Chung-Wei Lee | DeepAI

AI Chat

Featured Co-authors

Csaba Szepesvari
116 publications
Tuomas Sandholm
82 publications
Chi Jin
61 publications
Christian Kroer
60 publications
Haipeng Luo
58 publications
Tor Lattimore
57 publications
Gabriele Farina
47 publications
Yu-Chiang Frank Wang
41 publications
Chen-Yu Wei
33 publications
Yasin Abbasi-Yadkori
32 publications
Qinghua Liu
24 publications

∙ 06/22/2023

Context-lumpable stochastic bandits

We consider a contextual bandit problem with S contexts and A actions. I...

0 Chung-Wei Lee, et al. ∙

∙ 05/24/2023

Regret Matching+: (In)Stability and Fast Convergence in Games

Regret Matching+ (RM+) and its variants are important algorithms for sol...

0 Gabriele Farina, et al. ∙

∙ 02/23/2023

Practical Knowledge Distillation: Using DNNs to Beat DNNs

For tabular data sets, we explore data and model distillation, as well a...

0 Chung-Wei Lee, et al. ∙

∙ 08/31/2022

Clairvoyant Regret Minimization: Equivalence with Nemirovski's Conceptual Prox Method and Extension to General Convex Games

A recent paper by Piliouras et al. [2021, 2022] introduces an uncoupled ...

0 Gabriele Farina, et al. ∙

∙ 06/17/2022

Near-Optimal No-Regret Learning for General Convex Games

A recent line of work has established uncoupled learning dynamics such t...

0 Gabriele Farina, et al. ∙

∙ 04/25/2022

Uncoupled Learning Dynamics with O(log T) Swap Regret in Multiplayer Games

In this paper we establish efficient and uncoupled learning dynamics so ...

0 Ioannis Anagnostides, et al. ∙

∙ 02/01/2022

Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games

While extensive-form games (EFGs) can be converted into normal-form game...

0 Gabriele Farina, et al. ∙

∙ 07/18/2021

Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses

Policy optimization is a widely-used method in reinforcement learning. D...

0 Haipeng Luo, et al. ∙

∙ 06/27/2021

Last-iterate Convergence in Extensive-Form Games

Regret-based algorithms are highly efficient at finding approximate Nash...

0 Chung-Wei Lee, et al. ∙

∙ 02/11/2021

Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously

In this work, we develop linear bandit algorithms that automatically ada...

0 Chung-Wei Lee, et al. ∙

∙ 02/08/2021

Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games

We study infinite-horizon discounted two-player zero-sum Markov games, a...

0 Chen-Yu Wei, et al. ∙

∙ 06/16/2020

Linear Last-iterate Convergence for Matrix Games and Stochastic Games

Optimistic Gradient Descent Ascent (OGDA) algorithm for saddle-point opt...

0 Chung-Wei Lee, et al. ∙

∙ 06/14/2020

Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs

We develop a new approach to obtaining high probability regret bounds fo...

0 Chung-Wei Lee, et al. ∙

∙ 02/02/2020

A Closer Look at Small-loss Bounds for Bandits with Graph Feedback

We study small-loss bounds for the adversarial multi-armed bandits probl...

0 Chung-Wei Lee, et al. ∙

∙ 02/03/2019

A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free

We propose the first contextual bandit algorithm that is parameter-free,...

0 Yifang Chen, et al. ∙

∙ 11/17/2017

Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

In this paper, we propose a novel deep learning architecture for multi-l...

0 Chung-Wei Lee, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world