Sai Praneeth Karimireddy

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 58

Top Topics

Experimental Design

Matching Pursuit

Heterogeneous Data

Top Venues

Publications

Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan
Defection-Free Collaboration between Competitors in a Learning System. CoRR (2024)
El Mahdi Chayti, Sai Praneeth Karimireddy
Optimization with Access to Auxiliary Information. Trans. Mach. Learn. Res. 2024 (2024)
Nivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan
Privacy Can Arise Endogenously in an Economic System with Learning Agents. FORC (2024)
Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. CoRR (2024)
Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar
Data Acquisition via Experimental Design for Decentralized Data Markets. CoRR (2024)
Nivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan
Privacy Can Arise Endogenously in an Economic System with Learning Agents. CoRR (2024)
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
Federated Conformal Predictors for Distributed Uncertainty Quantification. CoRR (2023)
Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy
Agree to Disagree: Diversity through Disagreement for Better Transferability. ICLR (2023)
Mariel A. Werner, Lie He, Sai Praneeth Karimireddy, Michael I. Jordan, Martin Jaggi
Provably Personalized and Robust Federated Learning. CoRR (2023)
Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
Scaff-PD: Communication Efficient Fair and Robust Federated Learning. CoRR (2023)
Sai Praneeth Karimireddy, Narasimha Raghavan Veeraragavan, Severin Elvatun, Jan F. Nygård
Federated Learning Showdown: The Comparative Analysis of Federated Learning Frameworks. FMEC (2023)
Mariel A. Werner, Lie He, Michael I. Jordan, Martin Jaggi, Sai Praneeth Karimireddy
Provably Personalized and Robust Federated Learning. Trans. Mach. Learn. Res. 2023 (2023)
Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning. CoRR (2023)
Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, Michael I. Jordan
Online Learning in a Creator Economy. CoRR (2023)
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
Federated Conformal Predictors for Distributed Uncertainty Quantification. ICML (2023)
Sai Praneeth Karimireddy, Wenshuo Guo, Michael I. Jordan
Mechanisms that Incentivize Data Sharing in Federated Learning. CoRR (2022)
Andrei Afonin, Sai Praneeth Karimireddy
Towards Model Agnostic Federated Learning Using Knowledge Distillation. ICLR (2022)
Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy
Agree to Disagree: Diversity through Disagreement for Better Transferability. CoRR (2022)
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago S. Silva R., Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. CoRR (2022)
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. NeurIPS (2022)
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. CoRR (2022)
Sai Praneeth Karimireddy, Lie He, Martin Jaggi
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing. ICLR (2022)
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. NeurIPS (2022)
Lie He, Sai Praneeth Karimireddy, Martin Jaggi
Byzantine-Robust Decentralized Learning via Self-Centered Clipping. CoRR (2022)
El Mahdi Chayti, Sai Praneeth Karimireddy
Optimization with access to auxiliary information. CoRR (2022)
Andrei Afonin, Sai Praneeth Karimireddy
Towards Model Agnostic Federated Learning Using Knowledge Distillation. CoRR (2021)
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
A Field Guide to Federated Optimization. CoRR (2021)
El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich, Nicolas Flammarion, Martin Jaggi
Linear Speedup in Personalized Collaborative Learning. CoRR (2021)
Sai Praneeth Karimireddy, Lie He, Martin Jaggi
Learning from History for Byzantine Robust Optimization. ICML (2021)
Felix Grimberg, Mary-Anne Hartley, Sai Praneeth Karimireddy, Martin Jaggi
Optimal Model Averaging: Towards Personalized Collaborative Learning. CoRR (2021)
Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. ICML (2021)
Thijs Vogels, Lie He, Anastasia Koloskova, Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
RelaySum for Decentralized Deep Learning on Heterogeneous Data. CoRR (2021)
Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi
RelaySum for Decentralized Deep Learning on Heterogeneous Data. NeurIPS (2021)
Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. CoRR (2021)
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
Breaking the centralized barrier for cross-device federated learning. NeurIPS (2021)
Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra
Why are Adaptive Methods Good for Attention Models? NeurIPS (2020)
Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
Practical Low-Rank Communication Compression in Decentralized Deep Learning. NeurIPS (2020)
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. CoRR (2020)
Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning. CoRR (2020)
Lie He, Sai Praneeth Karimireddy, Martin Jaggi
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling. CoRR (2020)
Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni
Accelerating Gradient Boosting Machines. AISTATS (2020)
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. ICML (2020)
Felix Grimberg, Mary-Anne Hartley, Martin Jaggi, Sai Praneeth Karimireddy
Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning. DART/DCL@MICCAI (2020)
Sai Praneeth Karimireddy, Lie He, Martin Jaggi
Learning from History for Byzantine Robust Optimization. CoRR (2020)
Lie He, Sai Praneeth Karimireddy, Martin Jaggi
Secure Byzantine-Robust Machine Learning. CoRR (2020)
Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. NeurIPS (2019)
Sebastian U. Stich, Sai Praneeth Karimireddy
The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication. CoRR (2019)
Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
Efficient Greedy Coordinate Descent for Composite Problems. AISTATS (2019)
Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra
Why ADAM Beats SGD for Attention Models. CoRR (2019)
Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni
Accelerating Gradient Boosting Machine. CoRR (2019)
Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi
Error Feedback Fixes SignSGD and other Gradient Compression Schemes. ICML (2019)
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. CoRR (2019)
Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi
Error Feedback Fixes SignSGD and other Gradient Compression Schemes. CoRR (2019)
Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. CoRR (2019)
Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
Efficient Greedy Coordinate Descent for Composite Problems. CoRR (2018)
Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi
On Matching Pursuit and Coordinate Descent. ICML (2018)
Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi
Revisiting First-Order Convex Optimization Over Linear Spaces. CoRR (2018)
Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients. CoRR (2018)