Sai Praneeth Karimireddy

According to our database, Sai Praneeth Karimireddy authored at least 77 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs.

Vincent-Daniel Yun, Junhyuk Jo, Sai Praneeth Karimireddy, Sunwoo Lee

CoRR, May, 2026

Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs.

Amin Banayeeanzade, Qingchuan Yang, Dhruv Tarsadiya, Fatemeh Bahrani, Leonardo Blas, Alfy Samuel, Robin Jia, Meisam Razaviyayn, Sai Praneeth Karimireddy

CoRR, May, 2026

Robust Multi-Agent LLMs under Byzantine Faults.

Haejoon Lee, Vincent-Daniel Yun, Hyeonho Oh, Dimitra Panagou, Sai Praneeth Karimireddy

CoRR, May, 2026

Rethinking Visual Privacy: A Compositional Privacy Risk Framework for Severity Assessment with VLMs.

Efthymios Tsaprazlis, Tiantian Feng, Anil Ramakrishna, Sai Praneeth Karimireddy, Rahul Gupta, Shrikanth Narayanan

CoRR, March, 2026

Targeted Speaker Poisoning Framework in Zero-Shot Text-to-Speech.

Thanapat Trachu, Thanathai Lertpetchpun, Sai Praneeth Karimireddy, Shrikanth Narayanan

CoRR, March, 2026

EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors.

Amin Banayeeanzade, Qingchuan Yang, Deqing Fu, Spencer Hong, Erin Babinsky, Alfy Samuel, Anoop Kumar, Robin Jia, Sai Praneeth Karimireddy

CoRR, February, 2026

Sparks of Rationality: Do Reasoning LLMs Align with Human Judgment and Choice?

Ala N. Tak, Amin Banayeeanzade, Anahita Bolourani, Fatemeh Bahrani, Ashutosh Chaubey, Sai Praneeth Karimireddy, Norbert Schwarz, Jonathan Gratch

CoRR, January, 2026

Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment.

Yavuz Faruk Bakman, Duygu Nur Yaldiz, Salman Avestimehr, Sai Praneeth Karimireddy

CoRR, January, 2026

Block ModShift: Model Privacy via Dynamic Designed Shifts.

Nomaan Alam Kherani, Sai Praneeth Karimireddy, Urbashi Mitra

IEEE J. Sel. Areas Commun., 2026

OpaqueToolsBench: Learning Nuances of Tool Behavior Through Interaction.

Skyler Hallinan, Thejas Venkatesh, Xiang Ren, Sai Praneeth Karimireddy, Ashwin Paranjape, Yuhao Zhang, Jack Hessel

Proceedings of the ACM Conference on AI and Agentic Systems, 2026

Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness.

Amin Banayeeanzade, Ala N. Tak, Fatemeh Bahrani, Anahita Bolourani, Leonardo Blas, Emilio Ferrara, Jonathan Gratch, Sai Praneeth Karimireddy

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

2025

ContextLeak: Auditing Leakage in Private In-Context Learning Methods.

Jacob Choi, Shuying Cao, Xingjian Dong, Wang Bill Zhu, Robin Jia, Sai Praneeth Karimireddy

CoRR, December, 2025

Beyond URLs: Metadata Diversity and Position for Efficient LLM Pretraining.

Dongyang Fan, Diba Hashemi, Sai Praneeth Karimireddy, Martin Jaggi

CoRR, November, 2025

On the Limits of Momentum in Decentralized and Federated Optimization.

Riccardo Zaccone, Sai Praneeth Karimireddy, Carlo Masone

CoRR, November, 2025

DISCO: A Browser-Based Privacy-Preserving Framework for Distributed Collaborative Learning.

Julien Tuan Tu Vignoud, Valérian Rousset, Hugo El Guedj, Ignacio Aleman, Walid Bennaceur, Batuhan Faik Derinbay, Eduard Durech, Damien Gengler, Lucas Giordano, Felix Grimberg, Franziska Lippoldt, Christina Kopidaki, Jiafan Liu, Lauris Lopata, Nathan Maire, Paul Mansat, Martin Milenkoski, Emmanuel Omont, Günes Özgün, Mina Petrovic, Francesco Posa, Morgan Ridel, Giorgio Savini, Marcel Torne, Lucas Trognon, Alyssa Unell, Olena Zavertiaieva, Sai Praneeth Karimireddy, Tahseen Rabbani, Mary-Anne Hartley, Martin Jaggi

CoRR, November, 2025

A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning.

Minghui Chen, Hrad Ghoukasian, Ruinan Jin, Zehua Wang, Sai Praneeth Karimireddy, Xiaoxiao Li

CoRR, November, 2025

Reject Only Critical Tokens: Pivot-Aware Speculative Decoding.

Amir Ziashahabi, Yavuz Faruk Bakman, Duygu Nur Yaldiz, Mostafa El-Khamy, Sai Praneeth Karimireddy, Salman Avestimehr

CoRR, November, 2025

f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness.

Subhodip Panda, Dhruv Tarsadiya, Shashwat Sourav, Prathosh A. P., Sai Praneeth Karimireddy

CoRR, October, 2025

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering.

Yavuz Faruk Bakman, Sungmin Kang, Zhiqi Huang, Duygu Nur Yaldiz, Catarina G. Belém, Chenyang Zhu, Anoop Kumar, Alfy Samuel, Salman Avestimehr, Daben Liu, Sai Praneeth Karimireddy

CoRR, October, 2025

VoxGuard: Evaluating User and Attribute Privacy in Speech via Membership Inference Attacks.

Efthymios Tsaprazlis, Thanathai Lertpetchpun, Tiantian Feng, Sai Praneeth Karimireddy, Shrikanth Narayanan

CoRR, September, 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, Sai Praneeth Karimireddy, Niloofar Mireshghallah, Xiang Ren

CoRR, August, 2025

Conformal Prediction Adaptive to Unknown Subpopulation Shifts.

Nien-Shao Wang, Duygu Nur Yaldiz, Yavuz Faruk Bakman, Sai Praneeth Karimireddy

CoRR, June, 2025

From Fairness to Truthfulness: Rethinking Data Valuation Design.

Dongyang Fan, Tyler J. Rotello, Sai Praneeth Karimireddy

CoRR, April, 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

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

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

A Systematic Analysis of Base Model Choice for Reward Modeling.

Kian Ahrabian, Pegah Jandaghi, Negar Mokhberian, Sai Praneeth Karimireddy, Jay Pujara

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

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

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024

Optimization with Access to Auxiliary Information.

El Mahdi Chayti, Sai Praneeth Karimireddy

Trans. Mach. Learn. Res., 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

A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis.

Narasimha Raghavan Veeraragavan, Sai Praneeth Karimireddy, Jan Franz Nygård

CoRR, 2024

Defection-Free Collaboration between Competitors in a Learning System.

Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan

CoRR, 2024

Data Acquisition via Experimental Design for Decentralized Data Markets.

Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar

CoRR, 2024

Data Acquisition via Experimental Design for Data Markets.

Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Collaborative Heterogeneous Causal Inference Beyond Meta-analysis.

Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the 5th Symposium on Foundations of Responsible Computing, 2024

LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation.

Ljubomir Rokvic, Panayiotis Danassis, Sai Praneeth Karimireddy, Boi Faltings

Proceedings of the IEEE International Conference on Big Data, 2024

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

Scaff-PD: Communication Efficient Fair and Robust Federated Learning.

Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan

CoRR, 2023

Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning.

Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan

CoRR, 2023

Online Learning in a Creator Economy.

Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, Michael I. Jordan

CoRR, 2023

Federated Conformal Predictors for Distributed Uncertainty Quantification.

Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar

Proceedings of the International Conference on Machine Learning, 2023

Agree to Disagree: Diversity through Disagreement for Better Transferability.

Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Federated Learning Showdown: The Comparative Analysis of Federated Learning Frameworks.

Sai Praneeth Karimireddy, Narasimha Raghavan Veeraragavan, Severin Elvatun, Jan F. Nygård

Proceedings of the Eighth International Conference on Fog and Mobile Edge Computing, 2023

2022

Mechanisms that Incentivize Data Sharing in Federated Learning.

Sai Praneeth Karimireddy, Wenshuo Guo, Michael I. Jordan

CoRR, 2022

Byzantine-Robust Decentralized Learning via Self-Centered Clipping.

Lie He, Sai Praneeth Karimireddy, Martin Jaggi

CoRR, 2022

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels.

Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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 Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing.

Sai Praneeth Karimireddy, Lie He, Martin Jaggi

Proceedings of the Tenth International Conference on Learning Representations, 2022

Towards Model Agnostic Federated Learning Using Knowledge Distillation.

Andrei Afonin, Sai Praneeth Karimireddy

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Optimization methods for collaborative learning.

Sai Praneeth Karimireddy

PhD thesis, 2021

Linear Speedup in Personalized Collaborative Learning.

El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich, Nicolas Flammarion, Martin Jaggi

CoRR, 2021

Optimal Model Averaging: Towards Personalized Collaborative Learning.

Felix Grimberg, Mary-Anne Hartley, Sai Praneeth Karimireddy, Martin Jaggi

CoRR, 2021

A Field Guide to Federated Optimization.

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 Horváth, 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

CoRR, 2021

RelaySum for Decentralized Deep Learning on Heterogeneous Data.

Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning from History for Byzantine Robust Optimization.

Sai Praneeth Karimireddy, Lie He, Martin Jaggi

Proceedings of the 38th International Conference on Machine Learning, 2021

Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data.

Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi

Proceedings of the 38th International Conference on Machine Learning, 2021

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

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

Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

CoRR, 2020

Byzantine-Robust Learning on Heterogeneous Datasets via Resampling.

Lie He, Sai Praneeth Karimireddy, Martin Jaggi

CoRR, 2020

Secure Byzantine-Robust Machine Learning.

Lie He, Sai Praneeth Karimireddy, Martin Jaggi

CoRR, 2020

Why are Adaptive Methods Good for Attention Models?

Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Practical Low-Rank Communication Compression in Decentralized Deep Learning.

Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning.

Felix Grimberg, Mary-Anne Hartley, Martin Jaggi, Sai Praneeth Karimireddy

Proceedings of the Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, 2020

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning.

Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh

Proceedings of the 37th International Conference on Machine Learning, 2020

Accelerating Gradient Boosting Machines.

Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Why ADAM Beats SGD for Attention Models.

Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra

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

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

Sebastian U. Stich, Sai Praneeth Karimireddy

CoRR, 2019

Accelerating Gradient Boosting Machine.

Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni

CoRR, 2019

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization.

Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Error Feedback Fixes SignSGD and other Gradient Compression Schemes.

Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi

Proceedings of the 36th International Conference on Machine Learning, 2019

Efficient Greedy Coordinate Descent for Composite Problems.

Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

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

Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi

CoRR, 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

On Matching Pursuit and Coordinate Descent.

Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi

Proceedings of the 35th International Conference on Machine Learning, 2018

Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems.

Sai Praneeth Reddy Karimireddy, Sebastian U. Stich, Martin Jaggi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018