Amirhossein Reisizadeh | DeepAI

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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...

0 Amirhossein Reisizadeh, et al. ∙

∙ 03/02/2023

Variance-reduced Clipping for Non-convex Optimization

Gradient clipping is a standard training technique used in deep learning...

0 Amirhossein Reisizadeh, et al. ∙

∙ 06/16/2022

Gradient Descent for Low-Rank Functions

Several recent empirical studies demonstrate that important machine lear...

11 Romain Cosson, et al. ∙

∙ 06/16/2020

Robust Federated Learning: The Case of Affine Distribution Shifts

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

9 Amirhossein Reisizadeh, et al. ∙

∙ 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...

18 Amirhossein Reisizadeh, et al. ∙

∙ 07/24/2019

Robust and Communication-Efficient Collaborative Learning

We consider a decentralized learning problem, where a set of computing n...

0 Amirhossein Reisizadeh, et al. ∙

∙ 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 ...

0 Amirhossein Reisizadeh, et al. ∙

∙ 06/29/2018

Quantized Decentralized Consensus Optimization

We consider the problem of decentralized consensus optimization, where t...

0 Amirhossein Reisizadeh, et al. ∙

∙ 01/17/2018

Coded Computing for Distributed Graph Analytics

Many distributed graph computing systems have been developed recently fo...

0 Saurav Prakash, et al. ∙

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