Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems. - Sai Praneeth Reddy Karimireddy; Sebastian U. Stich; Martin Jaggi (AISTATS, 2018)

On Matching Pursuit and Coordinate Descent. - Francesco Locatello; Anant Raj; Sai Praneeth Karimireddy; Gunnar Rätsch; Bernhard Schölkopf; Sebastian U. Stich; Martin Jaggi (ICML, 2018)

Revisiting First-Order Convex Optimization Over Linear Spaces. - Francesco Locatello; Anant Raj; Sai Praneeth Karimireddy; Gunnar Rätsch; Bernhard Schölkopf; Sebastian U. Stich; Martin Jaggi (CoRR, 2018)

Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients. - Sai Praneeth Karimireddy; Sebastian U. Stich; Martin Jaggi (CoRR, 2018)

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

Error Feedback Fixes SignSGD and other Gradient Compression Schemes. - Sai Praneeth Karimireddy; Quentin Rebjock; Sebastian U. Stich; Martin Jaggi (ICML, 2019)

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

Accelerating Gradient Boosting Machine. - Haihao Lu; Sai Praneeth Karimireddy; Natalia Ponomareva 001; Vahab S. Mirrokni (CoRR, 2019)

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication. - Sebastian U. Stich; Sai Praneeth Karimireddy (CoRR, 2019)

SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. - Sai Praneeth Karimireddy; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (CoRR, 2019)

Why ADAM Beats SGD for Attention Models. - Jingzhao Zhang; Sai Praneeth Karimireddy; Andreas Veit; Seungyeon Kim 001; Sashank J. Reddi; Sanjiv Kumar; Suvrit Sra (CoRR, 2019)

Accelerating Gradient Boosting Machines. - Haihao Lu; Sai Praneeth Karimireddy; Natalia Ponomareva 001; Vahab S. Mirrokni (AISTATS, 2020)

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. - Sai Praneeth Karimireddy; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (ICML, 2020)

Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning. - Felix Grimberg; Mary-Anne Hartley; Martin Jaggi; Sai Praneeth Karimireddy (DART/DCL@MICCAI, 2020)

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

Why are Adaptive Methods Good for Attention Models? - Jingzhao Zhang; Sai Praneeth Karimireddy; Andreas Veit; Seungyeon Kim 001; Sashank J. Reddi; Sanjiv Kumar; Suvrit Sra (NeurIPS, 2020)

Secure Byzantine-Robust Machine Learning. - Lie He; Sai Praneeth Karimireddy; Martin Jaggi (CoRR, 2020)

Byzantine-Robust Learning on Heterogeneous Datasets via Resampling. - Lie He; Sai Praneeth Karimireddy; Martin Jaggi (CoRR, 2020)

PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning. - Thijs Vogels; Sai Praneeth Karimireddy; Martin Jaggi (CoRR, 2020)

Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. - Sai Praneeth Karimireddy; Martin Jaggi; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (CoRR, 2020)

Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. - Tao Lin 004; Sai Praneeth Karimireddy; Sebastian U. Stich; Martin Jaggi (ICML, 2021)

Learning from History for Byzantine Robust Optimization. - Sai Praneeth Karimireddy; Lie He; Martin Jaggi (ICML, 2021)

Breaking the centralized barrier for cross-device federated learning. - Sai Praneeth Karimireddy; Martin Jaggi; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (NeurIPS, 2021)

RelaySum for Decentralized Deep Learning on Heterogeneous Data. - Thijs Vogels; Lie He; Anastasia Koloskova; Sai Praneeth Karimireddy; Tao Lin 004; Sebastian U. Stich; Martin Jaggi (NeurIPS, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Optimal Model Averaging: Towards Personalized Collaborative Learning. - Felix Grimberg; Mary-Anne Hartley; Sai Praneeth Karimireddy; Martin Jaggi (CoRR, 2021)

Linear Speedup in Personalized Collaborative Learning. - El Mahdi Chayti; Sai Praneeth Karimireddy; Sebastian U. Stich; Nicolas Flammarion; Martin Jaggi (CoRR, 2021)

Optimization methods for collaborative learning. - Sai Praneeth Karimireddy (2021)

Towards Model Agnostic Federated Learning Using Knowledge Distillation. - Andrei Afonin; Sai Praneeth Karimireddy (ICLR, 2022)

Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing. - Sai Praneeth Karimireddy; Lie He; Martin Jaggi (ICLR, 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 001; Regis Loeb; Paul Mangold; Tanguy Marchand; Othmane Marfoq; Erum Mushtaq; Boris Muzellec; Constantin Philippenko; et al. (NeurIPS, 2022)

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. - Yaodong Yu; Alexander Wei 001; Sai Praneeth Karimireddy; Yi Ma 001; Michael I. Jordan (NeurIPS, 2022)

Byzantine-Robust Decentralized Learning via Self-Centered Clipping. - Lie He; Sai Praneeth Karimireddy; Martin Jaggi (CoRR, 2022)

Mechanisms that Incentivize Data Sharing in Federated Learning. - Sai Praneeth Karimireddy; Wenshuo Guo; Michael I. Jordan (CoRR, 2022)

Federated Learning Showdown: The Comparative Analysis of Federated Learning Frameworks. - Sai Praneeth Karimireddy; Narasimha Raghavan Veeraragavan; Severin Elvatun; Jan F. Nygård (FMEC, 2023)

Agree to Disagree: Diversity through Disagreement for Better Transferability. - Matteo Pagliardini; Martin Jaggi; François Fleuret; Sai Praneeth Karimireddy (ICLR, 2023)

Federated Conformal Predictors for Distributed Uncertainty Quantification. - Charles Lu 001; Yaodong Yu; Sai Praneeth Karimireddy; Michael I. Jordan; Ramesh Raskar (ICML, 2023)

Online Learning in a Creator Economy. - Banghua Zhu; Sai Praneeth Karimireddy; Jiantao Jiao; Michael I. Jordan (CoRR, 2023)

Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning. - Baihe Huang; Sai Praneeth Karimireddy; Michael I. Jordan (CoRR, 2023)

Scaff-PD: Communication Efficient Fair and Robust Federated Learning. - Yaodong Yu; Sai Praneeth Karimireddy; Yi Ma 001; Michael I. Jordan (CoRR, 2023)

Provably Personalized and Robust Federated Learning. - Mariel A. Werner; Lie He; Michael I. Jordan; Martin Jaggi; Sai Praneeth Karimireddy (Trans. Mach. Learn. Res., 2023)

LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation. - Ljubomir Rokvic; Panayiotis Danassis; Sai Praneeth Karimireddy; Boi Faltings (IEEE Big Data, 2024)

Privacy Can Arise Endogenously in an Economic System with Learning Agents. - Nivasini Ananthakrishnan; Tiffany Ding; Mariel A. Werner; Sai Praneeth Karimireddy; Michael I. Jordan (FORC, 2024)

Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. - Tianyu Guo 004; Sai Praneeth Karimireddy; Michael I. Jordan (ICML, 2024)

Data Acquisition via Experimental Design for Data Markets. - Charles Lu 001; Baihe Huang; Sai Praneeth Karimireddy; Praneeth Vepakomma; Michael I. Jordan; Ramesh Raskar (NeurIPS, 2024)

Data Acquisition via Experimental Design for Decentralized Data Markets. - Charles Lu 001; Baihe Huang; Sai Praneeth Karimireddy; Praneeth Vepakomma; Michael I. Jordan; Ramesh Raskar (CoRR, 2024)

Defection-Free Collaboration between Competitors in a Learning System. - Mariel A. Werner; Sai Praneeth Karimireddy; Michael I. Jordan (CoRR, 2024)

A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis. - Narasimha Raghavan Veeraragavan; Sai Praneeth Karimireddy; Jan Franz Nygård (CoRR, 2024)

MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images. - Klavdiia Naumova; Arnout Devos; Sai Praneeth Karimireddy; Martin Jaggi; Mary-Anne Hartley (npj Digit. Medicine, 2024)

Optimization with Access to Auxiliary Information. - El Mahdi Chayti; Sai Praneeth Karimireddy (Trans. Mach. Learn. Res., 2024)

Reconsidering LLM Uncertainty Estimation Methods in the Wild. - Yavuz Faruk Bakman; Duygu Nur Yaldiz; Sungmin Kang; Tuo Zhang; Baturalp Buyukates; Salman Avestimehr; Sai Praneeth Karimireddy (ACL (1), 2025)

Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum. - Riccardo Zaccone; Sai Praneeth Karimireddy; Carlo Masone; Marco Ciccone (Trans. Mach. Learn. Res., 2025)

From Fairness to Truthfulness: Rethinking Data Valuation Design. - Dongyang Fan; Tyler J. Rotello; Sai Praneeth Karimireddy (CoRR, 2025)

A Systematic Analysis of Base Model Choice for Reward Modeling. - Kian Ahrabian; Pegah Jandaghi; Negar Mokhberian; Sai Praneeth Karimireddy; Jay Pujara (CoRR, 2025)

Conformal Prediction Adaptive to Unknown Subpopulation Shifts. - Nien-Shao Wang; Duygu Nur Yaldiz; Yavuz Faruk Bakman; Sai Praneeth Karimireddy (CoRR, 2025)

TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs. - Duygu Nur Yaldiz; Yavuz Faruk Bakman; Sungmin Kang; Alperen Özis; Hayrettin Eren Yildiz; Mitash Ashish Shah; Zhiqi Huang; Anoop Kumar; Alfy Samuel; Daben Liu; Sai Praneeth Karimireddy; Salman Avestimehr (CoRR, 2025)

The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage. - Skyler Hallinan; Jaehun Jung; Melanie Sclar; Ximing Lu; Abhilasha Ravichander; Sahana Ramnath; Yejin Choi 001; Sai Praneeth Karimireddy; Niloofar Mireshghallah; Xiang Ren 001 (CoRR, 2025)