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ORCIDVaishnavh Nagarajan
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2020 – today
- 2020
[j2]Maria-Florina Balcan, Avrim Blum, Vaishnavh Nagarajan:
Lifelong learning in costly feature spaces. Theor. Comput. Sci. 808: 14-37 (2020)- [i11]Melrose Roderick, Vaishnavh Nagarajan, J. Zico Kolter:
Provably Safe PAC-MDP Exploration Using Analogies. CoRR abs/2007.03574 (2020) - [i10]Vaishnavh Nagarajan, Anders Andreassen, Behnam Neyshabur:
Understanding the Failure Modes of Out-of-Distribution Generalization. CoRR abs/2010.15775 (2020) - [i9]Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar:
A Learning Theoretic Perspective on Local Explainability. CoRR abs/2011.01205 (2020)
2010 – 2019
- 2019
- [c12]Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar:
Revisiting Adversarial Risk. AISTATS 2019: 2331-2339 - [c11]Vaishnavh Nagarajan, J. Zico Kolter:
Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience. ICLR (Poster) 2019 - [c10]Vaishnavh Nagarajan, J. Zico Kolter:
Uniform convergence may be unable to explain generalization in deep learning. NeurIPS 2019: 11611-11622 - [i8]Vaishnavh Nagarajan, J. Zico Kolter:
Generalization in Deep Networks: The Role of Distance from Initialization. CoRR abs/1901.01672 (2019) - [i7]Vaishnavh Nagarajan, J. Zico Kolter:
Uniform convergence may be unable to explain generalization in deep learning. CoRR abs/1902.04742 (2019) - [i6]Vaishnavh Nagarajan, J. Zico Kolter:
Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience. CoRR abs/1905.13344 (2019) - 2018
- [c9]Saurabh Kadekodi, Vaishnavh Nagarajan, Gregory R. Ganger:
Geriatrix: Aging what you see and what you don't see. A file system aging approach for modern storage systems. USENIX Annual Technical Conference 2018: 691-704 - [i5]Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar:
On Adversarial Risk and Training. CoRR abs/1806.02924 (2018) - 2017
- [j1]Leandro Soriano Marcolino
, Aravind S. Lakshminarayanan, Vaishnavh Nagarajan, Milind Tambe:
Every team deserves a second chance: an extended study on predicting team performance. Auton. Agents Multi Agent Syst. 31(5): 1003-1054 (2017) - [c8]Maria-Florina Balcan, Avrim Blum, Vaishnavh Nagarajan:
Lifelong Learning in Costly Feature Spaces. ALT 2017: 250-287 - [c7]Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik, Colin White:
Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems. COLT 2017: 213-274 - [c6]Vaishnavh Nagarajan, J. Zico Kolter:
Gradient descent GAN optimization is locally stable. NIPS 2017: 5585-5595 - [i4]Vaishnavh Nagarajan, J. Zico Kolter:
Gradient descent GAN optimization is locally stable. CoRR abs/1706.04156 (2017) - [i3]Maria-Florina Balcan, Avrim Blum, Vaishnavh Nagarajan:
Lifelong Learning in Costly Feature Spaces. CoRR abs/1706.10271 (2017) - 2016
- [c5]Hemank Lamba, Vaishnavh Nagarajan, Kijung Shin, Naji Shajarisales:
Incorporating Side Information in Tensor Completion. WWW (Companion Volume) 2016: 65-66 - [i2]Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik, Colin White:
Learning the best algorithm for max-cut, clustering, and other partitioning problems. CoRR abs/1611.04535 (2016) - 2015
- [c4]Vaishnavh Nagarajan, Leandro Soriano Marcolino, Milind Tambe:
Every Team Deserves a Second Chance: Identifying When Things Go Wrong (Student Abstract Version). AAAI 2015: 4184-4185 - [c3]Vaishnavh Nagarajan, Leandro Soriano Marcolino, Milind Tambe:
Every Team Makes Mistakes: An Initial Report on Predicting Failure in Teamwork. AAAI Workshop: Learning for General Competency in Video Games 2015 - [c2]Vaishnavh Nagarajan, Leandro Soriano Marcolino, Milind Tambe:
Every Team Deserves a Second Chance: Identifying when Things Go Wrong. AAMAS 2015: 695-703 - [c1]Leandro Soriano Marcolino, Vaishnavh Nagarajan, Milind Tambe:
Every Team Deserves a Second Chance: An Interactive 9x9 Go Experience (Demonstration). AAMAS 2015: 1909-1910 - [i1]Abhinav Garlapati, Aditi Raghunathan, Vaishnavh Nagarajan, Balaraman Ravindran:
A Reinforcement Learning Approach to Online Learning of Decision Trees. CoRR abs/1507.06923 (2015)
Coauthor Index
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