Sai Praneeth Karimireddy

91 papers A* 19A 2B 1Journal 63Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Block ModShift: Model Privacy via Dynamic Designed Shifts. IEEE J. Sel. Areas Commun. Nomaan Alam Kherani, Sai Praneeth Karimireddy, Urbashi Mitra
2026	J	`jnl`	EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors. CoRR Amin Banayeeanzade, Qingchuan Yang, Deqing Fu, Spencer Hong, Erin Babinsky, Alfy Samuel, Anoop Kumar, Robin Jia, Sai Praneeth Karimireddy
2026	J	`jnl`	Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment. CoRR Yavuz Faruk Bakman, Duygu Nur Yaldiz, Salman Avestimehr, Sai Praneeth Karimireddy
2026	J	`jnl`	OpaqueToolsBench: Learning Nuances of Tool Behavior Through Interaction. CoRR Skyler Hallinan, Thejas Venkatesh, Xiang Ren, Sai Praneeth Karimireddy, Ashwin Paranjape, Yuhao Zhang, Jack Hessel
2026	J	`jnl`	Rethinking Visual Privacy: A Compositional Privacy Risk Framework for Severity Assessment with VLMs. CoRR Efthymios Tsaprazlis, Tiantian Feng, Anil Ramakrishna, Sai Praneeth Karimireddy, Rahul Gupta, Shrikanth Narayanan
2026	J	`jnl`	Sparks of Rationality: Do Reasoning LLMs Align with Human Judgment and Choice? CoRR Ala N. Tak, Amin Banayeeanzade, Anahita Bolourani, Fatemeh Bahrani, Ashutosh Chaubey, Sai Praneeth Karimireddy, Norbert Schwarz, Jonathan Gratch
2026	J	`jnl`	Targeted Speaker Poisoning Framework in Zero-Shot Text-to-Speech. CoRR Thanapat Trachu, Thanathai Lertpetchpun, Sai Praneeth Karimireddy, Shrikanth Narayanan
2025	J	`jnl`	A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning. CoRR Minghui Chen, Hrad Ghoukasian, Ruinan Jin, Zehua Wang, Sai Praneeth Karimireddy, Xiaoxiao Li
2025	A*	`conf`	A Systematic Analysis of Base Model Choice for Reward Modeling. EMNLP Kian Ahrabian, Pegah Jandaghi, Negar Mokhberian, Sai Praneeth Karimireddy, Jay Pujara
2025	J	`jnl`	A Systematic Analysis of Base Model Choice for Reward Modeling. CoRR Kian Ahrabian, Pegah Jandaghi, Negar Mokhberian, Sai Praneeth Karimireddy, Jay Pujara
2025	J	`jnl`	Beyond URLs: Metadata Diversity and Position for Efficient LLM Pretraining. CoRR Dongyang Fan, Diba Hashemi, Sai Praneeth Karimireddy, Martin Jaggi
2025	J	`jnl`	Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum. Trans. Mach. Learn. Res. Riccardo Zaccone, Sai Praneeth Karimireddy, Carlo Masone, Marco Ciccone
2025	J	`jnl`	Conformal Prediction Adaptive to Unknown Subpopulation Shifts. CoRR Nien-Shao Wang, Duygu Nur Yaldiz, Yavuz Faruk Bakman, Sai Praneeth Karimireddy
2025	J	`jnl`	ContextLeak: Auditing Leakage in Private In-Context Learning Methods. CoRR Jacob Choi, Shuying Cao, Xingjian Dong, Wang Bill Zhu, Robin Jia, Sai Praneeth Karimireddy
2025	J	`jnl`	DISCO: A Browser-Based Privacy-Preserving Framework for Distributed Collaborative Learning. CoRR 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
2025	J	`jnl`	From Fairness to Truthfulness: Rethinking Data Valuation Design. CoRR Dongyang Fan, Tyler J. Rotello, Sai Praneeth Karimireddy
2025	J	`jnl`	On the Limits of Momentum in Decentralized and Federated Optimization. CoRR Riccardo Zaccone, Sai Praneeth Karimireddy, Carlo Masone
2025	J	`jnl`	Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness. CoRR Amin Banayeeanzade, Ala N. Tak, Fatemeh Bahrani, Anahita Bolourani, Leonardo Blas, Emilio Ferrara, Jonathan Gratch, Sai Praneeth Karimireddy
2025	—	`conf`	Reconsidering LLM Uncertainty Estimation Methods in the Wild. ACL (1) Yavuz Faruk Bakman, Duygu Nur Yaldiz, Sungmin Kang, Tuo Zhang, Baturalp Buyukates, Salman Avestimehr, Sai Praneeth Karimireddy
2025	J	`jnl`	Reconsidering LLM Uncertainty Estimation Methods in the Wild. CoRR Yavuz Faruk Bakman, Duygu Nur Yaldiz, Sungmin Kang, Tuo Zhang, Baturalp Buyukates, Salman Avestimehr, Sai Praneeth Karimireddy
2025	J	`jnl`	Reject Only Critical Tokens: Pivot-Aware Speculative Decoding. CoRR Amir Ziashahabi, Yavuz Faruk Bakman, Duygu Nur Yaldiz, Mostafa El-Khamy, Sai Praneeth Karimireddy, Salman Avestimehr
2025	J	`jnl`	The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage. CoRR Skyler Hallinan, Jaehun Jung, Melanie Sclar, Ximing Lu, Abhilasha Ravichander, Sahana Ramnath, Yejin Choi, Sai Praneeth Karimireddy, Niloofar Mireshghallah, Xiang Ren
2025	—	`conf`	TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs. EMNLP (System Demonstrations) 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
2025	J	`jnl`	TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs. CoRR 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
2025	J	`jnl`	Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering. CoRR 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
2025	J	`jnl`	VoxGuard: Evaluating User and Attribute Privacy in Speech via Membership Inference Attacks. CoRR Efthymios Tsaprazlis, Thanathai Lertpetchpun, Tiantian Feng, Sai Praneeth Karimireddy, Shrikanth Narayanan
2025	J	`jnl`	f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness. CoRR Subhodip Panda, Dhruv Tarsadiya, Shashwat Sourav, Prathosh A. P., Sai Praneeth Karimireddy
2024	J	`jnl`	A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis. CoRR Narasimha Raghavan Veeraragavan, Sai Praneeth Karimireddy, Jan Franz Nygård
2024	A*	`conf`	Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. ICML Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan
2024	J	`jnl`	Collaborative Heterogeneous Causal Inference Beyond Meta-analysis. CoRR Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan
2024	A*	`conf`	Data Acquisition via Experimental Design for Data Markets. NeurIPS Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar
2024	J	`jnl`	Data Acquisition via Experimental Design for Decentralized Data Markets. CoRR Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar
2024	J	`jnl`	Defection-Free Collaboration between Competitors in a Learning System. CoRR Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan
2024	B	`conf`	LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation. IEEE Big Data Ljubomir Rokvic, Panayiotis Danassis, Sai Praneeth Karimireddy, Boi Faltings
2024	J	`jnl`	MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images. npj Digit. Medicine Klavdiia Naumova, Arnout Devos, Sai Praneeth Karimireddy, Martin Jaggi, Mary-Anne Hartley
2024	J	`jnl`	Optimization with Access to Auxiliary Information. Trans. Mach. Learn. Res. El Mahdi Chayti, Sai Praneeth Karimireddy
2024	—	`conf`	Privacy Can Arise Endogenously in an Economic System with Learning Agents. FORC Nivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan
2024	J	`jnl`	Privacy Can Arise Endogenously in an Economic System with Learning Agents. CoRR Nivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan
2023	A*	`conf`	Agree to Disagree: Diversity through Disagreement for Better Transferability. ICLR Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy
2023	J	`jnl`	Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning. CoRR Baihe Huang, Sai Praneeth Karimireddy, Michael I. Jordan
2023	A*	`conf`	Federated Conformal Predictors for Distributed Uncertainty Quantification. ICML Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
2023	J	`jnl`	Federated Conformal Predictors for Distributed Uncertainty Quantification. CoRR Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
2023	—	`conf`	Federated Learning Showdown: The Comparative Analysis of Federated Learning Frameworks. FMEC Sai Praneeth Karimireddy, Narasimha Raghavan Veeraragavan, Severin Elvatun, Jan F. Nygård
2023	J	`jnl`	Online Learning in a Creator Economy. CoRR Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, Michael I. Jordan
2023	J	`jnl`	Provably Personalized and Robust Federated Learning. CoRR Mariel A. Werner, Lie He, Sai Praneeth Karimireddy, Michael I. Jordan, Martin Jaggi
2023	J	`jnl`	Provably Personalized and Robust Federated Learning. Trans. Mach. Learn. Res. Mariel A. Werner, Lie He, Michael I. Jordan, Martin Jaggi, Sai Praneeth Karimireddy
2023	J	`jnl`	Scaff-PD: Communication Efficient Fair and Robust Federated Learning. CoRR Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
2022	J	`jnl`	Agree to Disagree: Diversity through Disagreement for Better Transferability. CoRR Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy
2022	J	`jnl`	Byzantine-Robust Decentralized Learning via Self-Centered Clipping. CoRR Lie He, Sai Praneeth Karimireddy, Martin Jaggi
2022	A*	`conf`	Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing. ICLR Sai Praneeth Karimireddy, Lie He, Martin Jaggi
2022	A*	`conf`	FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. NeurIPS 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
2022	J	`jnl`	FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. CoRR 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
2022	J	`jnl`	Mechanisms that Incentivize Data Sharing in Federated Learning. CoRR Sai Praneeth Karimireddy, Wenshuo Guo, Michael I. Jordan
2022	J	`jnl`	Optimization with access to auxiliary information. CoRR El Mahdi Chayti, Sai Praneeth Karimireddy
2022	A*	`conf`	TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. NeurIPS Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
2022	J	`jnl`	TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. CoRR Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
2022	A*	`conf`	Towards Model Agnostic Federated Learning Using Knowledge Distillation. ICLR Andrei Afonin, Sai Praneeth Karimireddy
2021	J	`jnl`	A Field Guide to Federated Optimization. CoRR 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
2021	A*	`conf`	Breaking the centralized barrier for cross-device federated learning. NeurIPS Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
2021	A*	`conf`	Learning from History for Byzantine Robust Optimization. ICML Sai Praneeth Karimireddy, Lie He, Martin Jaggi
2021	J	`jnl`	Linear Speedup in Personalized Collaborative Learning. CoRR El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich, Nicolas Flammarion, Martin Jaggi
2021	J	`jnl`	Optimal Model Averaging: Towards Personalized Collaborative Learning. CoRR Felix Grimberg, Mary-Anne Hartley, Sai Praneeth Karimireddy, Martin Jaggi
2021	—	`—`	Optimization methods for collaborative learning. Sai Praneeth Karimireddy
2021	J	`jnl`	Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. CoRR Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
2021	A*	`conf`	Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. ICML Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
2021	A*	`conf`	RelaySum for Decentralized Deep Learning on Heterogeneous Data. NeurIPS Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi
2021	J	`jnl`	RelaySum for Decentralized Deep Learning on Heterogeneous Data. CoRR Thijs Vogels, Lie He, Anastasia Koloskova, Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
2021	J	`jnl`	Towards Model Agnostic Federated Learning Using Knowledge Distillation. CoRR Andrei Afonin, Sai Praneeth Karimireddy
2020	A	`conf`	Accelerating Gradient Boosting Machines. AISTATS Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni
2020	J	`jnl`	Byzantine-Robust Learning on Heterogeneous Datasets via Resampling. CoRR Lie He, Sai Praneeth Karimireddy, Martin Jaggi
2020	J	`jnl`	Learning from History for Byzantine Robust Optimization. CoRR Sai Praneeth Karimireddy, Lie He, Martin Jaggi
2020	J	`jnl`	Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. CoRR Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
2020	J	`jnl`	PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning. CoRR Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
2020	A*	`conf`	Practical Low-Rank Communication Compression in Decentralized Deep Learning. NeurIPS Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
2020	A*	`conf`	SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. ICML Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
2020	J	`jnl`	Secure Byzantine-Robust Machine Learning. CoRR Lie He, Sai Praneeth Karimireddy, Martin Jaggi
2020	—	`conf`	Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning. DART/DCL@MICCAI Felix Grimberg, Mary-Anne Hartley, Martin Jaggi, Sai Praneeth Karimireddy
2020	A*	`conf`	Why are Adaptive Methods Good for Attention Models? NeurIPS Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra
2019	J	`jnl`	Accelerating Gradient Boosting Machine. CoRR Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni
2019	A	`conf`	Efficient Greedy Coordinate Descent for Composite Problems. AISTATS Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
2019	A*	`conf`	Error Feedback Fixes SignSGD and other Gradient Compression Schemes. ICML Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi
2019	J	`jnl`	Error Feedback Fixes SignSGD and other Gradient Compression Schemes. CoRR Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi
2019	A*	`conf`	PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. NeurIPS Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
2019	J	`jnl`	PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. CoRR Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
2019	J	`jnl`	SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. CoRR Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
2019	J	`jnl`	The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication. CoRR Sebastian U. Stich, Sai Praneeth Karimireddy
2019	J	`jnl`	Why ADAM Beats SGD for Attention Models. CoRR Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra
2018	J	`jnl`	Efficient Greedy Coordinate Descent for Composite Problems. CoRR Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
2018	J	`jnl`	Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients. CoRR Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
2018	A*	`conf`	On Matching Pursuit and Coordinate Descent. ICML Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi
2018	J	`jnl`	Revisiting First-Order Convex Optimization Over Linear Spaces. CoRR Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi