sai dot karimireddy at epfl dot ch
I am a 5th year PhD student at EPFL advised by Martin Jaggi. I am also affiliated with iGH and IDDO where I work on distributed intelligence in health. Before this, I graduated from IIT Delhi. I am on the job market starting Fall 2021.
All models are wrong, but some are useful. - George Box.
My main research interest is in enabling machine learning in the wild; to take it outside of clean centralized datasets. My past research has involved topics such as learning in low-resource settings using compression, federated learning, decentralized learning, robustness, security, and privacy. For more detail, read this one page research overview, or watch these interviews.
* indicates equal contribution.
Learning from History for Byzantine Robust Optimization.
SPK, Lie He, Martin Jaggi.
[ Arxiv 2020 ]
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning.
SPK, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, Ananda Theertha Suresh.
[ Arxiv 2020 ], [ Slides ], [ Code ]
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling.
Lie He*, SPK*, Martin Jaggi.
[ Arxiv 2020 ]
Secure Byzantine Machine Learning.
Lie He, SPK, Martin Jaggi.
[ Arxiv 2020 ]
Why Adaptive methods beat SGD for Attention Models.
Jingzhao Zhang, SPK, Andreas Veit, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar.
[ NeurIPS 2020 ]
PowerGossip: Practical Communication Compression in Decentralized Deep Learning.
Thijs Vogels, SPK, Martin Jaggi.
[ NeurIPS 2020 ], [ Slides ], [ Code ]
Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning.
Felix Grimberg, Mary-Anne Hartley, Martin Jaggi, SPK.
[ DART 2020 (pdf) ]
Accelerated Gradient Boosted Machines.
Haihao Lu*, SPK*, Natalia Ponomareva, Vahab Mirrokni.
[ AISTATS 2020 ]
The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication.
Sebastian Stich, SPK.
[ JMLR 2020 ]
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning.
SPK, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, Ananda Theertha Suresh.
[ ICML 2020 ], [ Short talk ], [ Long talk ]
PowerSGD: Practical Low-rank Gradient Compression for Distributed Optimization.
Thijs Vogels, SPK, Martin Jaggi.
[ NeurIPS 2019 ], [ Short video ], [ Code ]
Global Convergence of Newton-type Methods without Strong-Convexity or Lipschitz Gradients.
SPK, Sebastian Stich, Martin Jaggi.
[ NeurIps OptML 2019 ]
Efficient greedy coordinate descent for composite problems.
SPK*, Anastasia Koloskova*, Martin Jaggi.
[ AISTATS 2019 ]
Error Feedback fixes SignSGD and other Gradient Compression Schemes. (
SPK, Quentin Rebjock, Sebastian Stich, Martin Jaggi.
[ ICML 2019 ], [ Slides ], [ Code ]
On Matching Pursuit and Coordinate Descent.
Francesco Locatello*, Anant Raj*, SPK, Sebastian Stich, Martin Jaggi.
[ ICML 2018 ]
Adaptive Balancing of Gradient and Update Computation Times using Approximate Subproblem Solvers. (
SPK, Sebastian Stich, Martin Jaggi.
[ AISTATS 2018 ], [ Slides ]
Assignment Techniques for Crowdsourcing Sensitive Tasks.
Elisa Celis*, SPK*, Ishaan Singh*, Shailesh Vaya*.
[ CSCW 2016 ]
Multi-Broadcasting under SINR Model.
Darek Kowalski*, SPK*, Shailesh Vaya*
[ PODC 2016 ]
Some results on a class of van der Waerden Numbers.
SPK*, Kaushik Maran*, Dravyansh Sharma*, Amitabha Tripati*.
[ Rocky Journal of Mathematics Vol. 48 ]