Ming Yin | DeepAI

DeepAI

Featured Co-authors

Svetha Venkatesh
116 publications
Yu-Xiang Wang
72 publications
Mengdi Wang
72 publications
Sunil Gupta
48 publications
Junbin Gao
45 publications
Yu Bai
40 publications
Shuai Ma
36 publications
Raman Arora
34 publications
Xiaojuan Ma
34 publications
Yi Guo
25 publications
Dan Goldwasser
25 publications

∙ 01/14/2023

Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making

In AI-assisted decision-making, it is critical for human decision-makers...

Shuai Ma, et al. ∙

∙ 11/23/2022

On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation

Sample-efficient offline reinforcement learning (RL) with linear functio...

Thanh Nguyen-Tang, et al. ∙

∙ 10/03/2022

Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient

Offline reinforcement learning, which aims at optimizing sequential deci...

Ming Yin, et al. ∙

∙ 06/13/2022

Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks

Quantized neural networks have drawn a lot of attention as they reduce t...

Kaiqi Zhang, et al. ∙

∙ 06/10/2022

Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality

Goal-oriented Reinforcement Learning, where the agent needs to reach the...

Ming Yin, et al. ∙

∙ 05/03/2022

A Holistic Framework for Analyzing the COVID-19 Vaccine Debate

The Covid-19 pandemic has led to infodemic of low quality information le...

Maria Leonor Pacheco, et al. ∙

∙ 03/11/2022

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

Offline reinforcement learning, which seeks to utilize offline/historica...

Ming Yin, et al. ∙

∙ 02/13/2022

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

We study the problem of reinforcement learning (RL) with low (policy) sw...

Dan Qiao, et al. ∙

∙ 10/17/2021

Towards Instance-Optimal Offline Reinforcement Learning with Pessimism

We study the offline reinforcement learning (offline RL) problem, where ...

Ming Yin, et al. ∙

∙ 05/13/2021

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

This work studies the statistical limits of uniform convergence for offl...

Ming Yin, et al. ∙

∙ 02/02/2021

Near-Optimal Offline Reinforcement Learning via Double Variance Reduction

We consider the problem of offline reinforcement learning (RL) – a well-...

Ming Yin, et al. ∙

∙ 07/07/2020

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

The Off-Policy Evaluation aims at estimating the performance of target p...

Ming Yin, et al. ∙

∙ 01/29/2020

Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning

We consider the problem of off-policy evaluation for reinforcement learn...

Ming Yin, et al. ∙

∙ 07/23/2019

Shared Generative Latent Representation Learning for Multi-view Clustering

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

Ming Yin, et al. ∙

∙ 10/23/2018

Working in Pairs: Understanding the Effects of Worker Interactions in Crowdwork

Crowdsourcing has gained popularity as a tool to harness human brain pow...

Chien-Ju Ho, et al. ∙

∙ 08/30/2016

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

The plenty information from multiple views data as well as the complemen...

Ming Yin, et al. ∙

∙ 01/27/2016

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

Due to its promising classification performance, sparse representation b...

Ming Yin, et al. ∙

∙ 01/04/2016

Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds

Sparse subspace clustering (SSC), as one of the most successful subspace...

Ming Yin, et al. ∙

∙ 06/30/2015

Representing data by sparse combination of contextual data points for classification

In this paper, we study the problem of using contextual da- ta points of...

Jingyan Wang, et al. ∙