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ORCIDSai Praneeth Karimireddy
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2020 – today
- 2022
[i25]Lie He, Sai Praneeth Karimireddy, Martin Jaggi:
Byzantine-Robust Decentralized Learning via Self-Centered Clipping. CoRR abs/2202.01545 (2022)- [i24]Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy:
Agree to Disagree: Diversity through Disagreement for Better Transferability. CoRR abs/2202.04414 (2022) - [i23]El Mahdi Chayti, Sai Praneeth Karimireddy:
Optimization with access to auxiliary information. CoRR abs/2206.00395 (2022) - [i22]Sai Praneeth Karimireddy, Wenshuo Guo, Michael I. Jordan:
Mechanisms that Incentivize Data Sharing in Federated Learning. CoRR abs/2207.04557 (2022) - [i21]Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. CoRR abs/2207.06343 (2022) - 2021
- [b1]Sai Praneeth Karimireddy:
Optimization methods for collaborative learning. EPFL, Switzerland, 2021 - [c14]Sai Praneeth Karimireddy, Lie He, Martin Jaggi:
Learning from History for Byzantine Robust Optimization. ICML 2021: 5311-5319 - [c13]Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. ICML 2021: 6654-6665 - [c12]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: 28004-28015 - [c11]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: 28663-28676 - [i20]Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. CoRR abs/2102.04761 (2021) - [i19]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:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i18]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 abs/2110.04175 (2021) - [i17]Felix Grimberg, Mary-Anne Hartley, Sai Praneeth Karimireddy, Martin Jaggi:
Optimal Model Averaging: Towards Personalized Collaborative Learning. CoRR abs/2110.12946 (2021) - [i16]Andrei Afonin, Sai Praneeth Karimireddy:
Towards Model Agnostic Federated Learning Using Knowledge Distillation. CoRR abs/2110.15210 (2021) - [i15]El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich, Nicolas Flammarion, Martin Jaggi:
Linear Speedup in Personalized Collaborative Learning. CoRR abs/2111.05968 (2021) - 2020
- [c10]Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni:
Accelerating Gradient Boosting Machines. AISTATS 2020: 516-526 - [c9]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: 5132-5143 - [c8]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: 160-169 - [c7]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
Practical Low-Rank Communication Compression in Decentralized Deep Learning. NeurIPS 2020 - [c6]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 - [i14]Lie He, Sai Praneeth Karimireddy, Martin Jaggi:
Secure Byzantine-Robust Machine Learning. CoRR abs/2006.04747 (2020) - [i13]Lie He, Sai Praneeth Karimireddy, Martin Jaggi:
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling. CoRR abs/2006.09365 (2020) - [i12]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning. CoRR abs/2008.01425 (2020) - [i11]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 abs/2008.03606 (2020) - [i10]Sai Praneeth Karimireddy, Lie He, Martin Jaggi:
Learning from History for Byzantine Robust Optimization. CoRR abs/2012.10333 (2020)
2010 – 2019
- 2019
- [c5]Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Efficient Greedy Coordinate Descent for Composite Problems. AISTATS 2019: 2887-2896 - [c4]Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi:
Error Feedback Fixes SignSGD and other Gradient Compression Schemes. ICML 2019: 3252-3261 - [c3]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. NeurIPS 2019: 14236-14245 - [i9]Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi:
Error Feedback Fixes SignSGD and other Gradient Compression Schemes. CoRR abs/1901.09847 (2019) - [i8]Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni:
Accelerating Gradient Boosting Machine. CoRR abs/1903.08708 (2019) - [i7]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. CoRR abs/1905.13727 (2019) - [i6]Sebastian U. Stich, Sai Praneeth Karimireddy:
The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication. CoRR abs/1909.05350 (2019) - [i5]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 abs/1910.06378 (2019) - [i4]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why ADAM Beats SGD for Attention Models. CoRR abs/1912.03194 (2019) - 2018
- [c2]Sai Praneeth Reddy Karimireddy, Sebastian U. Stich, Martin Jaggi:
Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems. AISTATS 2018: 1204-1213 - [c1]Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi:
On Matching Pursuit and Coordinate Descent. ICML 2018: 3204-3213 - [i3]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 abs/1803.09539 (2018) - [i2]Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients. CoRR abs/1806.00413 (2018) - [i1]Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Efficient Greedy Coordinate Descent for Composite Problems. CoRR abs/1810.06999 (2018)
Coauthor Index
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