Karimireddy, Sai Praneeth

32 publications

TMLR 2025 Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum Riccardo Zaccone, Sai Praneeth Karimireddy, Carlo Masone, Marco Ciccone

ICLRW 2025 From Fairness to Truthfulness: Rethinking Data Valuation Design Dongyang Fan, Tyler J. Rotello, Sai Praneeth Karimireddy

ICML 2024 Collaborative Heterogeneous Causal Inference Beyond Meta-Analysis Tianyu Guo, Sai Praneeth Karimireddy, Michael Jordan

NeurIPS 2024 Data Acquisition via Experimental Design for Data Markets Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar

NeurIPSW 2024 Defection-Free Collaboration Between Competitors in a Learning System Mariel Werner, Sai Praneeth Karimireddy, Michael Jordan

TMLR 2024 Optimization with Access to Auxiliary Information El Mahdi Chayti, Sai Praneeth Karimireddy

ICLR 2023 Agree to Disagree: Diversity Through Disagreement for Better Transferability Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy

ICMLW 2023 Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning Baihe Huang, Sai Praneeth Karimireddy, Michael Jordan

ICML 2023 Federated Conformal Predictors for Distributed Uncertainty Quantification Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael Jordan, Ramesh Raskar

ICMLW 2023 Federated Conformal Predictors for Distributed Uncertainty Quantification Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael Jordan, Ramesh Raskar

TMLR 2023 Provably Personalized and Robust Federated Learning Mariel Werner, Lie He, Michael Jordan, Martin Jaggi, Sai Praneeth Karimireddy

ICMLW 2023 SCAFF-PD: Communication Efficient Fair and Robust Federated Learning Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael Jordan

ICLR 2022 Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing Sai Praneeth Karimireddy, Lie He, Martin Jaggi

NeurIPSW 2022 Diversity Through Disagreement for Better Transferability Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy

NeurIPS 2022 FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings 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 Teleńczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux

NeurIPSW 2022 Mechanisms That Incentivize Data Sharing in Federated Learning Sai Praneeth Karimireddy, Wenshuo Guo, Michael Jordan

NeurIPS 2022 TCT: Convexifying Federated Learning Using Bootstrapped Neural Tangent Kernels Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan

ICLR 2022 Towards Model Agnostic Federated Learning Using Knowledge Distillation Andrei Afonin, Sai Praneeth Karimireddy

NeurIPSW 2022 Towards Provably Personalized Federated Learning via Threshold-Clustering of Similar Clients Mariel Werner, Lie He, Sai Praneeth Karimireddy, Michael Jordan, Martin Jaggi

NeurIPS 2021 Breaking the Centralized Barrier for Cross-Device Federated Learning Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian U Stich, Ananda Theertha Suresh

ICML 2021 Learning from History for Byzantine Robust Optimization Sai Praneeth Karimireddy, Lie He, Martin Jaggi

ICML 2021 Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data Tao Lin, Sai Praneeth Karimireddy, Sebastian Stich, Martin Jaggi

NeurIPS 2021 RelaySum for Decentralized Deep Learning on Heterogeneous Data Thijs Vogels, Lie He, Anastasiia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U Stich, Martin Jaggi

AISTATS 2020 Accelerating Gradient Boosting Machines Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab Mirrokni

NeurIPS 2020 Practical Low-Rank Communication Compression in Decentralized Deep Learning Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

ICML 2020 SCAFFOLD: Stochastic Controlled Averaging for Federated Learning Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, Ananda Theertha Suresh

JMLR 2020 The Error-Feedback Framework: SGD with Delayed Gradients Sebastian U. Stich, Sai Praneeth Karimireddy

NeurIPS 2020 Why Are Adaptive Methods Good for Attention Models? Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar, Suvrit Sra

AISTATS 2019 Efficient Greedy Coordinate Descent for Composite Problems Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi

ICML 2019 Error Feedback Fixes SignSGD and Other Gradient Compression Schemes Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian Stich, Martin Jaggi

NeurIPS 2019 PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

ICML 2018 On Matching Pursuit and Coordinate Descent Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Raetsch, Bernhard Schölkopf, Sebastian Stich, Martin Jaggi