b'Ali Jadbabaie'

research

∙ 04/03/2022

Byzantine-Robust Federated Linear Bandits

In this paper, we study a linear bandit optimization problem in a federa...

Ali Jadbabaie, et al. ∙

research

∙ 03/29/2022

Current Implicit Policies May Not Eradicate COVID-19

Successful predictive modeling of epidemics requires an understanding of...

Ali Jadbabaie, et al. ∙

research

∙ 01/07/2022

Unifying Epidemic Models with Mixtures

The COVID-19 pandemic has emphasized the need for a robust understanding...

Arnab Sarker, et al. ∙

research

∙ 01/06/2022

Federated Optimization of Smooth Loss Functions

In this work, we study empirical risk minimization (ERM) within a federa...

Ali Jadbabaie, et al. ∙

research

∙ 12/29/2021

Time varying regression with hidden linear dynamics

We revisit a model for time-varying linear regression that assumes the u...

Ali Jadbabaie, et al. ∙

research

∙ 10/12/2021

On Convergence of Training Loss Without Reaching Stationary Points

It is a well-known fact that nonconvex optimization is computationally i...

Jingzhao Zhang, et al. ∙

research

∙ 07/25/2021

In Defense of Fluid Democracy

Fluid democracy is a voting paradigm that allows voters to choose betwee...

Daniel Halpern, et al. ∙

research

∙ 04/18/2021

Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization

We provide a first-order oracle complexity lower bound for finding stati...

Haochuan Li, et al. ∙

research

∙ 03/12/2021

Can Single-Shuffle SGD be Better than Reshuffling SGD and GD?

We propose matrix norm inequalities that extend the Recht-Ré (2012) conj...

Chulhee Yun, et al. ∙

research

∙ 12/04/2020

Network Group Testing

We consider the problem of identifying infected individuals in a populat...

Paolo Bertolotti, et al. ∙

research

∙ 11/20/2020

A General Framework for Distributed Inference with Uncertain Models

This paper studies the problem of distributed classification with a netw...

James Z. Hare, et al. ∙

research

∙ 11/04/2020

Gradient-Based Empirical Risk Minimization using Local Polynomial Regression

In this paper, we consider the problem of empirical risk minimization (E...

Ali Jadbabaie, et al. ∙

research

∙ 07/07/2020

A Distributed Cubic-Regularized Newton Method for Smooth Convex Optimization over Networks

We propose a distributed, cubic-regularized Newton method for large-scal...

César A. Uribe, et al. ∙

research

∙ 06/18/2020

GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models

Generative adversarial networks (GANs) learn the distribution of observe...

Farzan Farnia, et al. ∙

research

∙ 06/16/2020

Robust Federated Learning: The Case of Affine Distribution Shifts

Federated learning is a distributed paradigm that aims at training model...

Amirhossein Reisizadeh, et al. ∙

research

∙ 06/15/2020

Estimation of Skill Distributions

In this paper, we study the problem of learning the skill distribution o...

Ali Jadbabaie, et al. ∙

research

∙ 06/08/2020

Stochastic Optimization with Non-stationary Noise

We investigate stochastic optimization problems under relaxed assumption...

Jingzhao Zhang, et al. ∙

research

∙ 05/06/2020

A Separation Theorem for Joint Sensor and Actuator Scheduling with Guaranteed Performance Bounds

We study the problem of jointly designing a sparse sensor and actuator s...

Milad Siami, et al. ∙

research

∙ 02/10/2020

On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions

We provide the first non-asymptotic analysis for finding stationary poin...

Jingzhao Zhang, et al. ∙

research

∙ 12/17/2019

Competitive Contagion with Sparse Seeding

This paper studies a strategic model of marketing and product diffusion ...

Milad Siami, et al. ∙

research

∙ 10/24/2019

Non-Bayesian Social Learning with Gaussian Uncertain Models

Non-Bayesian social learning theory provides a framework for distributed...

James Z. Hare, et al. ∙

research

∙ 09/28/2019

FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

Federated learning is a new distributed machine learning approach, where...

Amirhossein Reisizadeh, et al. ∙

research

∙ 09/25/2019

Robust and Adaptive Sequential Submodular Optimization

Emerging applications of control, estimation, and machine learning, rang...

Vasileios Tzoumas, et al. ∙

research

∙ 09/10/2019

Non-Bayesian Social Learning with Uncertain Models over Time-Varying Directed Graphs

We study the problem of non-Bayesian social learning with uncertain mode...

César A. Uribe, et al. ∙

research

∙ 09/09/2019

Non-Bayesian Social Learning with Uncertain Models

Non-Bayesian social learning theory provides a framework that models dis...

James Z. Hare, et al. ∙

research

∙ 07/09/2019

Are deep ResNets provably better than linear predictors?

Recently, a residual network (ResNet) with a single residual block has b...

Chulhee Yun, et al. ∙

research

∙ 05/28/2019

Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition

We provide a theoretical explanation for the fast convergence of gradien...

Jingzhao Zhang, et al. ∙

research

∙ 12/24/2018

On Increasing Self-Confidence in Non-Bayesian Social Learning over Time-Varying Directed Graphs

We study the convergence of the log-linear non-Bayesian social learning ...

César A. Uribe, et al. ∙

research

∙ 10/17/2018

Finite sample expressive power of small-width ReLU networks

We study universal finite sample expressivity of neural networks, define...

Chulhee Yun, et al. ∙

research

∙ 09/28/2018

Efficiently testing local optimality and escaping saddles for ReLU networks

We provide a theoretical algorithm for checking local optimality and esc...

Chulhee Yun, et al. ∙

research

∙ 09/06/2018

Escaping Saddle Points in Constrained Optimization

In this paper, we focus on escaping from saddle points in smooth nonconv...

Aryan Mokhtari, et al. ∙

research

∙ 09/05/2018

Blind Community Detection from Low-rank Excitations of a Graph Filter

This paper considers a novel framework to detect communities in a graph ...

Hoi-To Wai, et al. ∙

research

∙ 09/04/2018

Reasoning in Bayesian Opinion Exchange Networks Is PSPACE-Hard

We study the Bayesian model of opinion exchange of fully rational agents...

Jan Hązła, et al. ∙

research

∙ 07/13/2018

Random Walks on Simplicial Complexes and the normalized Hodge Laplacian

Modeling complex systems and data with graphs has been a mainstay of the...

Michael T. Schaub, et al. ∙

research

∙ 05/01/2018

Direct Runge-Kutta Discretization Achieves Acceleration

We study gradient-based optimization methods obtained by directly discre...

Jingzhao Zhang, et al. ∙

research

∙ 04/02/2018

Resilient Non-Submodular Maximization over Matroid Constraints

Applications in control, robotics, and optimization motivate the design ...

Vasileios Tzoumas, et al. ∙

research

∙ 03/21/2018

Resilient Monotone Sequential Maximization

Applications in machine learning, optimization, and control require the ...

Vasileios Tzoumas, et al. ∙

research

∙ 02/23/2018

LQG Control and Sensing Co-design

Linear-Quadratic-Gaussian (LQG) control is concerned with the design of ...

Vasileios Tzoumas, et al. ∙

research

∙ 02/23/2018

Control and Sensing Co-design

Linear-Quadratic-Gaussian (LQG) control is concerned with the design of ...

Vasileios Tzoumas, et al. ∙

research

∙ 02/20/2018

Simplicial Closure and Higher-order Link Prediction

Networks provide a powerful formalism for modeling complex systems, by r...

Austin R. Benson, et al. ∙

research

∙ 02/10/2018

A Critical View of Global Optimality in Deep Learning

We investigate the loss surface of deep linear and nonlinear neural netw...

Chulhee Yun, et al. ∙

research

∙ 01/17/2018

On the Limited Communication Analysis and Design for Decentralized Estimation

This paper pertains to the analysis and design of decentralized estimati...

Andreea B. Alexandru, et al. ∙

research

∙ 07/08/2017

Global optimality conditions for deep neural networks

We study the error landscape of deep linear and nonlinear neural network...

Chulhee Yun, et al. ∙

research

∙ 05/12/2017

Bayesian Group Decisions: Algorithms and Complexity

We address the computations that Bayesian agents undertake to realize th...

Ali Jadbabaie, et al. ∙

research

∙ 03/21/2017

Resilient Monotone Submodular Function Maximization

In this paper, we focus on applications in machine learning, optimizatio...

Vasileios Tzoumas, et al. ∙

research

∙ 02/21/2017

An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise

This paper addresses tracking of a moving target in a multi-agent networ...

Shahin Shahrampour, et al. ∙

research

∙ 11/10/2016

Distributed Estimation and Learning over Heterogeneous Networks

We consider several estimation and learning problems that networked agen...

M. Amin Rahimian, et al. ∙

research

∙ 09/09/2016

Distributed Online Optimization in Dynamic Environments Using Mirror Descent

This work addresses decentralized online optimization in non-stationary ...

Shahin Shahrampour, et al. ∙

research

∙ 09/30/2015

Learning without Recall: A Case for Log-Linear Learning

We analyze a model of learning and belief formation in networks in which...

Mohammad Amin Rahimian, et al. ∙

research

∙ 09/14/2015

Learning without Recall by Random Walks on Directed Graphs

We consider a network of agents that aim to learn some unknown state of ...

Mohammad Amin Rahimian, et al. ∙

Ali Jadbabaie

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