Chen-Yu Wei
We study the stochastic shortest path problem with adversarial costs and known transition, and show that the minimax regret is and $\widetilde{O}…
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo +1
We develop several new algorithms for learning Markov Decision Processes in an infinite-horizon average-reward setting with linear function approximation. Using the optimism princi…
Linear Last-iterate Convergence in Constrained Saddle-point Optimization
Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang +1
Optimistic Gradient Descent Ascent (OGDA) and Optimistic Multiplicative Weights Update (OMWU) for saddle-point optimization have received growing attention due to their favorable l…
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei +1
We develop a new approach to obtaining high probability regret bounds for online learning with bandit feedback against an adaptive adversary. While existing approaches all require…
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret
Mehdi Jafarnia-Jahromi, Chen-Yu Wei, Rahul Jain +1
Recently, model-free reinforcement learning has attracted research attention due to its simplicity, memory and computation efficiency, and the flexibility to combine with function…
Federated Residual Learning
Alekh Agarwal, John Langford, Chen-Yu Wei
We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model. Using this new federated…