Satyen Kale - RankMe

149 papers A* 59A 6B 5Journal 65Unranked 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Faster Rates For Federated Variational Inequalities. CoRR Guanghui Wang, Satyen Kale
2025	J	`jnl`	ABoN: Adaptive Best-of-N Alignment. CoRR Vinod Raman, Hilal Asi, Satyen Kale
2025	A*	`conf`	Almost Tight Bounds for Differentially Private Densest Subgraph. SODA Michael Dinitz, Satyen Kale, Silvio Lattanzi, Sergei Vassilvitskii
2025	J	`jnl`	Eager Updates For Overlapped Communication and Computation in DiLoCo. CoRR Satyen Kale, Arthur Douillard, Yanislav Donchev
2025	A*	`conf`	Efficient stagewise pretraining via progressive subnetworks. ICLR Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu, Sobhan Miryoosefi, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
2025	J	`jnl`	Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch. CoRR Arthur Douillard, Yanislav Donchev, Keith Rush, Satyen Kale, Zachary Charles, Zachary Garrett, Gabriel Teston, Dave Lacey, Ross McIlroy, Jiajun Shen, Alexandre Ramé, Arthur Szlam, Marc'Aurelio Ranzato, Paul Barham
2025	J	`jnl`	Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration. CoRR Ahmed Khaled, Satyen Kale, Arthur Douillard, Chi Jin, Rob Fergus, Manzil Zaheer
2024	J	`jnl`	Asynchronous Local-SGD Training for Language Modeling. CoRR Bo Liu, Rachita Chhaparia, Arthur Douillard, Satyen Kale, Andrei A. Rusu, Jiajun Shen, Arthur Szlam, Marc'Aurelio Ranzato
2024	J	`jnl`	Efficient Stagewise Pretraining via Progressive Subnetworks. CoRR Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu, Sobhan Miryoosefi, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
2024	A*	`conf`	Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements. ICML Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta
2024	J	`jnl`	On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. Trans. Mach. Learn. Res. Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang
2024	B	`conf`	Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data. ALT Pranjal Awasthi, Satyen Kale, Ankit Pensia
2024	J	`jnl`	Stacking as Accelerated Gradient Descent. CoRR Naman Agarwal, Pranjal Awasthi, Satyen Kale, Eric Zhao
2023	A*	`conf`	Beyond Uniform Lipschitz Condition in Differentially Private Optimization. ICML Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi
2023	A*	`conf`	Differentially Private and Lazy Online Convex Optimization. COLT Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta
2023	A*	`conf`	Efficient Training of Language Models using Few-Shot Learning. ICML Sashank J. Reddi, Sobhan Miryoosefi, Stefani Karp, Shankar Krishnan, Satyen Kale, Seungyeon Kim, Sanjiv Kumar
2023	J	`jnl`	Improved Differentially Private Densest Subgraph: Local and Purely Additive. CoRR Michael Dinitz, Satyen Kale, Silvio Lattanzi, Sergei Vassilvitskii
2023	J	`jnl`	Improved Differentially Private and Lazy Online Convex Optimization. CoRR Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta
2023	A*	`conf`	On the Convergence of Federated Averaging with Cyclic Client Participation. ICML Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang
2023	J	`jnl`	On the Convergence of Federated Averaging with Cyclic Client Participation. CoRR Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang
2022	A*	`conf`	Agnostic Learnability of Halfspaces via Logistic Loss. ICML Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp
2022	J	`jnl`	Agnostic Learnability of Halfspaces via Logistic Loss. CoRR Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp
2022	J	`jnl`	Beyond Uniform Lipschitz Condition in Differentially Private Optimization. CoRR Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi
2022	B	`conf`	Efficient Methods for Online Multiclass Logistic Regression. ALT Naman Agarwal, Satyen Kale, Julian Zimmert
2022	A	`conf`	Federated Functional Gradient Boosting. AISTATS Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi
2022	A*	`conf`	From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. NeurIPS Christopher De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan
2022	J	`jnl`	From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. CoRR Satyen Kale, Jason D. Lee, Chris De Sa, Ayush Sekhari, Karthik Sridharan
2022	J	`jnl`	Mixed Federated Learning: Joint Decentralized and Centralized Learning. CoRR Sean Augenstein, Andrew Hard, Lin Ning, Karan Singhal, Satyen Kale, Kurt Partridge, Rajiv Mathews
2022	J	`jnl`	On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. CoRR Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang
2022	A*	`conf`	Private Matrix Approximation and Geometry of Unitary Orbits. COLT Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Thakurta, Nisheeth K. Vishnoi
2022	J	`jnl`	Private Matrix Approximation and Geometry of Unitary Orbits. CoRR Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Guha Thakurta, Nisheeth K. Vishnoi
2022	A*	`conf`	Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. COLT Julian Zimmert, Naman Agarwal, Satyen Kale
2022	J	`jnl`	Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. CoRR Julian Zimmert, Naman Agarwal, Satyen Kale
2022	A*	`conf`	Reproducibility in Optimization: Theoretical Framework and Limits. NeurIPS Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
2022	J	`jnl`	Reproducibility in Optimization: Theoretical Framework and Limits. CoRR Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
2022	A*	`conf`	Self-Consistency of the Fokker Planck Equation. COLT Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani
2022	J	`jnl`	Self-Consistency of the Fokker-Planck Equation. CoRR Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani
2021	B	`conf`	A Deep Conditioning Treatment of Neural Networks. ALT Naman Agarwal, Pranjal Awasthi, Satyen Kale
2021	J	`jnl`	A Field Guide to Federated Optimization. CoRR 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 Horváth, 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
2021	J	`jnl`	A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness. CoRR Jacob D. Abernethy, Pranjal Awasthi, Satyen Kale
2021	A*	`conf`	Breaking the centralized barrier for cross-device federated learning. NeurIPS Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
2021	J	`jnl`	Efficient Methods for Online Multiclass Logistic Regression. CoRR Naman Agarwal, Satyen Kale, Julian Zimmert
2021	J	`jnl`	Federated Functional Gradient Boosting. CoRR Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi
2021	A*	`conf`	Learning with User-Level Privacy. NeurIPS Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
2021	J	`jnl`	Learning with User-Level Privacy. CoRR Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
2021	A*	`conf`	SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. NeurIPS Ayush Sekhari, Karthik Sridharan, Satyen Kale
2021	J	`jnl`	SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. CoRR Satyen Kale, Ayush Sekhari, Karthik Sridharan
2020	J	`jnl`	A Deep Conditioning Treatment of Neural Networks. CoRR Naman Agarwal, Pranjal Awasthi, Satyen Kale
2020	A*	`conf`	Estimating Training Data Influence by Tracing Gradient Descent. NeurIPS Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan
2020	J	`jnl`	Estimating Training Data Influence by Tracking Gradient Descent. CoRR Garima Pruthi, Frederick Liu, Mukund Sundararajan, Satyen Kale
2020	J	`jnl`	Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. CoRR Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
2020	A*	`conf`	PAC-Bayes Learning Bounds for Sample-Dependent Priors. NeurIPS Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri
2020	A*	`conf`	SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. ICML Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
2019	B	`conf`	Algorithmic Learning Theory 2019: Preface. ALT Aurélien Garivier, Satyen Kale
2019	B	`ed.`	Algorithmic Learning Theory, ALT 2019, 22-24 March 2019, Chicago, Illinois, USA. ALT Aurélien Garivier, Satyen Kale
2019	A*	`conf`	Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. NeurIPS Chuan Guo, Ali Mousavi, Xiang Wu, Daniel Niels Holtmann-Rice, Satyen Kale, Sashank J. Reddi, Sanjiv Kumar
2019	A*	`conf`	Escaping Saddle Points with Adaptive Gradient Methods. ICML Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra
2019	J	`jnl`	Escaping Saddle Points with Adaptive Gradient Methods. CoRR Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra
2019	A*	`conf`	Hypothesis Set Stability and Generalization. NeurIPS Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
2019	J	`jnl`	Hypothesis Set Stability and Generalization. CoRR Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
2019	J	`jnl`	On the Convergence of Adam and Beyond. CoRR Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
2019	J	`jnl`	SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. CoRR Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
2019	A	`conf`	Stochastic Negative Mining for Learning with Large Output Spaces. AISTATS Sashank J. Reddi, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Jiecao Chen, Sanjiv Kumar
2018	A*	`conf`	Adaptive Methods for Nonconvex Optimization. NeurIPS Manzil Zaheer, Sashank J. Reddi, Devendra Singh Sachan, Satyen Kale, Sanjiv Kumar
2018	A*	`conf`	Logistic Regression: The Importance of Being Improper. COLT Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
2018	J	`jnl`	Logistic Regression: The Importance of Being Improper. CoRR Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
2018	A*	`conf`	Loss Decomposition for Fast Learning in Large Output Spaces. ICML Ian En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar
2018	—	`conf`	On the Convergence of Adam and Beyond. ICLR Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
2018	A*	`conf`	Online Learning of Quantum States. NeurIPS Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak
2018	J	`jnl`	Parameter-free online learning via model selection. CoRR Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
2018	J	`jnl`	Stochastic Negative Mining for Learning with Large Output Spaces. CoRR Sashank J. Reddi, Satyen Kale, Felix X. Yu, Daniel N. Holtmann-Rice, Jiecao Chen, Sanjiv Kumar
2017	A*	`conf`	Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. ICML Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál
2017	J	`jnl`	Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. CoRR Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál
2017	J	`jnl`	Near-Optimal Algorithms for Online Matrix Prediction. SIAM J. Comput. Elad Hazan, Satyen Kale, Shai Shalev-Shwartz
2017	—	`conf`	Parameter-Free Online Learning via Model Selection. NIPS Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
2017	A*	`conf`	Preface: Conference on Learning Theory (COLT), 2017. COLT Satyen Kale, Ohad Shamir
2017	A*	`ed.`	Proceedings of the 30th Conference on Learning Theory, COLT 2017, Amsterdam, The Netherlands, 7-10 July 2017 COLT Satyen Kale, Ohad Shamir
2016	J	`jnl`	A Combinatorial, Primal-Dual Approach to Semidefinite Programs. J. ACM Sanjeev Arora, Satyen Kale
2016	—	`conf`	Hardness of Online Sleeping Combinatorial Optimization Problems. NIPS Satyen Kale, Chansoo Lee, Dávid Pál
2016	J	`jnl`	Learning rotations with little regret. Mach. Learn. Elad Hazan, Satyen Kale, Manfred K. Warmuth
2016	A*	`conf`	Online Semidefinite Programming. ICALP Noa Elad, Satyen Kale, Joseph (Seffi) Naor
2016	A*	`conf`	Online Sparse Linear Regression. COLT Dean P. Foster, Satyen Kale, Howard J. Karloff
2016	J	`jnl`	Online Sparse Linear Regression. CoRR Dean P. Foster, Satyen Kale, Howard J. Karloff
2016	A*	`conf`	Optimal and Adaptive Algorithms for Online Boosting. IJCAI Alina Beygelzimer, Satyen Kale, Haipeng Luo
2015	A*	`conf`	Budgeted Prediction with Expert Advice. AAAI Kareem Amin, Satyen Kale, Gerald Tesauro, Deepak S. Turaga
2015	J	`jnl`	Hardness of Online Sleeping Combinatorial Optimization Problems. CoRR Satyen Kale, Chansoo Lee, Dávid Pál
2015	—	`conf`	Online Gradient Boosting. NIPS Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo
2015	J	`jnl`	Online Gradient Boosting. CoRR Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo
2015	A*	`conf`	Optimal and Adaptive Algorithms for Online Boosting. ICML Alina Beygelzimer, Satyen Kale, Haipeng Luo
2015	J	`jnl`	Optimal and Adaptive Algorithms for Online Boosting. CoRR Alina Beygelzimer, Satyen Kale, Haipeng Luo
2015	A*	`ed.`	Proceedings of The 28th Conference on Learning Theory, COLT 2015, Paris, France, July 3-6, 2015 COLT Peter Grünwald, Elad Hazan, Satyen Kale
2014	J	`jnl`	Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization. J. Mach. Learn. Res. Elad Hazan, Satyen Kale
2014	A*	`conf`	Multiarmed Bandits With Limited Expert Advice. COLT Satyen Kale
2014	A*	`conf`	Open Problem: Efficient Online Sparse Regression. COLT Satyen Kale
2014	A*	`conf`	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. ICML Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
2014	J	`jnl`	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. CoRR Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
2013	—	`conf`	Adaptive Market Making via Online Learning. NIPS Jacob D. Abernethy, Satyen Kale
2013	J	`jnl`	Bargaining for Revenue Shares on Tree Trading Networks CoRR Arpita Ghosh, Satyen Kale, Kevin J. Lang, Benjamin Moseley
2013	A*	`conf`	Bargaining for Revenue Shares on Tree Trading Networks. IJCAI Arpita Ghosh, Satyen Kale, Kevin J. Lang, Benjamin Moseley
2013	J	`jnl`	Multiarmed Bandits With Limited Expert Advice. CoRR Satyen Kale
2013	J	`jnl`	Noise Tolerance of Expanders and Sublinear Expansion Reconstruction. SIAM J. Comput. Satyen Kale, Yuval Peres, C. Seshadhri
2013	—	`conf`	The Approximability of the Binary Paintshop Problem. APPROX-RANDOM Anupam Gupta, Satyen Kale, Viswanath Nagarajan, Rishi Saket, Baruch Schieber
2012	A*	`conf`	Commentary on "Online Optimization with Gradual Variations". COLT Satyen Kale
2012	J	`jnl`	Contextual Bandit Learning with Predictable Rewards CoRR Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire
2012	A	`conf`	Contextual Bandit Learning with Predictable Rewards. AISTATS Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire
2012	A*	`conf`	Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. ICML Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani
2012	J	`jnl`	Near-Optimal Algorithms for Online Matrix Prediction CoRR Elad Hazan, Satyen Kale, Shai Shalev-Shwartz
2012	A*	`conf`	Near-Optimal Algorithms for Online Matrix Prediction. COLT Elad Hazan, Satyen Kale, Shai Shalev-Shwartz
2012	J	`jnl`	Online submodular minimization. J. Mach. Learn. Res. Elad Hazan, Satyen Kale
2012	J	`jnl`	Projection-free Online Learning CoRR Elad Hazan, Satyen Kale
2012	A*	`conf`	Projection-free Online Learning. ICML Elad Hazan, Satyen Kale
2012	J	`jnl`	The Multiplicative Weights Update Method: a Meta-Algorithm and Applications. Theory Comput. Sanjeev Arora, Elad Hazan, Satyen Kale
2011	A*	`conf`	A simple multi-armed bandit algorithm with optimal variation-bounded regret. COLT Elad Hazan, Satyen Kale
2011	J	`jnl`	An Expansion Tester for Bounded Degree Graphs. SIAM J. Comput. Satyen Kale, C. Seshadhri
2011	J	`jnl`	Better Algorithms for Benign Bandits. J. Mach. Learn. Res. Elad Hazan, Satyen Kale
2011	A*	`conf`	Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization. COLT Elad Hazan, Satyen Kale
2011	A	`conf`	Combinatorial Approximation Algorithms for MaxCut using Random Walks. ICS Satyen Kale, C. Seshadhri
2011	A	`conf`	Cross-Validation and Mean-Square Stability. ICS Satyen Kale, Ravi Kumar, Sergei Vassilvitskii
2011	J	`jnl`	Efficient Optimal Learning for Contextual Bandits CoRR Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang
2011	A	`conf`	Efficient Optimal Learning for Contextual Bandits. UAI Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang
2011	—	`conf`	Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction. NIPS Elad Hazan, Satyen Kale
2011	A*	`conf`	Who moderates the moderators?: crowdsourcing abuse detection in user-generated content. EC Arpita Ghosh, Satyen Kale, R. Preston McAfee
2010	J	`jnl`	Combinatorial Approximation Algorithms for MaxCut using Random Walks CoRR Satyen Kale, C. Seshadhri
2010	J	`jnl`	Extracting certainty from uncertainty: regret bounded by variation in costs. Mach. Learn. Elad Hazan, Satyen Kale
2010	A*	`conf`	Learning Rotations with Little Regret. COLT Elad Hazan, Satyen Kale, Manfred K. Warmuth
2010	—	`conf`	Non-Stochastic Bandit Slate Problems. NIPS Satyen Kale, Lev Reyzin, Robert E. Schapire
2010	J	`jnl`	O(sqrt(log(n)) Approximation to SPARSEST CUT in Õ(n SIAM J. Comput. Sanjeev Arora, Elad Hazan, Satyen Kale
2010	A*	`conf`	On-line Variance Minimization in O(n2) per Trial? COLT Elad Hazan, Satyen Kale, Manfred K. Warmuth
2009	A*	`conf`	Better algorithms for benign bandits. SODA Elad Hazan, Satyen Kale
2009	—	`conf`	Beyond Convexity: Online Submodular Minimization. NIPS Elad Hazan, Satyen Kale
2009	—	`conf`	On Stochastic and Worst-case Models for Investing. NIPS Elad Hazan, Satyen Kale
2009	A*	`conf`	The uniform hardcore lemma via approximate Bregman projections. SODA Boaz Barak, Moritz Hardt, Satyen Kale
2008	—	`conf`	An Expansion Tester for Bounded Degree Graphs. ICALP (1) Satyen Kale, C. Seshadhri
2008	A*	`conf`	Extracting Certainty from Uncertainty: Regret Bounded by Variation in Costs. COLT Elad Hazan, Satyen Kale
2008	A*	`conf`	Noise Tolerance of Expanders and Sublinear Expander Reconstruction. FOCS Satyen Kale, Yuval Peres, C. Seshadhri
2007	A*	`conf`	A combinatorial, primal-dual approach to semidefinite programs. STOC Sanjeev Arora, Satyen Kale
2007	J	`jnl`	A variation on SVD based image compression. Image Vis. Comput. Abhiram G. Ranade, Srikanth S. Mahabalarao, Satyen Kale
2007	J	`jnl`	Boosting and hard-core set constructions: a simplified approach. Electron. Colloquium Comput. Complex. Satyen Kale
2007	—	`conf`	Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria. NIPS Elad Hazan, Satyen Kale
2007	A*	`conf`	Efficient aggregation algorithms for probabilistic data. SODA T. S. Jayram, Satyen Kale, Erik Vee
2007	J	`jnl`	Logarithmic regret algorithms for online convex optimization. Mach. Learn. Elad Hazan, Amit Agarwal, Satyen Kale
2007	A*	`conf`	Privacy, accuracy, and consistency too: a holistic solution to contingency table release. PODS Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, Kunal Talwar
2007	J	`jnl`	Testing Expansion in Bounded Degree Graphs. Electron. Colloquium Comput. Complex. Satyen Kale, C. Seshadhri
2006	—	`conf`	A Fast Random Sampling Algorithm for Sparsifying Matrices. APPROX-RANDOM Sanjeev Arora, Elad Hazan, Satyen Kale
2006	A*	`conf`	Algorithms for portfolio management based on the Newton method. ICML Amit Agarwal, Elad Hazan, Satyen Kale, Robert E. Schapire
2006	A*	`conf`	Logarithmic Regret Algorithms for Online Convex Optimization. COLT Elad Hazan, Adam Kalai, Satyen Kale, Amit Agarwal
2005	—	`conf`	Analysis and Algorithms for Content-Based Event Matching. ICDCS Workshops Satyen Kale, Elad Hazan, Fengyun Cao, Jaswinder Pal Singh
2005	A*	`conf`	Fast Algorithms for Approximate Semide.nite Programming using the Multiplicative Weights Update Method. FOCS Sanjeev Arora, Elad Hazan, Satyen Kale
2004	A*	`conf`	0(sqrt (log n)) Approximation to SPARSEST CUT in Õ(n FOCS Sanjeev Arora, Elad Hazan, Satyen Kale