Search Results for author: Mengxiao Zhang

Found 17 papers, 4 papers with code

A Survey of Data Pricing for Data Marketplaces

no code implementations7 Mar 2023 Mengxiao Zhang, Fernando Beltran, Jiamou Liu

Data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data.

No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution

no code implementations23 Oct 2022 Mengxiao Zhang, Shi Chen, Haipeng Luo, Yingfei Wang

Supply chain management (SCM) has been recognized as an important discipline with applications to many industries, where the two-echelon stochastic inventory model, involving one downstream retailer and one upstream supplier, plays a fundamental role for developing firms' SCM strategies.


Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs

no code implementations4 Oct 2022 Haipeng Luo, Hanghang Tong, Mengxiao Zhang, Yuheng Zhang

For general strongly observable graphs, we develop an algorithm that achieves the optimal regret $\widetilde{\mathcal{O}}((\sum_{t=1}^T\alpha_t)^{1/2}+\max_{t\in[T]}\alpha_t)$ with high probability, where $\alpha_t$ is the independence number of the feedback graph at round $t$.

Multi-Armed Bandits

SPAIC: A Spike-based Artificial Intelligence Computing Framework

1 code implementation26 Jul 2022 Chaofei Hong, Mengwen Yuan, Mengxiao Zhang, Xiao Wang, Chegnjun Zhang, Jiaxin Wang, Gang Pan, Zhaohui Wu, Huajin Tang

In this work, we present a Python based spiking neural network (SNN) simulation and training framework, aka SPAIC that aims to support brain-inspired model and algorithm researches integrated with features from both deep learning and neuroscience.

Adaptive Bandit Convex Optimization with Heterogeneous Curvature

no code implementations12 Feb 2022 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 each of them.

Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits

no code implementations12 Feb 2022 Haipeng Luo, Mengxiao Zhang, Peng Zhao, Zhi-Hua Zhou

The CORRAL algorithm of Agarwal et al. (2017) and its variants (Foster et al., 2020a) achieve this goal with a regret overhead of order $\widetilde{O}(\sqrt{MT})$ where $M$ is the number of base algorithms and $T$ is the time horizon.

No-Regret Learning in Time-Varying Zero-Sum Games

no code implementations30 Jan 2022 Mengxiao Zhang, Peng Zhao, Haipeng Luo, Zhi-Hua Zhou

Learning from repeated play in a fixed two-player zero-sum game is a classic problem in game theory and online learning.

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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