Sai Praneeth Karimireddy | DeepAI

DeepAI AI Chat

Featured Co-authors

Michael I. Jordan
274 publications
Martin Jaggi
101 publications
Ramesh Raskar
82 publications
Vahab Mirrokni
81 publications
Suvrit Sra
78 publications
Yi Ma
77 publications
Sanjiv Kumar
73 publications
Mehryar Mohri
52 publications
François Fleuret
51 publications
Tao Lin
49 publications
Nicolas Flammarion
43 publications

∙ 05/27/2023

Federated Conformal Predictors for Distributed Uncertainty Quantification

Conformal prediction is emerging as a popular paradigm for providing rig...

0 Charles Lu, et al. ∙

∙ 05/19/2023

Online Learning in a Creator Economy

The creator economy has revolutionized the way individuals can profit th...

0 Banghua Zhu, et al. ∙

∙ 07/13/2022

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels

State-of-the-art federated learning methods can perform far worse than t...

5 Yaodong Yu, et al. ∙

∙ 07/10/2022

Mechanisms that Incentivize Data Sharing in Federated Learning

Federated learning is typically considered a beneficial technology which...

25 Sai Praneeth Karimireddy, et al. ∙

∙ 06/01/2022

Optimization with access to auxiliary information

We investigate the fundamental optimization question of minimizing a tar...

0 El Mahdi Chayti, et al. ∙

∙ 02/09/2022

Agree to Disagree: Diversity through Disagreement for Better Transferability

Gradient-based learning algorithms have an implicit simplicity bias whic...

0 Matteo Pagliardini, et al. ∙

∙ 02/03/2022

Byzantine-Robust Decentralized Learning via Self-Centered Clipping

In this paper, we study the challenging task of Byzantine-robust decentr...

0 Lie He, et al. ∙

∙ 11/10/2021

Linear Speedup in Personalized Collaborative Learning

Personalization in federated learning can improve the accuracy of a mode...

0 El Mahdi Chayti, et al. ∙

∙ 10/28/2021

Towards Model Agnostic Federated Learning Using Knowledge Distillation

An often unquestioned assumption underlying most current federated learn...

0 Andrei Afonin, et al. ∙

∙ 10/08/2021

RelaySum for Decentralized Deep Learning on Heterogeneous Data

In decentralized machine learning, workers compute model updates on thei...

0 Thijs Vogels, et al. ∙

∙ 02/09/2021

Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Decentralized training of deep learning models is a key element for enab...

41 Tao Lin, et al. ∙

∙ 12/18/2020

Learning from History for Byzantine Robust Optimization

Byzantine robustness has received significant attention recently given i...

0 Sai Praneeth Karimireddy, et al. ∙

∙ 08/08/2020

Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Federated learning is a challenging optimization problem due to the hete...

0 Sai Praneeth Karimireddy, et al. ∙

∙ 08/04/2020

PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning

Lossy gradient compression has become a practical tool to overcome the c...

0 Thijs Vogels, et al. ∙

∙ 06/16/2020

Byzantine-Robust Learning on Heterogeneous Datasets via Resampling

In Byzantine robust distributed optimization, a central server wants to ...

0 Lie He, et al. ∙

∙ 06/08/2020

Secure Byzantine-Robust Machine Learning

Increasingly machine learning systems are being deployed to edge servers...

0 Lie He, et al. ∙

∙ 12/06/2019

Why ADAM Beats SGD for Attention Models

While stochastic gradient descent (SGD) is still the de facto algorithm ...

0 Jingzhao Zhang, et al. ∙

∙ 10/14/2019

SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning

Federated learning is a key scenario in modern large-scale machine learn...

7 Sai Praneeth Karimireddy, et al. ∙

∙ 09/11/2019

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication

We analyze (stochastic) gradient descent (SGD) with delayed updates on s...

0 Sebastian U. Stich, et al. ∙

∙ 07/11/2019

Amplifying Rényi Differential Privacy via Shuffling

Differential privacy is a useful tool to build machine learning models w...

0 Eloïse Berthier, et al. ∙

∙ 05/31/2019

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

We study gradient compression methods to alleviate the communication bot...

0 Thijs Vogels, et al. ∙

∙ 03/20/2019

Accelerating Gradient Boosting Machine

Gradient Boosting Machine (GBM) is an extremely powerful supervised lear...

0 Haihao Lu, et al. ∙

∙ 01/28/2019

Error Feedback Fixes SignSGD and other Gradient Compression Schemes

Sign-based algorithms (e.g. signSGD) have been proposed as a biased grad...

0 Sai Praneeth Karimireddy, et al. ∙

∙ 10/16/2018

Efficient Greedy Coordinate Descent for Composite Problems

Coordinate descent with random coordinate selection is the current state...

0 Sai Praneeth Karimireddy, et al. ∙

∙ 06/01/2018

Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients

We show that Newton's method converges globally at a linear rate for obj...

0 Sai Praneeth Karimireddy, et al. ∙