Search Results for author: Mengxiao Zhang

Found 9 papers, 3 papers with code

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

no code implementations8 Feb 2021 Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo

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.

Linear Last-iterate Convergence in Constrained Saddle-point Optimization

1 code implementation ICLR 2021 Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, Haipeng Luo

Specifically, for OMWU in bilinear games over the simplex, we show that when the equilibrium is unique, linear last-iterate convergence is achieved with a learning rate whose value is set to a universal constant, improving the result of (Daskalakis & Panageas, 2019b) under the same assumption.

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

no code implementations NeurIPS 2020 Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang

We develop a new approach to obtaining high probability regret bounds for online learning with bandit feedback against an adaptive adversary.

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

no code implementations2 Feb 2020 Chung-Wei Lee, Haipeng Luo, Mengxiao Zhang

We study small-loss bounds for adversarial multi-armed bandits with graph feedback, that is, adaptive regret bounds that depend on the loss of the best arm or related quantities, instead of the total number of rounds.

Multi-Armed Bandits

Defective Convolutional Networks

1 code implementation19 Nov 2019 Tiange Luo, Tianle Cai, Mengxiao Zhang, Siyu Chen, Di He, Li-Wei Wang

Robustness of convolutional neural networks (CNNs) has gained in importance on account of adversarial examples, i. e., inputs added as well-designed perturbations that are imperceptible to humans but can cause the model to predict incorrectly.

The Local Dimension of Deep Manifold

no code implementations5 Nov 2017 Mengxiao Zhang, Wangquan Wu, Yanren Zhang, Kun He, Tao Yu, Huan Long, John E. Hopcroft

Our results show that the dimensions of different categories are close to each other and decline quickly along the convolutional layers and fully connected layers.

Randomness in Deconvolutional Networks for Visual Representation

no code implementations2 Apr 2017 Kun He, Jingbo Wang, Haochuan Li, Yao Shu, Mengxiao Zhang, Man Zhu, Li-Wei Wang, John E. Hopcroft

Toward a deeper understanding on the inner work of deep neural networks, we investigate CNN (convolutional neural network) using DCN (deconvolutional network) and randomization technique, and gain new insights for the intrinsic property of this network architecture.

General Classification Image Reconstruction

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