| 2025 | ICLR | Efficient stagewise pretraining via progressive subnetworks. | Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu, Sobhan Miryoosefi, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar |
| 2025 | SODA | Almost Tight Bounds for Differentially Private Densest Subgraph. | Michael Dinitz, Satyen Kale, Silvio Lattanzi, Sergei Vassilvitskii |
| 2024 | ALT | Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data. | Pranjal Awasthi, Satyen Kale, Ankit Pensia |
| 2024 | ICML | Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements. | Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta |
| 2023 | COLT | Differentially Private and Lazy Online Convex Optimization. | Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta |
| 2023 | ICML | On the Convergence of Federated Averaging with Cyclic Client Participation. | Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang |
| 2023 | ICML | Beyond Uniform Lipschitz Condition in Differentially Private Optimization. | Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi |
| 2023 | ICML | Efficient Training of Language Models using Few-Shot Learning. | Sashank J. Reddi, Sobhan Miryoosefi, Stefani Karp, Shankar Krishnan, Satyen Kale, Seungyeon Kim, Sanjiv Kumar |
| 2022 | AISTATS | Federated Functional Gradient Boosting. | Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi |
| 2022 | ALT | Efficient Methods for Online Multiclass Logistic Regression. | Naman Agarwal, Satyen Kale, Julian Zimmert |
| 2022 | COLT | Private Matrix Approximation and Geometry of Unitary Orbits. | Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Thakurta, Nisheeth K. Vishnoi |
| 2022 | COLT | Self-Consistency of the Fokker Planck Equation. | Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani |
| 2022 | COLT | Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. | Julian Zimmert, Naman Agarwal, Satyen Kale |
| 2022 | ICML | Agnostic Learnability of Halfspaces via Logistic Loss. | Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp |
| 2021 | ALT | A Deep Conditioning Treatment of Neural Networks. | Naman Agarwal, Pranjal Awasthi, Satyen Kale |
| 2020 | ICML | SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. | Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh |
| 2019 | AISTATS | 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 |
| 2019 | ALT | Algorithmic Learning Theory 2019: Preface. | Aurlien Garivier, Satyen Kale |
| 2019 | ICML | Escaping Saddle Points with Adaptive Gradient Methods. | Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra |
| 2018 | COLT | Logistic Regression: The Importance of Being Improper. | Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan |
| 2018 | ICLR | On the Convergence of Adam and Beyond. | Sashank J. Reddi, Satyen Kale, Sanjiv Kumar |
| 2018 | ICML | 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 |
| 2017 | COLT | Preface: Conference on Learning Theory (COLT), 2017. | Satyen Kale, Ohad Shamir |
| 2017 | ICML | Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. | Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dvid Pl |
| 2016 | COLT | Online Sparse Linear Regression. | Dean P. Foster, Satyen Kale, Howard J. Karloff |
| 2016 | ICALP | Online Semidefinite Programming. | Noa Elad, Satyen Kale, Joseph (Seffi) Naor |
| 2016 | IJCAI | Optimal and Adaptive Algorithms for Online Boosting. | Alina Beygelzimer, Satyen Kale, Haipeng Luo |
| 2015 | AAAI | Budgeted Prediction with Expert Advice. | Kareem Amin, Satyen Kale, Gerald Tesauro, Deepak S. Turaga |
| 2015 | ICML | Optimal and Adaptive Algorithms for Online Boosting. | Alina Beygelzimer, Satyen Kale, Haipeng Luo |
| 2014 | COLT | Multiarmed Bandits With Limited Expert Advice. | Satyen Kale |
| 2014 | COLT | Open Problem: Efficient Online Sparse Regression. | Satyen Kale |
| 2014 | ICML | Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. | Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire |
| 2013 | IJCAI | Bargaining for Revenue Shares on Tree Trading Networks. | Arpita Ghosh, Satyen Kale, Kevin J. Lang, Benjamin Moseley |
| 2012 | ICML | Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. | Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani |
| 2012 | ICML | Projection-free Online Learning. | Elad Hazan, Satyen Kale |
| 2011 | UAI | Efficient Optimal Learning for Contextual Bandits. | Miroslav Dudk, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang |
| 2010 | COLT | Learning Rotations with Little Regret. | Elad Hazan, Satyen Kale, Manfred K. Warmuth |
| 2010 | COLT | On-line Variance Minimization in O(n2) per Trial? | Elad Hazan, Satyen Kale, Manfred K. Warmuth |
| 2009 | SODA | The uniform hardcore lemma via approximate Bregman projections. | Boaz Barak, Moritz Hardt, Satyen Kale |
| 2009 | SODA | Better algorithms for benign bandits. | Elad Hazan, Satyen Kale |
| 2008 | COLT | Extracting Certainty from Uncertainty: Regret Bounded by Variation in Costs. | Elad Hazan, Satyen Kale |
| 2008 | FOCS | Noise Tolerance of Expanders and Sublinear Expander Reconstruction. | Satyen Kale, Yuval Peres, C. Seshadhri |
| 2008 | ICALP | An Expansion Tester for Bounded Degree Graphs. | Satyen Kale, C. Seshadhri |
| 2007 | PODS | Privacy, accuracy, and consistency too: a holistic solution to contingency table release. | Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, Kunal Talwar |
| 2007 | SODA | Efficient aggregation algorithms for probabilistic data. | T. S. Jayram, Satyen Kale, Erik Vee |
| 2007 | STOC | A combinatorial, primal-dual approach to semidefinite programs. | Sanjeev Arora, Satyen Kale |
| 2006 | COLT | Logarithmic Regret Algorithms for Online Convex Optimization. | Elad Hazan, Adam Kalai, Satyen Kale, Amit Agarwal |
| 2006 | ICML | Algorithms for portfolio management based on the Newton method. | Amit Agarwal, Elad Hazan, Satyen Kale, Robert E. Schapire |
| 2005 | FOCS | Fast Algorithms for Approximate Semide.nite Programming using the Multiplicative Weights Update Method. | Sanjeev Arora, Elad Hazan, Satyen Kale |
| 2004 | FOCS | 0(sqrt (log n)) Approximation to SPARSEST CUT in (n | Sanjeev Arora, Elad Hazan, Satyen Kale |