Satyen Kale

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 74

Top Venues

CoRR

NeurIPS

ICML

COLT

Publications

Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu, Sobhan Miryoosefi, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
Efficient Stagewise Pretraining via Progressive Subnetworks. CoRR (2024)
Naman Agarwal, Pranjal Awasthi, Satyen Kale, Eric Zhao
Stacking as Accelerated Gradient Descent. CoRR (2024)
Pranjal Awasthi, Satyen Kale, Ankit Pensia
Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data. ALT (2024)
Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. Trans. Mach. Learn. Res. 2024 (2024)
Bo Liu, Rachita Chhaparia, Arthur Douillard, Satyen Kale, Andrei A. Rusu, Jiajun Shen, Arthur Szlam, Marc'Aurelio Ranzato
Asynchronous Local-SGD Training for Language Modeling. CoRR (2024)
Sashank J. Reddi, Sobhan Miryoosefi, Stefani Karp, Shankar Krishnan, Satyen Kale, Seungyeon Kim, Sanjiv Kumar
Efficient Training of Language Models using Few-Shot Learning. ICML (2023)
Michael Dinitz, Satyen Kale, Silvio Lattanzi, Sergei Vassilvitskii
Improved Differentially Private Densest Subgraph: Local and Purely Additive. CoRR (2023)
Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang
On the Convergence of Federated Averaging with Cyclic Client Participation. CoRR (2023)
Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. ICML (2023)
Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta
Improved Differentially Private and Lazy Online Convex Optimization. CoRR (2023)
Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta
Differentially Private and Lazy Online Convex Optimization. COLT (2023)
Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang
On the Convergence of Federated Averaging with Cyclic Client Participation. ICML (2023)
Naman Agarwal, Satyen Kale, Julian Zimmert
Efficient Methods for Online Multiclass Logistic Regression. ALT (2022)
Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. CoRR (2022)
Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani
Self-Consistency of the Fokker-Planck Equation. CoRR (2022)
Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Thakurta, Nisheeth K. Vishnoi
Private Matrix Approximation and Geometry of Unitary Orbits. COLT (2022)
Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani
Self-Consistency of the Fokker Planck Equation. COLT (2022)
Julian Zimmert, Naman Agarwal, Satyen Kale
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. CoRR (2022)
Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi
Federated Functional Gradient Boosting. AISTATS (2022)
Julian Zimmert, Naman Agarwal, Satyen Kale
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. COLT (2022)
Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. CoRR (2022)
Satyen Kale, Jason D. Lee, Chris De Sa, Ayush Sekhari, Karthik Sridharan
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. CoRR (2022)
Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp
Agnostic Learnability of Halfspaces via Logistic Loss. CoRR (2022)
Sean Augenstein, Andrew Hard, Lin Ning, Karan Singhal, Satyen Kale, Kurt Partridge, Rajiv Mathews
Mixed Federated Learning: Joint Decentralized and Centralized Learning. CoRR (2022)
Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
Reproducibility in Optimization: Theoretical Framework and Limits. CoRR (2022)
Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Guha Thakurta, Nisheeth K. Vishnoi
Private Matrix Approximation and Geometry of Unitary Orbits. CoRR (2022)
Christopher De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. NeurIPS (2022)
Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp
Agnostic Learnability of Halfspaces via Logistic Loss. ICML (2022)
Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
Reproducibility in Optimization: Theoretical Framework and Limits. NeurIPS (2022)
Ayush Sekhari, Karthik Sridharan, Satyen Kale
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. NeurIPS (2021)
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 Horvath, 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 (2021)
Naman Agarwal, Pranjal Awasthi, Satyen Kale
A Deep Conditioning Treatment of Neural Networks. ALT (2021)
Jacob D. Abernethy, Pranjal Awasthi, Satyen Kale
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness. CoRR (2021)
Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi
Federated Functional Gradient Boosting. CoRR (2021)
Satyen Kale, Ayush Sekhari, Karthik Sridharan
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. CoRR (2021)
Naman Agarwal, Satyen Kale, Julian Zimmert
Efficient Methods for Online Multiclass Logistic Regression. CoRR (2021)
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)
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
Learning with User-Level Privacy. NeurIPS (2021)
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
Learning with User-Level Privacy. CoRR (2021)
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 (2020)
Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan
Estimating Training Data Influence by Tracing Gradient Descent. NeurIPS (2020)
Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri
PAC-Bayes Learning Bounds for Sample-Dependent Priors. NeurIPS (2020)
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)
Garima Pruthi, Frederick Liu, Mukund Sundararajan, Satyen Kale
Estimating Training Data Influence by Tracking Gradient Descent. CoRR (2020)
Naman Agarwal, Pranjal Awasthi, Satyen Kale
A Deep Conditioning Treatment of Neural Networks. CoRR (2020)
Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
On the Convergence of Adam and Beyond. CoRR (2019)
Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra
Escaping Saddle Points with Adaptive Gradient Methods. CoRR (2019)
Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Hypothesis Set Stability and Generalization. NeurIPS (2019)
Sashank J. Reddi, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Jiecao Chen, Sanjiv Kumar
Stochastic Negative Mining for Learning with Large Output Spaces. AISTATS (2019)
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 (2019)
Chuan Guo, Ali Mousavi, Xiang Wu, Daniel Niels Holtmann-Rice, Satyen Kale, Sashank J. Reddi, Sanjiv Kumar
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. NeurIPS (2019)
Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Hypothesis Set Stability and Generalization. CoRR (2019)
Aurélien Garivier, Satyen Kale
Algorithmic Learning Theory 2019: Preface. ALT (2019)
Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra
Escaping Saddle Points with Adaptive Gradient Methods. ICML (2019)
Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Logistic Regression: The Importance of Being Improper. COLT (2018)
Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Logistic Regression: The Importance of Being Improper. CoRR (2018)
Sashank J. Reddi, Satyen Kale, Felix X. Yu, Daniel N. Holtmann-Rice, Jiecao Chen, Sanjiv Kumar
Stochastic Negative Mining for Learning with Large Output Spaces. CoRR (2018)
Manzil Zaheer, Sashank J. Reddi, Devendra Singh Sachan, Satyen Kale, Sanjiv Kumar
Adaptive Methods for Nonconvex Optimization. NeurIPS (2018)
Ian En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar
Loss Decomposition for Fast Learning in Large Output Spaces. ICML (2018)
Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak
Online Learning of Quantum States. NeurIPS (2018)
Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
Parameter-free online learning via model selection. CoRR (2018)
Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
On the Convergence of Adam and Beyond. ICLR (2018)
Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. ICML (2017)
Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
Parameter-Free Online Learning via Model Selection. NIPS (2017)
Elad Hazan, Satyen Kale, Shai Shalev-Shwartz
Near-Optimal Algorithms for Online Matrix Prediction. SIAM J. Comput. 46 (2) (2017)
Satyen Kale, Ohad Shamir
Preface: Conference on Learning Theory (COLT), 2017. COLT (2017)
Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. CoRR (2017)
Satyen Kale, Chansoo Lee, Dávid Pál
Hardness of Online Sleeping Combinatorial Optimization Problems. NIPS (2016)
Noa Elad, Satyen Kale, Joseph (Seffi) Naor
Online Semidefinite Programming. ICALP (2016)
Elad Hazan, Satyen Kale, Manfred K. Warmuth
Learning rotations with little regret. Mach. Learn. 104 (1) (2016)
Alina Beygelzimer, Satyen Kale, Haipeng Luo
Optimal and Adaptive Algorithms for Online Boosting. IJCAI (2016)
Dean P. Foster, Satyen Kale, Howard J. Karloff
Online Sparse Linear Regression. COLT (2016)
Sanjeev Arora, Satyen Kale
A Combinatorial, Primal-Dual Approach to Semidefinite Programs. J. ACM 63 (2) (2016)
Dean P. Foster, Satyen Kale, Howard J. Karloff
Online Sparse Linear Regression. CoRR (2016)
Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo
Online Gradient Boosting. CoRR (2015)
Satyen Kale, Chansoo Lee, Dávid Pál
Hardness of Online Sleeping Combinatorial Optimization Problems. CoRR (2015)
Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo
Online Gradient Boosting. NIPS (2015)
Alina Beygelzimer, Satyen Kale, Haipeng Luo
Optimal and Adaptive Algorithms for Online Boosting. ICML (2015)
Kareem Amin, Satyen Kale, Gerald Tesauro, Deepak S. Turaga
Budgeted Prediction with Expert Advice. AAAI (2015)
Alina Beygelzimer, Satyen Kale, Haipeng Luo
Optimal and Adaptive Algorithms for Online Boosting. CoRR (2015)
Elad Hazan, Satyen Kale
Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization. J. Mach. Learn. Res. 15 (1) (2014)
Satyen Kale
Open Problem: Efficient Online Sparse Regression. COLT (2014)
Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. CoRR (2014)
Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. ICML (2014)
Satyen Kale
Multiarmed Bandits With Limited Expert Advice. COLT (2014)
Arpita Ghosh, Satyen Kale, Kevin J. Lang, Benjamin Moseley
Bargaining for Revenue Shares on Tree Trading Networks CoRR (2013)
Satyen Kale, Yuval Peres, C. Seshadhri
Noise Tolerance of Expanders and Sublinear Expansion Reconstruction. SIAM J. Comput. 42 (1) (2013)
Anupam Gupta, Satyen Kale, Viswanath Nagarajan, Rishi Saket, Baruch Schieber
The Approximability of the Binary Paintshop Problem. APPROX-RANDOM (2013)
Arpita Ghosh, Satyen Kale, Kevin J. Lang, Benjamin Moseley
Bargaining for Revenue Shares on Tree Trading Networks. IJCAI (2013)
Satyen Kale
Multiarmed Bandits With Limited Expert Advice. CoRR (2013)
Jacob D. Abernethy, Satyen Kale
Adaptive Market Making via Online Learning. NIPS (2013)
Elad Hazan, Satyen Kale
Projection-free Online Learning CoRR (2012)
Satyen Kale
Commentary on "Online Optimization with Gradual Variations". COLT (2012)
Elad Hazan, Satyen Kale
Projection-free Online Learning. ICML (2012)
Elad Hazan, Satyen Kale, Shai Shalev-Shwartz
Near-Optimal Algorithms for Online Matrix Prediction CoRR (2012)
Elad Hazan, Satyen Kale
Online submodular minimization. J. Mach. Learn. Res. 13 (2012)
Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire
Contextual Bandit Learning with Predictable Rewards. AISTATS (2012)
Sanjeev Arora, Elad Hazan, Satyen Kale
The Multiplicative Weights Update Method: a Meta-Algorithm and Applications. Theory Comput. 8 (1) (2012)
Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani
Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. ICML (2012)
Elad Hazan, Satyen Kale, Shai Shalev-Shwartz
Near-Optimal Algorithms for Online Matrix Prediction. COLT (2012)

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Publication Activity (10 Years)

Top Topics

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Satyen Kale