Search Results for author: Yuheng Zhang

Found 11 papers, 3 papers with code

On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation

no code implementations22 Feb 2024 Yuheng Zhang, Nan Jiang

We study off-policy evaluation (OPE) in partially observable environments with complex observations, with the goal of developing estimators whose guarantee avoids exponential dependence on the horizon.

Off-policy evaluation

Efficient Contextual Bandits with Uninformed Feedback Graphs

no code implementations12 Feb 2024 Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro

Bandits with feedback graphs are powerful online learning models that interpolate between the full information and classic bandit problems, capturing many real-life applications.

Multi-Armed Bandits regression

A Theoretical Analysis of Nash Learning from Human Feedback under General KL-Regularized Preference

no code implementations11 Feb 2024 Chenlu Ye, Wei Xiong, Yuheng Zhang, Nan Jiang, Tong Zhang

In this work, we provide theoretical insights for a recently proposed learning paradigm, Nash learning from human feedback (NLHF), which considered a general preference model and formulated the alignment process as a game between two competitive LLMs.

FRAD: Front-Running Attacks Detection on Ethereum using Ternary Classification Model

no code implementations24 Nov 2023 Yuheng Zhang, Pin Liu, Guojun Wang, Peiqiang Li, Wanyi Gu, Houji Chen, Xuelei Liu, Jinyao Zhu

Front-running attacks, a unique form of security threat, pose significant challenges to the integrity of blockchain transactions.

Offline Learning in Markov Games with General Function Approximation

no code implementations6 Feb 2023 Yuheng Zhang, Yu Bai, Nan Jiang

We study offline multi-agent reinforcement learning (RL) in Markov games, where the goal is to learn an approximate equilibrium -- such as Nash equilibrium and (Coarse) Correlated Equilibrium -- from an offline dataset pre-collected from the game.

Multi-agent Reinforcement Learning Reinforcement Learning (RL)

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

Improved Algorithms for Neural Active Learning

1 code implementation2 Oct 2022 Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, Jingrui He

We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting.

Active Learning

Improving Robustness to Model Inversion Attacks via Mutual Information Regularization

2 code implementations11 Sep 2020 Tianhao Wang, Yuheng Zhang, Ruoxi Jia

This paper studies defense mechanisms against model inversion (MI) attacks -- a type of privacy attacks aimed at inferring information about the training data distribution given the access to a target machine learning model.

Convolutional Ordinal Regression Forest for Image Ordinal Estimation

no code implementations7 Aug 2020 Haiping Zhu, Hongming Shan, Yuheng Zhang, Lingfu Che, Xiaoyang Xu, Junping Zhang, Jianbo Shi, Fei-Yue Wang

We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships.

Age Estimation Binary Classification +1

The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks

1 code implementation CVPR 2020 Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, Dawn Song

This paper studies model-inversion attacks, in which the access to a model is abused to infer information about the training data.

Face Recognition regression

Ordinal Distribution Regression for Gait-based Age Estimation

no code implementations27 May 2019 Haiping Zhu, Yuheng Zhang, Guohao Li, Junping Zhang, Hongming Shan

This paper proposes an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation.

Age Estimation regression

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