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。”
no code implementations • 3 May 2022 • Maria Leonor Pacheco, Tunazzina Islam, Monal Mahajan, Andrey Shor, Ming Yin, Lyle Ungar, Dan Goldwasser
The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions.
no code implementations • 11 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.
no code implementations • 13 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 $O(HSA \log\log T)$.
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 $\mu$.
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.
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.
no code implementations • 7 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.
no code implementations • 29 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 $\mu$.
1 code implementation • 23 Jul 2019 • Ming Yin, Weitian Huang, Junbin Gao
Clustering multi-view data has been a fundamental research topic in the computer vision community.
no code implementations • 30 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.
no code implementations • 27 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.
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.
no code implementations • 18 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.
no code implementations • 30 Jun 2015 • Jing-Yan Wang, Yihua Zhou, Ming Yin, Shaochang Chen, Benjamin Edwards
In this objective, the reconstruction error is minimized and the coefficient spar- sity is encouraged.