
A General Framework for Distributed Inference with Uncertain Models
This paper studies the problem of distributed classification with a netw...
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GradientBased Empirical Risk Minimization using Local Polynomial Regression
In this paper, we consider the problem of empirical risk minimization (E...
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A Distributed CubicRegularized Newton Method for Smooth Convex Optimization over Networks
We propose a distributed, cubicregularized Newton method for largescal...
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GATGMM: Generative Adversarial Training for Gaussian Mixture Models
Generative adversarial networks (GANs) learn the distribution of observe...
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Robust Federated Learning: The Case of Affine Distribution Shifts
Federated learning is a distributed paradigm that aims at training model...
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Estimation of Skill Distributions
In this paper, we study the problem of learning the skill distribution o...
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Stochastic Optimization with Nonstationary Noise
We investigate stochastic optimization problems under relaxed assumption...
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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...
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On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions
We provide the first nonasymptotic analysis for finding stationary poin...
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Competitive Contagion with Sparse Seeding
This paper studies a strategic model of marketing and product diffusion ...
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NonBayesian Social Learning with Gaussian Uncertain Models
NonBayesian social learning theory provides a framework for distributed...
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FedPAQ: A CommunicationEfficient Federated Learning Method with Periodic Averaging and Quantization
Federated learning is a new distributed machine learning approach, where...
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Robust and Adaptive Sequential Submodular Optimization
Emerging applications of control, estimation, and machine learning, rang...
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NonBayesian Social Learning with Uncertain Models over TimeVarying Directed Graphs
We study the problem of nonBayesian social learning with uncertain mode...
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NonBayesian Social Learning with Uncertain Models
NonBayesian social learning theory provides a framework that models dis...
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Are deep ResNets provably better than linear predictors?
Recently, a residual network (ResNet) with a single residual block has b...
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Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition
We provide a theoretical explanation for the fast convergence of gradien...
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On Increasing SelfConfidence in NonBayesian Social Learning over TimeVarying Directed Graphs
We study the convergence of the loglinear nonBayesian social learning ...
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Finite sample expressive power of smallwidth ReLU networks
We study universal finite sample expressivity of neural networks, define...
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Efficiently testing local optimality and escaping saddles for ReLU networks
We provide a theoretical algorithm for checking local optimality and esc...
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Escaping Saddle Points in Constrained Optimization
In this paper, we focus on escaping from saddle points in smooth nonconv...
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Blind Community Detection from Lowrank Excitations of a Graph Filter
This paper considers a novel framework to detect communities in a graph ...
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Reasoning in Bayesian Opinion Exchange Networks Is PSPACEHard
We study the Bayesian model of opinion exchange of fully rational agents...
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Random Walks on Simplicial Complexes and the normalized Hodge Laplacian
Modeling complex systems and data with graphs has been a mainstay of the...
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Direct RungeKutta Discretization Achieves Acceleration
We study gradientbased optimization methods obtained by directly discre...
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Resilient NonSubmodular Maximization over Matroid Constraints
Applications in control, robotics, and optimization motivate the design ...
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Resilient Monotone Sequential Maximization
Applications in machine learning, optimization, and control require the ...
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LQG Control and Sensing Codesign
LinearQuadraticGaussian (LQG) control is concerned with the design of ...
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Control and Sensing Codesign
LinearQuadraticGaussian (LQG) control is concerned with the design of ...
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Simplicial Closure and Higherorder Link Prediction
Networks provide a powerful formalism for modeling complex systems, by r...
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A Critical View of Global Optimality in Deep Learning
We investigate the loss surface of deep linear and nonlinear neural netw...
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On the Limited Communication Analysis and Design for Decentralized Estimation
This paper pertains to the analysis and design of decentralized estimati...
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Global optimality conditions for deep neural networks
We study the error landscape of deep linear and nonlinear neural network...
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Bayesian Group Decisions: Algorithms and Complexity
We address the computations that Bayesian agents undertake to realize th...
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Resilient Monotone Submodular Function Maximization
In this paper, we focus on applications in machine learning, optimizatio...
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An Online Optimization Approach for MultiAgent Tracking of Dynamic Parameters in the Presence of Adversarial Noise
This paper addresses tracking of a moving target in a multiagent networ...
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Distributed Estimation and Learning over Heterogeneous Networks
We consider several estimation and learning problems that networked agen...
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Distributed Online Optimization in Dynamic Environments Using Mirror Descent
This work addresses decentralized online optimization in nonstationary ...
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Learning without Recall: A Case for LogLinear Learning
We analyze a model of learning and belief formation in networks in which...
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Learning without Recall by Random Walks on Directed Graphs
We consider a network of agents that aim to learn some unknown state of ...
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Switching to Learn
A network of agents attempt to learn some unknown state of the world dra...
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Online Optimization : Competing with Dynamic Comparators
Recent literature on online learning has focused on developing adaptive ...
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Distributed Detection : Finitetime Analysis and Impact of Network Topology
This paper addresses the problem of distributed detection in multiagent...
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Online Learning of Dynamic Parameters in Social Networks
This paper addresses the problem of online learning in a dynamic setting...
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Exponentially Fast Parameter Estimation in Networks Using Distributed Dual Averaging
In this paper we present an optimizationbased view of distributed param...
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Ali Jadbabaie
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JR East Professor of Engineering at Massachusetts Institute of Technology