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ORCIDShashank Rajput
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2020 – today
- 2020
[c5]Shashank Rajput, Anant Gupta, Dimitris S. Papailiopoulos:
Closing the convergence gap of SGD without replacement. ICML 2020: 7964-7973- [c4]Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris S. Papailiopoulos:
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient. NeurIPS 2020 - [c3]Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris S. Papailiopoulos:
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. NeurIPS 2020 - [i6]Shashank Rajput, Anant Gupta, Dimitris S. Papailiopoulos:
Closing the convergence gap of SGD without replacement. CoRR abs/2002.10400 (2020) - [i5]Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris S. Papailiopoulos:
Optimal Lottery Tickets via SubsetSum: Logarithmic Over-Parameterization is Sufficient. CoRR abs/2006.07990 (2020) - [i4]Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris S. Papailiopoulos:
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. CoRR abs/2007.05084 (2020)
2010 – 2019
- 2019
- [c2]Shashank Rajput, Zhili Feng, Zachary B. Charles, Po-Ling Loh, Dimitris S. Papailiopoulos:
Does Data Augmentation Lead to Positive Margin? ICML 2019: 5321-5330 - [c1]Shashank Rajput, Hongyi Wang, Zachary B. Charles, Dimitris S. Papailiopoulos:
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. NeurIPS 2019: 10320-10330 - [i3]Shashank Rajput, Zhili Feng, Zachary B. Charles, Po-Ling Loh, Dimitris S. Papailiopoulos:
Does Data Augmentation Lead to Positive Margin? CoRR abs/1905.03177 (2019) - [i2]Zachary B. Charles, Shashank Rajput, Stephen Wright, Dimitris S. Papailiopoulos:
Convergence and Margin of Adversarial Training on Separable Data. CoRR abs/1905.09209 (2019) - [i1]Shashank Rajput, Hongyi Wang, Zachary B. Charles, Dimitris S. Papailiopoulos:
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. CoRR abs/1907.12205 (2019)
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