16 citations · 21 across the 5 of their papers we have counts for
11 papers
Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits
Haipeng Luo, Mengxiao Zhang, Peng Zhao +1
We consider the problem of combining and learning over a set of adversarial bandit algorithms with the goal of adaptively tracking the best one on the fly. The CORRAL algorithm of…
Adaptive Bandit Convex Optimization with Heterogeneous Curvature
Haipeng Luo, Mengxiao Zhang, Peng Zhao
We consider the problem of adversarial bandit convex optimization, that is, online learning over a sequence of arbitrary convex loss functions with only one function evaluation for…
No-Regret Learning in Time-Varying Zero-Sum Games
Mengxiao Zhang, Peng Zhao, Haipeng Luo +1
Learning from repeated play in a fixed two-player zero-sum game is a classic problem in game theory and online learning. We consider a variant of this problem where the game payoff…
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei +2
In this work, we develop linear bandit algorithms that automatically adapt to different environments. By plugging a novel loss estimator into the optimization problem that characte…
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang +1
We study infinite-horizon discounted two-player zero-sum Markov games, and develop a decentralized algorithm that provably converges to the set of Nash equilibria under self-play.…
The Relevance of Classic Fuzz Testing: Have We Solved This One?
Barton P. Miller, Mengxiao Zhang, Elisa R. Heymann
As fuzz testing has passed its 30th anniversary, and in the face of the incredible progress in fuzz testing techniques and tools, the question arises if the classic, basic fuzz tec…