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08/22/2023
EM for Mixture of Linear Regression with Clustered Data
Modern data-driven and distributed learning frameworks deal with diverse...
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03/02/2023
Variance-reduced Clipping for Non-convex Optimization
Gradient clipping is a standard training technique used in deep learning...
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06/16/2022
Gradient Descent for Low-Rank Functions
Several recent empirical studies demonstrate that important machine lear...
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06/16/2020
Robust Federated Learning: The Case of Affine Distribution Shifts
Federated learning is a distributed paradigm that aims at training model...
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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...
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07/24/2019
Robust and Communication-Efficient Collaborative Learning
We consider a decentralized learning problem, where a set of computing n...
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02/06/2019
CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning
We focus on the commonly used synchronous Gradient Descent paradigm for ...
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06/29/2018
Quantized Decentralized Consensus Optimization
We consider the problem of decentralized consensus optimization, where t...
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01/17/2018