0(sqrt (log n)) Approximation to SPARSEST CUT in Õ(n2) Time. - Sanjeev Arora; Elad Hazan; Satyen Kale (FOCS, 2004)

Fast Algorithms for Approximate Semide.nite Programming using the Multiplicative Weights Update Method. - Sanjeev Arora; Elad Hazan; Satyen Kale (FOCS, 2005)

Analysis and Algorithms for Content-Based Event Matching. - Satyen Kale; Elad Hazan; Fengyun Cao; Jaswinder Pal Singh (ICDCS Workshops, 2005)

A Fast Random Sampling Algorithm for Sparsifying Matrices. - Sanjeev Arora; Elad Hazan; Satyen Kale (APPROX-RANDOM, 2006)

Logarithmic Regret Algorithms for Online Convex Optimization. - Elad Hazan; Adam Kalai; Satyen Kale; Amit Agarwal 008 (COLT, 2006)

Algorithms for portfolio management based on the Newton method. - Amit Agarwal 008; Elad Hazan; Satyen Kale; Robert E. Schapire (ICML, 2006)

Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria. - Elad Hazan; Satyen Kale (NIPS, 2007)

Privacy, accuracy, and consistency too: a holistic solution to contingency table release. - Boaz Barak; Kamalika Chaudhuri; Cynthia Dwork; Satyen Kale; Frank McSherry; Kunal Talwar (PODS, 2007)

Efficient aggregation algorithms for probabilistic data. - T. S. Jayram; Satyen Kale; Erik Vee (SODA, 2007)

Boosting and hard-core set constructions: a simplified approach. - Satyen Kale (Electron. Colloquium Comput. Complex., 2007)

Testing Expansion in Bounded Degree Graphs. - Satyen Kale; C. Seshadhri 001 (Electron. Colloquium Comput. Complex., 2007)

A variation on SVD based image compression. - Abhiram G. Ranade; Srikanth S. Mahabalarao; Satyen Kale (Image Vis. Comput., 2007)

Logarithmic regret algorithms for online convex optimization. - Elad Hazan; Amit Agarwal 008; Satyen Kale (Mach. Learn., 2007)

Noise Tolerance of Expanders and Sublinear Expander Reconstruction. - Satyen Kale; Yuval Peres; C. Seshadhri 001 (FOCS, 2008)

Beyond Convexity: Online Submodular Minimization. - Elad Hazan; Satyen Kale (NIPS, 2009)

On Stochastic and Worst-case Models for Investing. - Elad Hazan; Satyen Kale (NIPS, 2009)

The uniform hardcore lemma via approximate Bregman projections. - Boaz Barak; Moritz Hardt; Satyen Kale (SODA, 2009)

On-line Variance Minimization in O(n2) per Trial? - Elad Hazan; Satyen Kale; Manfred K. Warmuth (COLT, 2010)

Non-Stochastic Bandit Slate Problems. - Satyen Kale; Lev Reyzin; Robert E. Schapire (NIPS, 2010)

Extracting certainty from uncertainty: regret bounded by variation in costs. - Elad Hazan; Satyen Kale (Mach. Learn., 2010)

O(sqrt(log(n)) Approximation to SPARSEST CUT in Õ(n2) Time. - Sanjeev Arora; Elad Hazan; Satyen Kale (SIAM J. Comput., 2010)

Cross-Validation and Mean-Square Stability. - Satyen Kale; Ravi Kumar 001; Sergei Vassilvitskii (ICS, 2011)

Combinatorial Approximation Algorithms for MaxCut using Random Walks. - Satyen Kale; C. Seshadhri 001 (ICS, 2011)

Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction. - Elad Hazan; Satyen Kale (NIPS, 2011)

Who moderates the moderators?: crowdsourcing abuse detection in user-generated content. - Arpita Ghosh; Satyen Kale; R. Preston McAfee (EC, 2011)

Efficient Optimal Learning for Contextual Bandits. - Miroslav Dudík; Daniel J. Hsu; Satyen Kale; Nikos Karampatziakis; John Langford 001; Lev Reyzin; Tong Zhang 001 (UAI, 2011)

Better Algorithms for Benign Bandits. - Elad Hazan; Satyen Kale (J. Mach. Learn. Res., 2011)

Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization. - Elad Hazan; Satyen Kale (COLT, 2011)

A simple multi-armed bandit algorithm with optimal variation-bounded regret. - Elad Hazan; Satyen Kale (COLT, 2011)

An Expansion Tester for Bounded Degree Graphs. - Satyen Kale; C. Seshadhri 001 (SIAM J. Comput., 2011)

Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. - Haim Avron; Satyen Kale; Shiva Prasad Kasiviswanathan; Vikas Sindhwani (ICML, 2012)

Projection-free Online Learning. - Elad Hazan; Satyen Kale (ICML, 2012)

Contextual Bandit Learning with Predictable Rewards. - Alekh Agarwal; Miroslav Dudík; Satyen Kale; John Langford 001; Robert E. Schapire (AISTATS, 2012)

Online submodular minimization. - Elad Hazan; Satyen Kale (J. Mach. Learn. Res., 2012)

Commentary on "Online Optimization with Gradual Variations". - Satyen Kale (COLT, 2012)

The Multiplicative Weights Update Method: a Meta-Algorithm and Applications. - Sanjeev Arora; Elad Hazan; Satyen Kale (Theory Comput., 2012)

The Approximability of the Binary Paintshop Problem. - Anupam Gupta 001; Satyen Kale; Viswanath Nagarajan; Rishi Saket; Baruch Schieber (APPROX-RANDOM, 2013)

Bargaining for Revenue Shares on Tree Trading Networks. - Arpita Ghosh; Satyen Kale; Kevin J. Lang; Benjamin Moseley (IJCAI, 2013)

Adaptive Market Making via Online Learning. - Jacob D. Abernethy; Satyen Kale (NIPS, 2013)

Noise Tolerance of Expanders and Sublinear Expansion Reconstruction. - Satyen Kale; Yuval Peres; C. Seshadhri 001 (SIAM J. Comput., 2013)

Multiarmed Bandits With Limited Expert Advice. - Satyen Kale (COLT, 2014)

Open Problem: Efficient Online Sparse Regression. - Satyen Kale (COLT, 2014)

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. - Alekh Agarwal; Daniel J. Hsu; Satyen Kale; John Langford 001; Lihong Li 001; Robert E. Schapire (ICML, 2014)

Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization. - Elad Hazan; Satyen Kale (J. Mach. Learn. Res., 2014)

Budgeted Prediction with Expert Advice. - Kareem Amin 002; Satyen Kale; Gerald Tesauro; Deepak S. Turaga (AAAI, 2015)

Optimal and Adaptive Algorithms for Online Boosting. - Alina Beygelzimer; Satyen Kale; Haipeng Luo (ICML, 2015)

Online Gradient Boosting. - Alina Beygelzimer; Elad Hazan; Satyen Kale; Haipeng Luo (NIPS, 2015)

Online Sparse Linear Regression. - Dean P. Foster; Satyen Kale; Howard J. Karloff (COLT, 2016)

Online Semidefinite Programming. - Noa Elad; Satyen Kale; Joseph (Seffi) Naor (ICALP, 2016)

Hardness of Online Sleeping Combinatorial Optimization Problems. - Satyen Kale; Chansoo Lee; Dávid Pál (NIPS, 2016)

A Combinatorial, Primal-Dual Approach to Semidefinite Programs. - Sanjeev Arora; Satyen Kale (J. ACM, 2016)

Learning rotations with little regret. - Elad Hazan; Satyen Kale; Manfred K. Warmuth (Mach. Learn., 2016)

Preface: Conference on Learning Theory (COLT), 2017. - Satyen Kale; Ohad Shamir (COLT, 2017)

Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. - Satyen Kale; Zohar S. Karnin; Tengyuan Liang; Dávid Pál (ICML, 2017)

Parameter-Free Online Learning via Model Selection. - Dylan J. Foster; Satyen Kale; Mehryar Mohri; Karthik Sridharan (NIPS, 2017)

Near-Optimal Algorithms for Online Matrix Prediction. - Elad Hazan; Satyen Kale; Shai Shalev-Shwartz (SIAM J. Comput., 2017)

Logistic Regression: The Importance of Being Improper. - Dylan J. Foster; Satyen Kale; Haipeng Luo; Mehryar Mohri; Karthik Sridharan (COLT, 2018)

On the Convergence of Adam and Beyond. - Sashank J. Reddi; Satyen Kale; Sanjiv Kumar (ICLR, 2018)

Loss Decomposition for Fast Learning in Large Output Spaces. - Ian En-Hsu Yen; Satyen Kale; Felix X. Yu; Daniel Niels Holtmann-Rice; Sanjiv Kumar; Pradeep Ravikumar (ICML, 2018)

Online Learning of Quantum States. - Scott Aaronson; Xinyi Chen 001; Elad Hazan; Satyen Kale; Ashwin Nayak 001 (NeurIPS, 2018)

Adaptive Methods for Nonconvex Optimization. - Manzil Zaheer; Sashank J. Reddi; Devendra Singh Sachan; Satyen Kale; Sanjiv Kumar (NeurIPS, 2018)

Stochastic Negative Mining for Learning with Large Output Spaces. - Sashank J. Reddi; Satyen Kale; Felix X. Yu; Daniel Niels Holtmann-Rice; Jiecao Chen; Sanjiv Kumar (AISTATS, 2019)

Algorithmic Learning Theory 2019: Preface. - Aurélien Garivier; Satyen Kale (ALT, 2019)

Escaping Saddle Points with Adaptive Gradient Methods. - Matthew Staib; Sashank J. Reddi; Satyen Kale; Sanjiv Kumar; Suvrit Sra (ICML, 2019)

Hypothesis Set Stability and Generalization. - Dylan J. Foster; Spencer Greenberg; Satyen Kale; Haipeng Luo; Mehryar Mohri; Karthik Sridharan (NeurIPS, 2019)

Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. - Chuan Guo 001; Ali Mousavi 003; Xiang Wu; Daniel Niels Holtmann-Rice; Satyen Kale; Sashank J. Reddi; Sanjiv Kumar (NeurIPS, 2019)

SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. - Sai Praneeth Karimireddy; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (CoRR, 2019)

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. - Sai Praneeth Karimireddy; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (ICML, 2020)

PAC-Bayes Learning Bounds for Sample-Dependent Priors. - Pranjal Awasthi; Satyen Kale; Stefani Karp; Mehryar Mohri (NeurIPS, 2020)

Estimating Training Data Influence by Tracing Gradient Descent. - Garima Pruthi; Frederick Liu; Satyen Kale; Mukund Sundararajan (NeurIPS, 2020)

Estimating Training Data Influence by Tracking Gradient Descent. - Garima Pruthi; Frederick Liu; Mukund Sundararajan; Satyen Kale (CoRR, 2020)

Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. - Sai Praneeth Karimireddy; Martin Jaggi; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (CoRR, 2020)

A Deep Conditioning Treatment of Neural Networks. - Naman Agarwal; Pranjal Awasthi; Satyen Kale (ALT, 2021)

Breaking the centralized barrier for cross-device federated learning. - Sai Praneeth Karimireddy; Martin Jaggi; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (NeurIPS, 2021)

Learning with User-Level Privacy. - Daniel Levy; Ziteng Sun; Kareem Amin 002; Satyen Kale; Alex Kulesza; Mehryar Mohri; Ananda Theertha Suresh (NeurIPS, 2021)

SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. - Ayush Sekhari; Karthik Sridharan; Satyen Kale (NeurIPS, 2021)

A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness. - Jacob D. Abernethy; Pranjal Awasthi; Satyen Kale (CoRR, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Federated Functional Gradient Boosting. - Zebang Shen; Hamed Hassani; Satyen Kale; Amin Karbasi (AISTATS, 2022)

Efficient Methods for Online Multiclass Logistic Regression. - Naman Agarwal; Satyen Kale; Julian Zimmert (ALT, 2022)

Private Matrix Approximation and Geometry of Unitary Orbits. - Oren Mangoubi; Yikai Wu 001; Satyen Kale; Abhradeep Thakurta; Nisheeth K. Vishnoi (COLT, 2022)

Self-Consistency of the Fokker Planck Equation. - Zebang Shen; Zhenfu Wang; Satyen Kale; Alejandro Ribeiro; Amin Karbasi; Hamed Hassani (COLT, 2022)

Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. - Julian Zimmert; Naman Agarwal; Satyen Kale (COLT, 2022)

Agnostic Learnability of Halfspaces via Logistic Loss. - Ziwei Ji; Kwangjun Ahn; Pranjal Awasthi; Satyen Kale; Stefani Karp (ICML, 2022)

Reproducibility in Optimization: Theoretical Framework and Limits. - Kwangjun Ahn; Prateek Jain 002; Ziwei Ji; Satyen Kale; Praneeth Netrapalli; Gil I. Shamir (NeurIPS, 2022)

From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. - Christopher De Sa; Satyen Kale; Jason D. Lee; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2022)

Mixed Federated Learning: Joint Decentralized and Centralized Learning. - Sean Augenstein; Andrew Hard; Lin Ning 001; Karan Singhal; Satyen Kale; Kurt Partridge; Rajiv Mathews (CoRR, 2022)

Differentially Private and Lazy Online Convex Optimization. - Naman Agarwal; Satyen Kale; Karan Singh; Abhradeep Thakurta (COLT, 2023)

On the Convergence of Federated Averaging with Cyclic Client Participation. - Yae Jee Cho; Pranay Sharma; Gauri Joshi; Zheng Xu 002; Satyen Kale; Tong Zhang 001 (ICML, 2023)

Beyond Uniform Lipschitz Condition in Differentially Private Optimization. - Rudrajit Das; Satyen Kale; Zheng Xu 002; Tong Zhang 001; Sujay Sanghavi (ICML, 2023)

Efficient Training of Language Models using Few-Shot Learning. - Sashank J. Reddi; Sobhan Miryoosefi; Stefani Karp; Shankar Krishnan; Satyen Kale; Seungyeon Kim 001; Sanjiv Kumar (ICML, 2023)

Improved Differentially Private Densest Subgraph: Local and Purely Additive. - Michael Dinitz; Satyen Kale; Silvio Lattanzi; Sergei Vassilvitskii (CoRR, 2023)

Improved Differentially Private and Lazy Online Convex Optimization. - Naman Agarwal; Satyen Kale; Karan Singh; Abhradeep Guha Thakurta (CoRR, 2023)

Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data. - Pranjal Awasthi; Satyen Kale; Ankit Pensia (ALT, 2024)

Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements. - Naman Agarwal; Satyen Kale; Karan Singh; Abhradeep Guha Thakurta (ICML, 2024)

Asynchronous Local-SGD Training for Language Modeling. - Bo Liu; Rachita Chhaparia; Arthur Douillard; Satyen Kale; Andrei A. Rusu; Jiajun Shen; Arthur Szlam; Marc'Aurelio Ranzato (CoRR, 2024)

Stacking as Accelerated Gradient Descent. - Naman Agarwal; Pranjal Awasthi; Satyen Kale; Eric Zhao 003 (CoRR, 2024)

On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. - Jianyu Wang; Rudrajit Das; Gauri Joshi; Satyen Kale; Zheng Xu 002; Tong Zhang 001 (Trans. Mach. Learn. Res., 2024)

Efficient stagewise pretraining via progressive subnetworks. - Abhishek Panigrahi; Nikunj Saunshi; Kaifeng Lyu; Sobhan Miryoosefi; Sashank J. Reddi; Satyen Kale; Sanjiv Kumar (ICLR, 2025)

Almost Tight Bounds for Differentially Private Densest Subgraph. - Michael Dinitz; Satyen Kale; Silvio Lattanzi; Sergei Vassilvitskii (SODA, 2025)

Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch. - Arthur Douillard; Yanislav Donchev; Keith Rush; Satyen Kale; Zachary Charles; Zachary Garrett; Gabriel Teston; Dave Lacey; Ross McIlroy; Jiajun Shen; Alexandre Ramé; Arthur Szlam; et al. (CoRR, 2025)

Eager Updates For Overlapped Communication and Computation in DiLoCo. - Satyen Kale; Arthur Douillard; Yanislav Donchev (CoRR, 2025)

ABoN: Adaptive Best-of-N Alignment. - Vinod Raman; Hilal Asi; Satyen Kale (CoRR, 2025)