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07/16/2023
Optimal Compression of Unit Norm Vectors in the High Distortion Regime
Motivated by the need for communication-efficient distributed learning, ...
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10/20/2022
An Improved Algorithm for Clustered Federated Learning
In this paper, we address the dichotomy between heterogeneous models and...
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07/23/2022
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference
We present a non-asymptotic lower bound on the eigenspectrum of the desi...
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07/06/2022
Model Selection in Reinforcement Learning with General Function Approximations
We consider model selection for classic Reinforcement Learning (RL) envi...
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05/31/2022
Decentralized Competing Bandits in Non-Stationary Matching Markets
Understanding complex dynamics of two-sided online matching markets, whe...
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05/26/2022
On Learning Mixture of Linear Regressions in the Non-Realizable Setting
While mixture of linear regressions (MLR) is a well-studied topic, prior...
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05/19/2022
Breaking the √(T) Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits
We prove an instance independent (poly) logarithmic regret for stochasti...
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07/13/2021
Model Selection with Near Optimal Rates for Reinforcement Learning with General Model Classes
We address the problem of model selection for the finite horizon episodi...
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07/07/2021
Model Selection for Generic Contextual Bandits
We consider the problem of model selection for the general stochastic co...
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06/15/2021
Collaborative Learning and Personalization in Multi-Agent Stochastic Linear Bandits
We consider the problem of minimizing regret in an N agent heterogeneous...
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05/16/2021
LocalNewton: Reducing Communication Bottleneck for Distributed Learning
To address the communication bottleneck problem in distributed optimizat...
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03/17/2021
Escaping Saddle Points in Distributed Newton's Method with Communication efficiency and Byzantine Resilience
We study the problem of optimizing a non-convex loss function (with sadd...
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06/15/2020
Distributed Newton Can Communicate Less and Resist Byzantine Workers
We develop a distributed second order optimization algorithm that is com...
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06/07/2020
An Efficient Framework for Clustered Federated Learning
We address the problem of Federated Learning (FL) where users are distri...
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06/04/2020
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits
We consider the problem of model selection for two popular stochastic li...
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04/23/2020
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression
We address the problem of solving mixed random linear equations. We have...
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11/21/2019
Communication-Efficient and Byzantine-Robust Distributed Learning
We develop a communication-efficient distributed learning algorithm that...
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06/21/2019
Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation
Max-affine regression refers to a model where the unknown regression fun...
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06/16/2019
Robust Federated Learning in a Heterogeneous Environment
We study a recently proposed large-scale distributed learning paradigm, ...
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07/09/2018
Online Scoring with Delayed Information: A Convex Optimization Viewpoint
We consider a system where agents enter in an online fashion and are eva...
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07/06/2018
Faster Data-access in Large-scale Systems: Network-scale Latency Analysis under General Service-time Distributions
In cloud storage systems with a large number of servers, files are typic...
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06/18/2018
Online Absolute Ranking with Partial Information: A Bipartite Graph Matching Approach
Ever since the introduction of the secretary problem, the notion of sele...
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05/03/2012
An Evolutionary Approach to Drug-Design Using a Novel Neighbourhood Based Genetic Algorithm
The present work provides a new approach to evolve ligand structures whi...
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05/03/2012
Multi-robot Cooperative Box-pushing problem using Multi-objective Particle Swarm Optimization Technique
The present work provides a new approach to solve the well-known multi-r...
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05/03/2012