Search Results for author: Ming Yin

Found 15 papers, 1 papers with code

基于自动识别的委婉语历时性发展变化与社会共变研究(A Study on the Diachronic Development and Social Covariance of Euphemism Based on Automatic Recognition)

no code implementations CCL 2021 Chenlin Zhang, Mingwen Wang, Yiming Tan, Ming Yin, Xinyi Zhang

“本文主要以汉语委婉语作为研究对象, 基于大量人工标注, 借助机器学习有监督分类方法, 实现了较高精度的委婉语自动识别, 并基于此对1946年-2017年的《人民日报》中的委婉语历时变化发展情况进行量化统计分析。从大规模数据的角度探讨委婉语历时性发展变化、委婉语与社会之间的共变关系, 验证了语言的格雷什姆规律与更新规律。”

Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism

no code implementations11 Mar 2022 Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang

However, a precise understanding of the statistical limits with function representations, remains elusive, even when such a representation is linear.

Decision Making reinforcement-learning

Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost

no code implementations13 Feb 2022 Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang

In this paper, we propose a new algorithm based on stage-wise exploration and adaptive policy elimination that achieves a regret of $\widetilde{O}(\sqrt{H^4S^2AT})$ while requiring a switching cost of $O(HSA \log\log T)$.

reinforcement-learning

Towards Instance-Optimal Offline Reinforcement Learning with Pessimism

no code implementations NeurIPS 2021 Ming Yin, Yu-Xiang Wang

We study the offline reinforcement learning (offline RL) problem, where the goal is to learn a reward-maximizing policy in an unknown Markov Decision Process (MDP) using the data coming from a policy $\mu$.

Offline RL reinforcement-learning

Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings

no code implementations NeurIPS 2021 Ming Yin, Yu-Xiang Wang

This work studies the statistical limits of uniform convergence for offline policy evaluation (OPE) problems with model-based methods (for episodic MDP) and provides a unified framework towards optimal learning for several well-motivated offline tasks.

Offline RL

Near-Optimal Offline Reinforcement Learning via Double Variance Reduction

no code implementations NeurIPS 2021 Ming Yin, Yu Bai, Yu-Xiang Wang

Our main result shows that OPDVR provably identifies an $\epsilon$-optimal policy with $\widetilde{O}(H^2/d_m\epsilon^2)$ episodes of offline data in the finite-horizon stationary transition setting, where $H$ is the horizon length and $d_m$ is the minimal marginal state-action distribution induced by the behavior policy.

Offline RL reinforcement-learning

Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning

no code implementations7 Jul 2020 Ming Yin, Yu Bai, Yu-Xiang Wang

The problem of Offline Policy Evaluation (OPE) in Reinforcement Learning (RL) is a critical step towards applying RL in real-life applications.

Offline RL reinforcement-learning

Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning

no code implementations29 Jan 2020 Ming Yin, Yu-Xiang Wang

We consider the problem of off-policy evaluation for reinforcement learning, where the goal is to estimate the expected reward of a target policy $\pi$ using offline data collected by running a logging policy $\mu$.

reinforcement-learning

Shared Generative Latent Representation Learning for Multi-view Clustering

1 code implementation23 Jul 2019 Ming Yin, Weitian Huang, Junbin Gao

Clustering multi-view data has been a fundamental research topic in the computer vision community.

Representation Learning

Low-rank Multi-view Clustering in Third-Order Tensor Space

no code implementations30 Aug 2016 Ming Yin, Junbin Gao, Shengli Xie, Yi Guo

Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace.

Multi-view Subspace Clustering

Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices

no code implementations27 Jan 2016 Ming Yin, Shengli Xie, Yi Guo, Junbin Gao, Yun Zhang

Due to its promising classification performance, sparse representation based classification(SRC) algorithm has attracted great attention in the past few years.

General Classification Robust classification +1

Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds

no code implementations CVPR 2016 Ming Yin, Yi Guo, Junbin Gao, Zhaoshui He, Shengli Xie

Sparse subspace clustering (SSC), as one of the most successful subspace clustering methods, has achieved notable clustering accuracy in computer vision tasks.

Supervised learning of sparse context reconstruction coefficients for data representation and classification

no code implementations18 Aug 2015 Xuejie Liu, Jingbin Wang, Ming Yin, Benjamin Edwards, Peijuan Xu

Context of data points, which is usually defined as the other data points in a data set, has been found to play important roles in data representation and classification.

General Classification

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