Kumar Kshitij Patel

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Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Sebastian Urban StichCISPA Helmholtz CenterVerified email at cispa.de
Blake WoodworthGeorge Washington UniversityVerified email at gwu.edu
Brian BullinsAssistant Professor, Purdue UniversityVerified email at purdue.edu
Lingxiao WangToyota Technological Institute at ChicagoVerified email at ttic.edu
Ohad ShamirWeizmann Institute of ScienceVerified email at weizmann.ac.il
Aymeric DieuleveutProfessor, Ecole Polytechnique, FranceVerified email at polytechnique.edu
Martin JaggiEPFLVerified email at epfl.ch
Tao LINWestlake University | EPFLVerified email at westlake.edu.cn
H. Brendan McMahanResearch Scientist, Google SeattleVerified email at google.com
Zhen DaiSome Quant Reserach CompanyVerified email at berkeley.edu
Gal VardiWeizmann Institute of ScienceVerified email at weizmann.ac.il
Pedro SavareseTTICVerified email at ttic.edu
Matthew R. WalterToyota Technological Institute at ChicagoVerified email at ttic.edu
Michael MaireUniversity of ChicagoVerified email at uchicago.edu
Karen LivescuTTI-ChicagoVerified email at ttic.edu
David YunisGraduate Student, TTICVerified email at ttic.edu
Aadirupa SahaResearch Scientist, Apple ML Research, CupertinoVerified email at ttic.edu
Margalit R GlasgowStanford UniversityVerified email at stanford.edu
Nirmit JoshiToyota Technological Institute at ChicagoVerified email at ttic.edu

Kumar Kshitij Patel

Phd Student, TTIC

Verified email at ttic.edu - Homepage

Optimization Machine Learning Federated Learning


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Don't use large mini-batches, use local sgd T Lin, SU Stich, KK Patel, M Jaggi arXiv preprint arXiv:1808.07217, 2018	494	2018
Is local SGD better than minibatch SGD? B Woodworth, KK Patel, S Stich, Z Dai, B Bullins, B Mcmahan, O Shamir, ... International Conference on Machine Learning, 10334-10343, 2020	297	2020
Minibatch vs local sgd for heterogeneous distributed learning BE Woodworth, KK Patel, N Srebro Advances in Neural Information Processing Systems 33, 6281-6292, 2020	225	2020
Communication trade-offs for local-sgd with large step size KK Patel, A Dieuleveut Advances In Neural Information Processing Systems 32 (32), 2825-2830, 2019	86*	2019
Corruption-tolerant bandit learning S Kapoor, KK Patel, P Kar Machine Learning 108 (4), 687-715, 2019	64	2019
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization KK Patel, L Wang, B Woodworth, B Bullins, N Srebro Advances in Neural Information Systems 36, 2022	20	2022
On Convexity and Linear Mode Connectivity in Neural Networks D Yunis, KK Patel, PHP Savarese, G Vardi, J Frankle, M Walter, K Livescu, ... OPT 2022: Optimization for Machine Learning (NeurIPS 2022 Workshop), 2022	19	2022
Federated online and bandit convex optimization KK Patel, L Wang, A Saha, N Srebro International Conference on Machine Learning, 27439-27460, 2023	18*	2023
A stochastic newton algorithm for distributed convex optimization B Bullins, K Patel, O Shamir, N Srebro, BE Woodworth Advances in Neural Information Processing Systems 34, 26818-26830, 2021	18	2021
The limits and potentials of local sgd for distributed heterogeneous learning with intermittent communication KK Patel, M Glasgow, A Zindari, L Wang, SU Stich, Z Cheng, N Joshi, ... arXiv preprint arXiv:2405.11667, 2024	15*	2024
On the Effect of Defections in Federated Learning and How to Prevent Them M Han, KK Patel, H Shao, L Wang arXiv preprint arXiv:2311.16459, 2023	5	2023
Approaching Deep Learning through the Spectral Dynamics of Weights D Yunis, KK Patel, S Wheeler, P Savarese, G Vardi, K Livescu, M Maire, ... arXiv preprint arXiv:2408.11804, 2024		2024
The Limitations of Model Retraining in the Face of Performativity A Kabra, KK Patel arXiv preprint arXiv:2408.08499, 2024		2024
Online Combinatorial Optimization with Group Fairness Constraints N Golrezaei, R Niazadeh, KK Patel, F Susan Available at SSRN 4824251, 2024		2024
Urban Heat Effect in Ghent: a Time Series Analysis KK Patel, S Roels		2018
Personalization Mitigates the Perils of Local SGD for Heterogeneous Distributed Learning KK Patel, N Gazagnadou, L Wang, L Lyu
One Shot Learning in Humans: A Non-Parametric Bayesian Model KK Patel
Paraphrase Generation Using Deep Generative Models Final Project Report CS-772 N Asnani, K Gandhi, KK Patel
Efficient Private Federated Non-Convex Optimization With Shuffled Model L Wang, X Zhou, KK Patel, L Tang, A Saha Privacy Regulation and Protection in Machine Learning, 0
Private Overparameterized Linear Regression without Suffering in High Dimensions L Wang, D Zou, KK Patel, J Wu, N Srebro

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