Satyen Kale

According to our database1, Satyen Kale authored at least 99 papers between 2004 and 2024.

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Bibliography

2024
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data.
Trans. Mach. Learn. Res., 2024

Stacking as Accelerated Gradient Descent.
CoRR, 2024

Efficient Stagewise Pretraining via Progressive Subnetworks.
CoRR, 2024

Asynchronous Local-SGD Training for Language Modeling.
CoRR, 2024

Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Semi-supervised Group DRO: Combating Sparsity with Unlabeled Data.
Proceedings of the International Conference on Algorithmic Learning Theory, 2024

2023
Improved Differentially Private and Lazy Online Convex Optimization.
CoRR, 2023

Improved Differentially Private Densest Subgraph: Local and Purely Additive.
CoRR, 2023

Efficient Training of Language Models using Few-Shot Learning.
Proceedings of the International Conference on Machine Learning, 2023

Beyond Uniform Lipschitz Condition in Differentially Private Optimization.
Proceedings of the International Conference on Machine Learning, 2023

On the Convergence of Federated Averaging with Cyclic Client Participation.
Proceedings of the International Conference on Machine Learning, 2023

Differentially Private and Lazy Online Convex Optimization.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Mixed Federated Learning: Joint Decentralized and Centralized Learning.
CoRR, 2022

From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Reproducibility in Optimization: Theoretical Framework and Limits.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Agnostic Learnability of Halfspaces via Logistic Loss.
Proceedings of the International Conference on Machine Learning, 2022

Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Self-Consistency of the Fokker Planck Equation.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Private Matrix Approximation and Geometry of Unitary Orbits.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Efficient Methods for Online Multiclass Logistic Regression.
Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

Federated Functional Gradient Boosting.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
A Field Guide to Federated Optimization.
CoRR, 2021

A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness.
CoRR, 2021

SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning with User-Level Privacy.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Breaking the centralized barrier for cross-device federated learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Deep Conditioning Treatment of Neural Networks.
Proceedings of the Algorithmic Learning Theory, 2021

2020
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning.
CoRR, 2020

Estimating Training Data Influence by Tracking Gradient Descent.
CoRR, 2020

Estimating Training Data Influence by Tracing Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

PAC-Bayes Learning Bounds for Sample-Dependent Priors.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning.
CoRR, 2019

Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Hypothesis Set Stability and Generalization.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Escaping Saddle Points with Adaptive Gradient Methods.
Proceedings of the 36th International Conference on Machine Learning, 2019

Algorithmic Learning Theory 2019: Preface.
Proceedings of the Algorithmic Learning Theory, 2019

Stochastic Negative Mining for Learning with Large Output Spaces.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Adaptive Methods for Nonconvex Optimization.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Online Learning of Quantum States.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Loss Decomposition for Fast Learning in Large Output Spaces.
Proceedings of the 35th International Conference on Machine Learning, 2018

On the Convergence of Adam and Beyond.
Proceedings of the 6th International Conference on Learning Representations, 2018

Logistic Regression: The Importance of Being Improper.
Proceedings of the Conference On Learning Theory, 2018

2017
Near-Optimal Algorithms for Online Matrix Prediction.
SIAM J. Comput., 2017

Parameter-Free Online Learning via Model Selection.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP.
Proceedings of the 34th International Conference on Machine Learning, 2017

Preface: Conference on Learning Theory (COLT), 2017.
Proceedings of the 30th Conference on Learning Theory, 2017

2016
Learning rotations with little regret.
Mach. Learn., 2016

A Combinatorial, Primal-Dual Approach to Semidefinite Programs.
J. ACM, 2016

Hardness of Online Sleeping Combinatorial Optimization Problems.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Online Semidefinite Programming.
Proceedings of the 43rd International Colloquium on Automata, Languages, and Programming, 2016

Online Sparse Linear Regression.
Proceedings of the 29th Conference on Learning Theory, 2016

2015
Online Gradient Boosting.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Optimal and Adaptive Algorithms for Online Boosting.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Budgeted Prediction with Expert Advice.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization.
J. Mach. Learn. Res., 2014

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits.
Proceedings of the 31th International Conference on Machine Learning, 2014

Open Problem: Efficient Online Sparse Regression.
Proceedings of The 27th Conference on Learning Theory, 2014

Multiarmed Bandits With Limited Expert Advice.
Proceedings of The 27th Conference on Learning Theory, 2014

2013
Noise Tolerance of Expanders and Sublinear Expansion Reconstruction.
SIAM J. Comput., 2013

Adaptive Market Making via Online Learning.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Bargaining for Revenue Shares on Tree Trading Networks.
Proceedings of the IJCAI 2013, 2013

The Approximability of the Binary Paintshop Problem.
Proceedings of the Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 2013

2012
The Multiplicative Weights Update Method: a Meta-Algorithm and Applications.
Theory Comput., 2012

Commentary on "Online Optimization with Gradual Variations".
Proceedings of the COLT 2012, 2012

Online submodular minimization.
J. Mach. Learn. Res., 2012

Contextual Bandit Learning with Predictable Rewards.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Projection-free Online Learning.
Proceedings of the 29th International Conference on Machine Learning, 2012

Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
An Expansion Tester for Bounded Degree Graphs.
SIAM J. Comput., 2011

A simple multi-armed bandit algorithm with optimal variation-bounded regret.
Proceedings of the COLT 2011, 2011

Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization.
Proceedings of the COLT 2011, 2011

Better Algorithms for Benign Bandits.
J. Mach. Learn. Res., 2011

Efficient Optimal Learning for Contextual Bandits.
Proceedings of the UAI 2011, 2011

Who moderates the moderators?: crowdsourcing abuse detection in user-generated content.
Proceedings of the Proceedings 12th ACM Conference on Electronic Commerce (EC-2011), 2011

Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Combinatorial Approximation Algorithms for MaxCut using Random Walks.
Proceedings of the Innovations in Computer Science, 2011

Cross-Validation and Mean-Square Stability.
Proceedings of the Innovations in Computer Science, 2011

2010
O(sqrt(log(n)) Approximation to SPARSEST CUT in Õ(n<sup>2</sup>) Time.
SIAM J. Comput., 2010

Extracting certainty from uncertainty: regret bounded by variation in costs.
Mach. Learn., 2010

Non-Stochastic Bandit Slate Problems.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

On-line Variance Minimization in O(n2) per Trial?
Proceedings of the COLT 2010, 2010

2009
The uniform hardcore lemma via approximate Bregman projections.
Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2009

On Stochastic and Worst-case Models for Investing.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Beyond Convexity: Online Submodular Minimization.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008
Noise Tolerance of Expanders and Sublinear Expander Reconstruction.
Proceedings of the 49th Annual IEEE Symposium on Foundations of Computer Science, 2008

2007
Logarithmic regret algorithms for online convex optimization.
Mach. Learn., 2007

A variation on SVD based image compression.
Image Vis. Comput., 2007

Testing Expansion in Bounded Degree Graphs.
Electron. Colloquium Comput. Complex., 2007

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

Efficient aggregation algorithms for probabilistic data.
Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2007

Privacy, accuracy, and consistency too: a holistic solution to contingency table release.
Proceedings of the Twenty-Sixth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 2007

Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006
Algorithms for portfolio management based on the Newton method.
Proceedings of the Machine Learning, 2006

Logarithmic Regret Algorithms for Online Convex Optimization.
Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006

A Fast Random Sampling Algorithm for Sparsifying Matrices.
Proceedings of the Approximation, 2006

2005
Analysis and Algorithms for Content-Based Event Matching.
Proceedings of the 25th International Conference on Distributed Computing Systems Workshops (ICDCS 2005 Workshops), 2005

Fast Algorithms for Approximate Semide.nite Programming using the Multiplicative Weights Update Method.
Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2005), 2005

2004
0(sqrt (log n)) Approximation to SPARSEST CUT in Õ(n<sup>2</sup>) Time.
Proceedings of the 45th Symposium on Foundations of Computer Science (FOCS 2004), 2004


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