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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...
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01/10/2023
On the Robustness of AlphaFold: A COVID-19 Case Study
Protein folding neural networks (PFNNs) such as AlphaFold predict remark...
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01/02/2023
Robust Average-Reward Markov Decision Processes
In robust Markov decision processes (MDPs), the uncertainty in the trans...
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08/05/2021
BOSS: Bidirectional One-Shot Synthesis of Adversarial Examples
The design of additive imperceptible perturbations to the inputs of deep...
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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...
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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...
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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...
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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...
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05/25/2018
Randomized Robust Matrix Completion for the Community Detection Problem
This paper focuses on the unsupervised clustering of large partially obs...
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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...
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12/04/2017
Data Dropout in Arbitrary Basis for Deep Network Regularization
An important problem in training deep networks with high capacity is to ...
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06/12/2017
Subspace Clustering via Optimal Direction Search
This letter presents a new spectral-clustering-based approach to the sub...
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05/09/2017
Spatial Random Sampling: A Structure-Preserving Data Sketching Tool
Random column sampling is not guaranteed to yield data sketches that pre...
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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 =...
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11/18/2016
Robust and Scalable Column/Row Sampling from Corrupted Big Data
Conventional sampling techniques fall short of drawing descriptive sketc...
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09/15/2016
Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis
This paper presents a remarkably simple, yet powerful, algorithm termed ...
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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...
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05/21/2015
Randomized Robust Subspace Recovery for High Dimensional Data Matrices
This paper explores and analyzes two randomized designs for robust Princ...
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02/01/2015
High Dimensional Low Rank plus Sparse Matrix Decomposition
This paper is concerned with the problem of low rank plus sparse matrix ...
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04/02/2013