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BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory

Oct 26, 2020
Amirali Aghazadeh, Vipul Gupta, Alex DeWeese, O. Ozan Koyluoglu, Kannan Ramchandran


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Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism

Oct 18, 2020
Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney

* 17 pages, 8 figures 

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FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning

Sep 23, 2020
Swanand Kadhe, Nived Rajaraman, O. Ozan Koyluoglu, Kannan Ramchandran

* Shorter version accepted in ICML Workshop on Federated Learning, July 2020, and CCS Workshop on Privacy-Preserving Machine Learning in Practice, November 2020 

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Boundary thickness and robustness in learning models

Jul 09, 2020
Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney


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Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits

Jun 15, 2020
Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran

* 24 pages, 8 figures 

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An Efficient Framework for Clustered Federated Learning

Jun 07, 2020
Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran

* 20 pages, 4 figures and 1 table 

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Communication-Efficient Gradient Coding for Straggler Mitigation in Distributed Learning

May 14, 2020
Swanand Kadhe, O. Ozan Koyluoglu, Kannan Ramchandran

* Shorter version accepted in 2020 IEEE International Symposium on Information Theory (ISIT) 

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Alternating Minimization Converges Super-Linearly for Mixed Linear Regression

Apr 23, 2020
Avishek Ghosh, Kannan Ramchandran

* Accepted for publication at AISTATS, 2020 

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Communication-Efficient and Byzantine-Robust Distributed Learning

Nov 21, 2019
Avishek Ghosh, Raj Kumar Maity, Swanand Kadhe, Arya Mazumdar, Kannan Ramchandran


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Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation

Jun 21, 2019
Avishek Ghosh, Ashwin Pananjady, Adityanand Guntuboyina, Kannan Ramchandran

* The first two authors contributed equally to this work and are ordered alphabetically 

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Robust Federated Learning in a Heterogeneous Environment

Jun 16, 2019
Avishek Ghosh, Justin Hong, Dong Yin, Kannan Ramchandran

* 30 pages, 4 figures 

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Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion

May 09, 2019
Orhan Ocal, Oguz H. Elibol, Gokce Keskin, Cory Stephenson, Anil Thomas, Kannan Ramchandran


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Gradient Coding Based on Block Designs for Mitigating Adversarial Stragglers

Apr 30, 2019
Swanand Kadhe, O. Ozan Koyluoglu, Kannan Ramchandran

* Shorter version accepted in 2019 IEEE International Symposium on Information Theory (ISIT) 

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OverSketched Newton: Fast Convex Optimization for Serverless Systems

Mar 21, 2019
Vipul Gupta, Swanand Kadhe, Thomas Courtade, Michael W. Mahoney, Kannan Ramchandran

* 27 pages, 11 figures 

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Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples

Jan 24, 2019
Kamil Nar, Orhan Ocal, S. Shankar Sastry, Kannan Ramchandran


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Frank-Wolfe Algorithm for Exemplar Selection

Nov 06, 2018
Gary Cheng, Armin Askari, Laurent El Ghaoui, Kannan Ramchandran


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Rademacher Complexity for Adversarially Robust Generalization

Oct 29, 2018
Dong Yin, Kannan Ramchandran, Peter Bartlett


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Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning

Sep 14, 2018
Dong Yin, Yudong Chen, Kannan Ramchandran, Peter Bartlett


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Online Scoring with Delayed Information: A Convex Optimization Viewpoint

Jul 09, 2018
Avishek Ghosh, Kannan Ramchandran

* 8 pages, 4 figures 

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Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Mar 05, 2018
Dong Yin, Yudong Chen, Kannan Ramchandran, Peter Bartlett


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Speeding Up Distributed Machine Learning Using Codes

Jan 29, 2018
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, Kannan Ramchandran

* This work is published in IEEE Transactions on Information Theory and presented in part at the NIPS 2015 Workshop on Machine Learning Systems and the IEEE ISIT 2016 

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Gradient Diversity: a Key Ingredient for Scalable Distributed Learning

Jan 07, 2018
Dong Yin, Ashwin Pananjady, Max Lam, Dimitris Papailiopoulos, Kannan Ramchandran, Peter Bartlett


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Approximate Ranking from Pairwise Comparisons

Jan 04, 2018
Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright

* AISTATS 2017 

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A Sequential Approximation Framework for Coded Distributed Optimization

Oct 24, 2017
Jingge Zhu, Ye Pu, Vipul Gupta, Claire Tomlin, Kannan Ramchandran

* presented in 55th Annual Allerton Conference on Communication, Control, and Computing, Oct. 2017 

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The Sample Complexity of Online One-Class Collaborative Filtering

May 31, 2017
Reinhard Heckel, Kannan Ramchandran

* ICML 2017 

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Active Ranking from Pairwise Comparisons and when Parametric Assumptions Don't Help

Sep 23, 2016
Reinhard Heckel, Nihar B. Shah, Kannan Ramchandran, Martin J. Wainwright

* improved log factor in main result; added discussion on comparison probabilities close to zero; added numerical results 

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CYCLADES: Conflict-free Asynchronous Machine Learning

May 31, 2016
Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Chris Re, Benjamin Recht


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Perturbed Iterate Analysis for Asynchronous Stochastic Optimization

Mar 25, 2016
Horia Mania, Xinghao Pan, Dimitris Papailiopoulos, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan

* 30 pages 

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SPRIGHT: A Fast and Robust Framework for Sparse Walsh-Hadamard Transform

Aug 26, 2015
Xiao Li, Joseph K. Bradley, Sameer Pawar, Kannan Ramchandran

* Part of our results was reported in ISIT 2014, titled "The SPRIGHT algorithm for robust sparse Hadamard Transforms." 

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