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ORCIDVaishnavh Nagarajan
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2020 – today
- 2024
[j3]Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
What do larger image classifiers memorise? Trans. Mach. Learn. Res. 2024 (2024)- [c22]Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan:
Think before you speak: Training Language Models With Pause Tokens. ICLR 2024 - [c21]Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite:
The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning. ICLR 2024 - [c20]Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan:
Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning. ICLR 2024 - [c19]Gregor Bachmann, Vaishnavh Nagarajan:
The Pitfalls of Next-Token Prediction. ICML 2024 - [i20]Gregor Bachmann, Vaishnavh Nagarajan:
The pitfalls of next-token prediction. CoRR abs/2403.06963 (2024) - [i19]Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan:
Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning. CoRR abs/2405.20439 (2024) - 2023
- [c18]Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar:
On student-teacher deviations in distillation: does it pay to disobey? NeurIPS 2023 - [c17]Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan, Zonglin Li, Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Sanjiv Kumar:
ResMem: Learn what you can and memorize the rest. NeurIPS 2023 - [i18]Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar:
On student-teacher deviations in distillation: does it pay to disobey? CoRR abs/2301.12923 (2023) - [i17]Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan, Zonglin Li, Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Sanjiv Kumar:
ResMem: Learn what you can and memorize the rest. CoRR abs/2302.01576 (2023) - [i16]Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan:
Think before you speak: Training Language Models With Pause Tokens. CoRR abs/2310.02226 (2023) - [i15]Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite:
The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning. CoRR abs/2310.04680 (2023) - [i14]Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
What do larger image classifiers memorise? CoRR abs/2310.05337 (2023) - 2022
- [c16]Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, J. Zico Kolter:
Assessing Generalization of SGD via Disagreement. ICLR 2022 - 2021
- [c15]Melrose Roderick, Vaishnavh Nagarajan, J. Zico Kolter:
Provably Safe PAC-MDP Exploration Using Analogies. AISTATS 2021: 1216-1224 - [c14]Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar:
A Learning Theoretic Perspective on Local Explainability. ICLR 2021 - [c13]Vaishnavh Nagarajan, Anders Andreassen, Behnam Neyshabur:
Understanding the failure modes of out-of-distribution generalization. ICLR 2021 - [i13]Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, J. Zico Kolter:
Assessing Generalization of SGD via Disagreement. CoRR abs/2106.13799 (2021) - [i12]Vaishnavh Nagarajan:
Explaining generalization in deep learning: progress and fundamental limits. CoRR abs/2110.08922 (2021) - 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 ATC 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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