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Yue Wang
263 publications
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21 publications
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∙ 05/17/2023

Model-Free Robust Average-Reward Reinforcement Learning

Robust Markov decision processes (MDPs) address the challenge of model u...

0 Yue Wang, et al. ∙

∙ 01/10/2023

On the Robustness of AlphaFold: A COVID-19 Case Study

Protein folding neural networks (PFNNs) such as AlphaFold predict remark...

0 Ismail Alkhouri, et al. ∙

∙ 01/02/2023

Robust Average-Reward Markov Decision Processes

In robust Markov decision processes (MDPs), the uncertainty in the trans...

0 Yue Wang, et al. ∙

∙ 08/05/2021

BOSS: Bidirectional One-Shot Synthesis of Adversarial Examples

The design of additive imperceptible perturbations to the inputs of deep...

5 Ismail Alkhouri, et al. ∙

∙ 07/09/2021

Learning Probabilistic Reward Machines from Non-Markovian Stochastic Reward Processes

The success of reinforcement learning in typical settings is, in part, p...

0 Alvaro Velasquez, et al. ∙

∙ 06/05/2021

Controller Synthesis for Omega-Regular and Steady-State Specifications

Given a Markov decision process (MDP) and a linear-time (ω-regular or LT...

0 Alvaro Velasquez, et al. ∙

∙ 03/12/2020

A Multi-criteria Approach for Fast and Outlier-aware Representative Selection from Manifolds

The problem of representative selection amounts to sampling few informat...

0 Mahlagha Sedghi, et al. ∙

∙ 09/26/2018

Rediscovering Deep Neural Networks in Finite-State Distributions

We propose a new way of thinking about deep neural networks, in which th...

4 Amir Emad Marvasti, et al. ∙

∙ 05/25/2018

Randomized Robust Matrix Completion for the Community Detection Problem

This paper focuses on the unsupervised clustering of large partially obs...

0 Mostafa Rahmani, et al. ∙

∙ 03/14/2018

A Game-Theoretic Framework for the Virtual Machines Migration Timing Problem

In a multi-tenant cloud, a number of Virtual Machines (VMs) are collocat...

0 Ahmed H. Anwar, et al. ∙

∙ 12/04/2017

Data Dropout in Arbitrary Basis for Deep Network Regularization

An important problem in training deep networks with high capacity is to ...

0 Mostafa Rahmani, et al. ∙

∙ 06/12/2017

Subspace Clustering via Optimal Direction Search

This letter presents a new spectral-clustering-based approach to the sub...

0 Mostafa Rahmani, et al. ∙

∙ 05/09/2017

Spatial Random Sampling: A Structure-Preserving Data Sketching Tool

Random column sampling is not guaranteed to yield data sketches that pre...

0 Mostafa Rahmani, et al. ∙

∙ 02/07/2017

Low Rank Matrix Recovery with Simultaneous Presence of Outliers and Sparse Corruption

We study a data model in which the data matrix D can be expressed as D =...

0 Mostafa Rahmani, et al. ∙

∙ 11/18/2016

Robust and Scalable Column/Row Sampling from Corrupted Big Data

Conventional sampling techniques fall short of drawing descriptive sketc...

0 Mostafa Rahmani, et al. ∙

∙ 09/15/2016

Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis

This paper presents a remarkably simple, yet powerful, algorithm termed ...

0 Mostafa Rahmani, et al. ∙

∙ 12/02/2015

Innovation Pursuit: A New Approach to Subspace Clustering

In subspace clustering, a group of data points belonging to a union of s...

0 Mostafa Rahmani, et al. ∙

∙ 05/21/2015

Randomized Robust Subspace Recovery for High Dimensional Data Matrices

This paper explores and analyzes two randomized designs for robust Princ...

0 Mostafa Rahmani, et al. ∙

∙ 02/01/2015

High Dimensional Low Rank plus Sparse Matrix Decomposition

This paper is concerned with the problem of low rank plus sparse matrix ...

0 Mostafa Rahmani, et al. ∙

∙ 04/02/2013

Sparse Signal Processing with Linear and Nonlinear Observations: A Unified Shannon-Theoretic Approach

We derive fundamental sample complexity bounds for recovering sparse and...

0 Cem Aksoylar, et al. ∙

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