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07/14/2023
Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise
We propose an improved convergence analysis technique that characterizes...
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06/09/2023
Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications
This paper focuses on a multi-agent zeroth-order online optimization pro...
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05/08/2023
Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data
Decentralized learning enables the training of deep learning models over...
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03/19/2023
On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing
Decentralized learning and optimization is a central problem in control ...
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02/10/2022
No-Regret Learning in Dynamic Stackelberg Games
In a Stackelberg game, a leader commits to a randomized strategy, and a ...
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06/30/2021
Robust Generative Adversarial Imitation Learning via Local Lipschitzness
We explore methodologies to improve the robustness of generative adversa...
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06/16/2021
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
Geometric median (Gm) is a classical method in statistics for achieving ...
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06/13/2021
DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning
In this paper, we focus on facilitating differentially private quantized...
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03/04/2021
Function Approximation via Sparse Random Features
Random feature methods have been successful in various machine learning ...
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12/07/2020
Improved Convergence Rates for Non-Convex Federated Learning with Compression
Federated learning is a new distributed learning paradigm that enables e...
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11/20/2020
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization
In decentralized optimization, it is common algorithmic practice to have...
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09/27/2019
Identifying Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach
A variety of queries about stochastic systems boil down to study of Mark...
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07/22/2019
Stochastic-Greedy++: Closing the Optimality Gap in Exact Weak Submodular Maximization
Many problems in discrete optimization can be formulated as the task of ...
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10/29/2018
Evolutionary Self-Expressive Models for Subspace Clustering
The problem of organizing data that evolves over time into clusters is e...
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10/31/2017
Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation
We study the problem of sampling a bandlimited graph signal in the prese...
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10/31/2017
Accelerated Sparse Subspace Clustering
State-of-the-art algorithms for sparse subspace clustering perform spect...
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08/08/2016
Sparse recovery via Orthogonal Least-Squares under presence of Noise
We consider the Orthogonal Least-Squares (OLS) algorithm for the recover...
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08/08/2016
Sampling Requirements and Accelerated Schemes for Sparse Linear Regression with Orthogonal Least-Squares
The Orthogonal Least Squares (OLS) algorithm sequentially selects column...
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02/22/2016