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2020 – today
- 2024
- [j14]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) - [c79]Pranjal Awasthi, Satyen Kale, Ankit Pensia:
Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data. ALT 2024: 125-160 - [c78]Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta:
Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements. ICML 2024 - [i46]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 abs/2401.09135 (2024) - [i45]Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu, Sobhan Miryoosefi, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
Efficient Stagewise Pretraining via Progressive Subnetworks. CoRR abs/2402.05913 (2024) - [i44]Naman Agarwal, Pranjal Awasthi, Satyen Kale, Eric Zhao:
Stacking as Accelerated Gradient Descent. CoRR abs/2403.04978 (2024) - 2023
- [c77]Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta:
Differentially Private and Lazy Online Convex Optimization. COLT 2023: 4599-4632 - [c76]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: 5677-5721 - [c75]Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi:
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. ICML 2023: 7066-7101 - [c74]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: 14553-14568 - [i43]Yae Jee Cho, Pranay Sharma
On the Convergence of Federated Averaging with Cyclic Client Participation. CoRR abs/2302.03109 (2023) - [i42]Michael Dinitz, Satyen Kale, Silvio Lattanzi, Sergei Vassilvitskii:
Improved Differentially Private Densest Subgraph: Local and Purely Additive. CoRR abs/2308.10316 (2023) - [i41]Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta:
Improved Differentially Private and Lazy Online Convex Optimization. CoRR abs/2312.11534 (2023) - 2022
- [c73]Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi:
Federated Functional Gradient Boosting. AISTATS 2022: 7814-7840 - [c72]Naman Agarwal, Satyen Kale, Julian Zimmert:
Efficient Methods for Online Multiclass Logistic Regression. ALT 2022: 3-33 - [c71]Julian Zimmert, Naman Agarwal, Satyen Kale:
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. COLT 2022: 182-226 - [c70]Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani:
Self-Consistency of the Fokker Planck Equation. COLT 2022: 817-841 - [c69]Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Thakurta, Nisheeth K. Vishnoi:
Private Matrix Approximation and Geometry of Unitary Orbits. COLT 2022: 3547-3588 - [c68]Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp:
Agnostic Learnability of Halfspaces via Logistic Loss. ICML 2022: 10068-10103 - [c67]Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir:
Reproducibility in Optimization: Theoretical Framework and Limits. NeurIPS 2022 - [c66]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 - [i40]Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp:
Agnostic Learnability of Halfspaces via Logistic Loss. CoRR abs/2201.13419 (2022) - [i39]Julian Zimmert, Naman Agarwal, Satyen Kale:
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. CoRR abs/2202.02765 (2022) - [i38]Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir:
Reproducibility in Optimization: Theoretical Framework and Limits. CoRR abs/2202.04598 (2022) - [i37]Sean Augenstein, Andrew Hard, Lin Ning, Karan Singhal, Satyen Kale, Kurt Partridge, Rajiv Mathews:
Mixed Federated Learning: Joint Decentralized and Centralized Learning. CoRR abs/2205.13655 (2022) - [i36]Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani:
Self-Consistency of the Fokker-Planck Equation. CoRR abs/2206.00860 (2022) - [i35]Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang:
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. CoRR abs/2206.04723 (2022) - [i34]Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi:
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. CoRR abs/2206.10713 (2022) - [i33]Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Guha Thakurta, Nisheeth K. Vishnoi:
Private Matrix Approximation and Geometry of Unitary Orbits. CoRR abs/2207.02794 (2022) - [i32]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 abs/2210.06705 (2022) - 2021
- [c65]Naman Agarwal, Pranjal Awasthi, Satyen Kale:
A Deep Conditioning Treatment of Neural Networks. ALT 2021: 249-305 - [c64]Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh:
Learning with User-Level Privacy. NeurIPS 2021: 12466-12479 - [c63]Ayush Sekhari, Karthik Sridharan, Satyen Kale:
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. NeurIPS 2021: 27422-27433 - [c62]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: 28663-28676 - [i31]Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh:
Learning with User-Level Privacy. CoRR abs/2102.11845 (2021) - [i30]Jacob D. Abernethy, Pranjal Awasthi, Satyen Kale:
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness. CoRR abs/2103.01276 (2021) - [i29]Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi:
Federated Functional Gradient Boosting. CoRR abs/2103.06972 (2021) - [i28]Satyen Kale, Ayush Sekhari, Karthik Sridharan:
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. CoRR abs/2107.05074 (2021) - [i27]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
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i26]Naman Agarwal, Satyen Kale, Julian Zimmert:
Efficient Methods for Online Multiclass Logistic Regression. CoRR abs/2110.03020 (2021) - 2020
- [c61]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: 5132-5143 - [c60]Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri:
PAC-Bayes Learning Bounds for Sample-Dependent Priors. NeurIPS 2020 - [c59]Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan:
Estimating Training Data Influence by Tracing Gradient Descent. NeurIPS 2020 - [i25]Naman Agarwal, Pranjal Awasthi, Satyen Kale:
A Deep Conditioning Treatment of Neural Networks. CoRR abs/2002.01523 (2020) - [i24]Garima Pruthi, Frederick Liu, Mukund Sundararajan, Satyen Kale:
Estimating Training Data Influence by Tracking Gradient Descent. CoRR abs/2002.08484 (2020) - [i23]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 abs/2008.03606 (2020)
2010 – 2019
- 2019
- [c58]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: 1940-1949 - [c57]Aurélien Garivier, Satyen Kale:
Algorithmic Learning Theory 2019: Preface. ALT 2019: 1-2 - [c56]Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra:
Escaping Saddle Points with Adaptive Gradient Methods. ICML 2019: 5956-5965 - [c55]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: 4944-4954 - [c54]Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Hypothesis Set Stability and Generalization. NeurIPS 2019: 6726-6736 - [e3]Aurélien Garivier, Satyen Kale:
Algorithmic Learning Theory, ALT 2019, 22-24 March 2019, Chicago, Illinois, USA. Proceedings of Machine Learning Research 98, PMLR 2019 [contents] - [i22]Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra:
Escaping Saddle Points with Adaptive Gradient Methods. CoRR abs/1901.09149 (2019) - [i21]Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Hypothesis Set Stability and Generalization. CoRR abs/1904.04755 (2019) - [i20]Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
On the Convergence of Adam and Beyond. CoRR abs/1904.09237 (2019) - [i19]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 abs/1910.06378 (2019) - 2018
- [c53]Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Logistic Regression: The Importance of Being Improper. COLT 2018: 167-208 - [c52]Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
On the Convergence of Adam and Beyond. ICLR 2018 - [c51]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: 5626-5635 - [c50]Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak:
Online Learning of Quantum States. NeurIPS 2018: 8976-8986 - [c49]Manzil Zaheer, Sashank J. Reddi, Devendra Singh Sachan, Satyen Kale, Sanjiv Kumar:
Adaptive Methods for Nonconvex Optimization. NeurIPS 2018: 9815-9825 - [i18]Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan:
Parameter-free online learning via model selection. CoRR abs/1801.00101 (2018) - [i17]Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Logistic Regression: The Importance of Being Improper. CoRR abs/1803.09349 (2018) - [i16]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 abs/1810.07076 (2018) - 2017
- [j13]Elad Hazan
Near-Optimal Algorithms for Online Matrix Prediction. SIAM J. Comput. 46(2): 744-773 (2017) - [c48]Satyen Kale, Ohad Shamir:
Preface: Conference on Learning Theory (COLT), 2017. COLT 2017: 1-3 - [c47]Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál:
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. ICML 2017: 1780-1788 - [c46]Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan:
Parameter-Free Online Learning via Model Selection. NIPS 2017: 6020-6030 - [e2]Satyen Kale, Ohad Shamir:
Proceedings of the 30th Conference on Learning Theory, COLT 2017, Amsterdam, The Netherlands, 7-10 July 2017. Proceedings of Machine Learning Research 65, PMLR 2017 [contents] - [i15]Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál:
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. CoRR abs/1706.04690 (2017) - 2016
- [j12]Sanjeev Arora, Satyen Kale:
A Combinatorial, Primal-Dual Approach to Semidefinite Programs. J. ACM 63(2): 12:1-12:35 (2016) - [j11]Elad Hazan
Learning rotations with little regret. Mach. Learn. 104(1): 129-148 (2016) - [c45]Dean P. Foster, Satyen Kale, Howard J. Karloff:
Online Sparse Linear Regression. COLT 2016: 960-970 - [c44]Noa Elad, Satyen Kale, Joseph (Seffi) Naor:
Online Semidefinite Programming. ICALP 2016: 40:1-40:13 - [c43]Alina Beygelzimer, Satyen Kale, Haipeng Luo:
Optimal and Adaptive Algorithms for Online Boosting. IJCAI 2016: 4120-4124 - [c42]Satyen Kale, Chansoo Lee, Dávid Pál:
Hardness of Online Sleeping Combinatorial Optimization Problems. NIPS 2016: 2181-2189 - [i14]Dean P. Foster, Satyen Kale, Howard J. Karloff:
Online Sparse Linear Regression. CoRR abs/1603.02250 (2016) - 2015
- [c41]Kareem Amin, Satyen Kale, Gerald Tesauro, Deepak S. Turaga:
Budgeted Prediction with Expert Advice. AAAI 2015: 2490-2496 - [c40]Alina Beygelzimer, Satyen Kale, Haipeng Luo:
Optimal and Adaptive Algorithms for Online Boosting. ICML 2015: 2323-2331 - [c39]Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo:
Online Gradient Boosting. NIPS 2015: 2458-2466 - [e1]Peter Grünwald, Elad Hazan, Satyen Kale:
Proceedings of The 28th Conference on Learning Theory, COLT 2015, Paris, France, July 3-6, 2015. JMLR Workshop and Conference Proceedings 40, JMLR.org 2015 [contents] - [i13]Alina Beygelzimer, Satyen Kale, Haipeng Luo:
Optimal and Adaptive Algorithms for Online Boosting. CoRR abs/1502.02651 (2015) - [i12]Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo:
Online Gradient Boosting. CoRR abs/1506.04820 (2015) - [i11]Satyen Kale, Chansoo Lee, Dávid Pál:
Hardness of Online Sleeping Combinatorial Optimization Problems. CoRR abs/1509.03600 (2015) - 2014
- [j10]Elad Hazan, Satyen Kale:
Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization. J. Mach. Learn. Res. 15(1): 2489-2512 (2014) - [c38]Satyen Kale:
Multiarmed Bandits With Limited Expert Advice. COLT 2014: 107-122 - [c37]Satyen Kale:
Open Problem: Efficient Online Sparse Regression. COLT 2014: 1299-1301 - [c36]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: 1638-1646 - [i10]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 abs/1402.0555 (2014) - 2013
- [j9]Satyen Kale, Yuval Peres, C. Seshadhri:
Noise Tolerance of Expanders and Sublinear Expansion Reconstruction. SIAM J. Comput. 42(1): 305-323 (2013) - [c35]Anupam Gupta, Satyen Kale, Viswanath Nagarajan, Rishi Saket, Baruch Schieber:
The Approximability of the Binary Paintshop Problem. APPROX-RANDOM 2013: 205-217 - [c34]Arpita Ghosh, Satyen Kale, Kevin J. Lang, Benjamin Moseley:
Bargaining for Revenue Shares on Tree Trading Networks. IJCAI 2013: 129-135 - [c33]Jacob D. Abernethy, Satyen Kale:
Adaptive Market Making via Online Learning. NIPS 2013: 2058-2066 - [i9]Arpita Ghosh, Satyen Kale, Kevin J. Lang, Benjamin Moseley:
Bargaining for Revenue Shares on Tree Trading Networks. CoRR abs/1304.5822 (2013) - [i8]Satyen Kale:
Multiarmed Bandits With Limited Expert Advice. CoRR abs/1306.4653 (2013) - 2012
- [j8]Elad Hazan, Satyen Kale:
Online submodular minimization. J. Mach. Learn. Res. 13: 2903-2922 (2012) - [j7]Sanjeev Arora, Elad Hazan, Satyen Kale:
The Multiplicative Weights Update Method: a Meta-Algorithm and Applications. Theory Comput. 8(1): 121-164 (2012) - [c32]Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani:
Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. ICML 2012 - [c31]Elad Hazan, Satyen Kale:
Projection-free Online Learning. ICML 2012 - [c30]Satyen Kale:
Commentary on "Online Optimization with Gradual Variations". COLT 2012: 6.21-6.24 - [c29]Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire:
Contextual Bandit Learning with Predictable Rewards. AISTATS 2012: 19-26 - [c28]Elad Hazan, Satyen Kale, Shai Shalev-Shwartz:
Near-Optimal Algorithms for Online Matrix Prediction. COLT 2012: 38.1-38.13 - [i7]Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire:
Contextual Bandit Learning with Predictable Rewards. CoRR abs/1202.1334 (2012) - [i6]Elad Hazan, Satyen Kale, Shai Shalev-Shwartz:
Near-Optimal Algorithms for Online Matrix Prediction. CoRR abs/1204.0136 (2012) - [i5]Elad Hazan, Satyen Kale:
Projection-free Online Learning. CoRR abs/1206.4657 (2012) - 2011
- [j6]Elad Hazan, Satyen Kale:
Better Algorithms for Benign Bandits. J. Mach. Learn. Res. 12: 1287-1311 (2011) - [j5]Satyen Kale, C. Seshadhri:
An Expansion Tester for Bounded Degree Graphs. SIAM J. Comput. 40(3): 709-720 (2011) - [c27]Satyen Kale, C. Seshadhri:
Combinatorial Approximation Algorithms for MaxCut using Random Walks. ICS 2011: 367-388 - [c26]Satyen Kale, Ravi Kumar, Sergei Vassilvitskii:
Cross-Validation and Mean-Square Stability. ICS 2011: 487-495 - [c25]Elad Hazan, Satyen Kale:
Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction. NIPS 2011: 891-899 - [c24]Arpita Ghosh, Satyen Kale, R. Preston McAfee:
Who moderates the moderators?: crowdsourcing abuse detection in user-generated content. EC 2011: 167-176 - [c23]Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang:
Efficient Optimal Learning for Contextual Bandits. UAI 2011: 169-178 - [c22]Elad Hazan, Satyen Kale:
Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization. COLT 2011: 421-436 - [c21]Elad Hazan, Satyen Kale:
A simple multi-armed bandit algorithm with optimal variation-bounded regret. COLT 2011: 817-820 - [i4]Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang:
Efficient Optimal Learning for Contextual Bandits. CoRR abs/1106.2369 (2011) - 2010
- [j4]Elad Hazan
Extracting certainty from uncertainty: regret bounded by variation in costs. Mach. Learn. 80(2-3): 165-188 (2010) - [j3]Sanjeev Arora, Elad Hazan
O(sqrt(log(n)) Approximation to SPARSEST CUT in Õ(n2) Time. SIAM J. Comput. 39(5): 1748-1771 (2010) - [c20]Elad Hazan, Satyen Kale, Manfred K. Warmuth:
Learning Rotations with Little Regret. COLT 2010: 144-154 - [c19]Elad Hazan, Satyen Kale, Manfred K. Warmuth:
On-line Variance Minimization in O(n2) per Trial? COLT 2010: 314-315 - [c18]Satyen Kale, Lev Reyzin, Robert E. Schapire:
Non-Stochastic Bandit Slate Problems. NIPS 2010: 1054-1062 - [i3]Satyen Kale, C. Seshadhri:
Combinatorial Approximation Algorithms for MaxCut using Random Walks. CoRR abs/1008.3938 (2010)
2000 – 2009
- 2009
- [c17]Elad Hazan, Satyen Kale:
Beyond Convexity: Online Submodular Minimization. NIPS 2009: 700-708 - [c16]Elad Hazan, Satyen Kale:
On Stochastic and Worst-case Models for Investing. NIPS 2009: 709-717 - [c15]Elad Hazan, Satyen Kale:
Better algorithms for benign bandits. SODA 2009: 38-47 - [c14]Boaz Barak, Moritz Hardt, Satyen Kale:
The uniform hardcore lemma via approximate Bregman projections. SODA 2009: 1193-1200 - 2008
- [c13]Elad Hazan, Satyen Kale:
Extracting Certainty from Uncertainty: Regret Bounded by Variation in Costs. COLT 2008: 57-68 - [c12]Satyen Kale, Yuval Peres, C. Seshadhri:
Noise Tolerance of Expanders and Sublinear Expander Reconstruction. FOCS 2008: 719-728 - [c11]Satyen Kale, C. Seshadhri:
An Expansion Tester for Bounded Degree Graphs. ICALP (1) 2008: 527-538 - 2007
- [j2]Abhiram G. Ranade, Srikanth S. Mahabalarao, Satyen Kale:
A variation on SVD based image compression. Image Vis. Comput. 25(6): 771-777 (2007) - [j1]Elad Hazan
Logarithmic regret algorithms for online convex optimization. Mach. Learn. 69(2-3): 169-192 (2007) - [c10]Elad Hazan, Satyen Kale:
Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria. NIPS 2007: 625-632 - [c9]Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, Kunal Talwar:
Privacy, accuracy, and consistency too: a holistic solution to contingency table release. PODS 2007: 273-282 - [c8]T. S. Jayram, Satyen Kale, Erik Vee:
Efficient aggregation algorithms for probabilistic data. SODA 2007: 346-355 - [c7]Sanjeev Arora, Satyen Kale:
A combinatorial, primal-dual approach to semidefinite programs. STOC 2007: 227-236 - [i2]Satyen Kale:
Boosting and hard-core set constructions: a simplified approach. Electron. Colloquium Comput. Complex. TR07 (2007) - [i1]Satyen Kale, C. Seshadhri:
Testing Expansion in Bounded Degree Graphs. Electron. Colloquium Comput. Complex. TR07 (2007) - 2006
- [c6]Sanjeev Arora, Elad Hazan, Satyen Kale:
A Fast Random Sampling Algorithm for Sparsifying Matrices. APPROX-RANDOM 2006: 272-279 - [c5]Elad Hazan, Adam Kalai, Satyen Kale, Amit Agarwal:
Logarithmic Regret Algorithms for Online Convex Optimization. COLT 2006: 499-513 - [c4]Amit Agarwal, Elad Hazan
Algorithms for portfolio management based on the Newton method. ICML 2006: 9-16 - 2005
- [c3]Sanjeev Arora, Elad Hazan
Fast Algorithms for Approximate Semide.nite Programming using the Multiplicative Weights Update Method. FOCS 2005: 339-348 - [c2]Satyen Kale, Elad Hazan
Analysis and Algorithms for Content-Based Event Matching. ICDCS Workshops 2005: 363-369 - 2004
- [c1]Sanjeev Arora, Elad Hazan, Satyen Kale:
0(sqrt (log n)) Approximation to SPARSEST CUT in Õ(n2) Time. FOCS 2004: 238-247
Coauthor Index
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load citations from opencitations.net
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last updated on 2024-10-08 21:31 CEST by the dblp team
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