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no code implementations • 29 Oct 2023 • Suraj Singireddy, Andre Beckus, George Atia, Sumit Jha, Alvaro Velasquez

Reinforcement learning (RL) is a powerful tool for finding optimal policies in sequential decision processes.

no code implementations • 17 May 2023 • Yue Wang, Alvaro Velasquez, George Atia, Ashley Prater-Bennette, Shaofeng Zou

Robust Markov decision processes (MDPs) address the challenge of model uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs.

no code implementations • 10 Jan 2023 • Ismail Alkhouri, Sumit Jha, Andre Beckus, George Atia, Alvaro Velasquez, Rickard Ewetz, Arvind Ramanathan, Susmit Jha

To measure the robustness of the predicted structures, we utilize (i) the root-mean-square deviation (RMSD) and (ii) the Global Distance Test (GDT) similarity measure between the predicted structure of the original sequence and the structure of its adversarially perturbed version.

no code implementations • 2 Jan 2023 • Yue Wang, Alvaro Velasquez, George Atia, Ashley Prater-Bennette, Shaofeng Zou

We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.

no code implementations • 9 Jul 2021 • Taylor Dohmen, Noah Topper, George Atia, Andre Beckus, Ashutosh Trivedi, Alvaro Velasquez

The success of reinforcement learning in typical settings is predicated on Markovian assumptions on the reward signal by which an agent learns optimal policies.

no code implementations • 5 Jun 2021 • Alvaro Velasquez, Ismail Alkhouri, Andre Beckus, Ashutosh Trivedi, George Atia

Given a Markov decision process (MDP) and a linear-time ($\omega$-regular or LTL) specification, the controller synthesis problem aims to compute the optimal policy that satisfies the specification.

no code implementations • 12 Mar 2020 • Mahlagha Sedghi, George Atia, Michael Georgiopoulos

The problem of representative selection amounts to sampling few informative exemplars from large datasets.

no code implementations • 26 Sep 2018 • Amir Emad Marvasti, Ehsan Emad Marvasti, George Atia, Hassan Foroosh

We propose a new way of thinking about deep neural networks, in which the linear and non-linear components of the network are naturally derived and justified in terms of principles in probability theory.

no code implementations • 25 May 2018 • Mostafa Rahmani, Andre Beckus, Adel Karimian, George Atia

Uniform random node sampling is shown to improve the computational complexity over clustering of the full graph when the cluster sizes are balanced.

no code implementations • 4 Dec 2017 • Mostafa Rahmani, George Atia

An important problem in training deep networks with high capacity is to ensure that the trained network works well when presented with new inputs outside the training dataset.

no code implementations • ICML 2017 • Mostafa Rahmani, George Atia

To the best of our knowledge, this is the first provable robust PCA algorithm that is simultaneously non-iterative, can tolerate a large number of outliers and is robust to linearly dependent outliers.

no code implementations • ICML 2017 • Mostafa Rahmani, George Atia

Remarkably, the proposed approach can provably yield exact clustering even when the subspaces have significant intersections.

no code implementations • 12 Jun 2017 • Mostafa Rahmani, George Atia

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

no code implementations • 9 May 2017 • Mostafa Rahmani, George Atia

Random column sampling is not guaranteed to yield data sketches that preserve the underlying structures of the data and may not sample sufficiently from less-populated data clusters.

no code implementations • 7 Feb 2017 • Mostafa Rahmani, George Atia

Our approach hinges on the sparse approximation of a sparsely corrupted column so that the sparse expansion of a column with respect to the other data points is used to distinguish a sparsely corrupted inlier column from an outlying data point.

no code implementations • 18 Nov 2016 • Mostafa Rahmani, George Atia

Conventional sampling techniques fall short of drawing descriptive sketches of the data when the data is grossly corrupted as such corruptions break the low rank structure required for them to perform satisfactorily.

no code implementations • 15 Sep 2016 • Mostafa Rahmani, George Atia

As inliers lie in a low dimensional subspace and are mostly correlated, an inlier is likely to have strong mutual coherence with a large number of data points.

no code implementations • 2 Dec 2015 • Mostafa Rahmani, George Atia

This paper presents a new approach dubbed Innovation Pursuit (iPursuit) to the problem of subspace clustering using a new geometrical idea whereby subspaces are identified based on their relative novelties.

no code implementations • 31 Oct 2015 • Alan Paris, George Atia, Azadeh Vosoughi, Stephen Berman

A new class of formal latent-variable stochastic processes called hidden quantum models (HQM's) is defined in order to clarify the theoretical foundations of ion channel signal processing.

no code implementations • 21 May 2015 • Mostafa Rahmani, George Atia

This paper explores and analyzes two randomized designs for robust Principal Component Analysis (PCA) employing low-dimensional data sketching.

no code implementations • 1 Feb 2015 • Mostafa Rahmani, George Atia

In this paper, a scalable subspace-pursuit approach that transforms the decomposition problem to a subspace learning problem is proposed.

no code implementations • 2 Apr 2013 • Cem Aksoylar, George Atia, Venkatesh Saligrama

These mutual information expressions unify conditions for both linear and nonlinear observations.

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