Search Results for author: Bahman Gharesifard

Found 12 papers, 1 papers with code

Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens

no code implementations16 Apr 2024 Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard

We provide the first known algorithm that provably achieves $\varepsilon$-optimality within $\widetilde{\mathcal{O}}(1/\varepsilon)$ function evaluations for the discounted discrete-time LQR problem with unknown parameters, without relying on two-point gradient estimates.

A Unifying Generator Loss Function for Generative Adversarial Networks

no code implementations14 Aug 2023 Justin Veiner, Fady Alajaji, Bahman Gharesifard

A unifying $\alpha$-parametrized generator loss function is introduced for a dual-objective generative adversarial network (GAN), which uses a canonical (or classical) discriminator loss function such as the one in the original GAN (VanillaGAN) system.

Generative Adversarial Network

Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and Rényi Measures

1 code implementation20 Jun 2022 Adam Gronowski, William Paul, Fady Alajaji, Bahman Gharesifard, Philippe Burlina

Designing machine learning algorithms that are accurate yet fair, not discriminating based on any sensitive attribute, is of paramount importance for society to accept AI for critical applications.

Attribute Fairness +2

Event-Triggered Control for Nonlinear Time-Delay Systems

no code implementations8 Jun 2022 Kexue Zhang, Bahman Gharesifard, Elena Braverman

This article studies the event-triggered control problem of general nonlinear systems with time delay.

Neural ODE Control for Trajectory Approximation of Continuity Equation

no code implementations18 May 2022 Karthik Elamvazhuthi, Bahman Gharesifard, Andrea Bertozzi, Stanley Osher

As a corollary to this result, we establish that the continuity equation of the neural ODE is approximately controllable on the set of compactly supported probability measures that are absolutely continuous with respect to the Lebesgue measure.

Renyi Fair Information Bottleneck for Image Classification

no code implementations9 Mar 2022 Adam Gronowski, William Paul, Fady Alajaji, Bahman Gharesifard, Philippe Burlina

We develop a novel method for ensuring fairness in machine learning which we term as the Renyi Fair Information Bottleneck (RFIB).

Classification Fairness +1

A Small Gain Analysis of Single Timescale Actor Critic

no code implementations4 Mar 2022 Alex Olshevsky, Bahman Gharesifard

We consider a version of actor-critic which uses proportional step-sizes and only one critic update with a single sample from the stationary distribution per actor step.

Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory

no code implementations ICLR 2021 Paulo Tabuada, Bahman Gharesifard

In this paper, we explain the universal approximation capabilities of deep residual neural networks through geometric nonlinear control.

Riccati updates for online linear quadratic control

no code implementations L4DC 2020 Mohammad Akbari, Bahman Gharesifard, Tamas Linder

We study an online setting of the linear quadratic Gaussian optimal control problem on a sequence of cost functions, where similar to classical online optimization, the future decisions are made by only knowing the cost in hindsight.

Least $k$th-Order and Rényi Generative Adversarial Networks

no code implementations3 Jun 2020 Himesh Bhatia, William Paul, Fady Alajaji, Bahman Gharesifard, Philippe Burlina

Another novel GAN generator loss function is next proposed in terms of R\'{e}nyi cross-entropy functionals with order $\alpha >0$, $\alpha\neq 1$.


Hybrid Event-Triggered and Impulsive Control for Time-Delay Systems

no code implementations5 Dec 2019 Kexue Zhang, Bahman Gharesifard

In this sense, the proposed algorithm is a hybrid impulsive and event-triggered strategy.

Distributed Optimization Under Adversarial Nodes

no code implementations29 Jun 2016 Shreyas Sundaram, Bahman Gharesifard

We then propose a resilient distributed optimization algorithm that guarantees that the non-adversarial nodes converge to the convex hull of the minimizers of their local functions under certain conditions on the graph topology, regardless of the actions of a certain number of adversarial nodes.

Systems and Control Distributed, Parallel, and Cluster Computing Optimization and Control

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