Every Team Deserves a Second Chance: Identifying When Things Go Wrong (Student Abstract Version). - Vaishnavh Nagarajan; Leandro Soriano Marcolino; Milind Tambe (AAAI, 2015)

Every Team Makes Mistakes: An Initial Report on Predicting Failure in Teamwork. - Vaishnavh Nagarajan; Leandro Soriano Marcolino; Milind Tambe (AAAI Workshop: Learning for General Competency in Video Games, 2015)

Every Team Deserves a Second Chance: An Interactive 9x9 Go Experience (Demonstration). - Leandro Soriano Marcolino; Vaishnavh Nagarajan; Milind Tambe (AAMAS, 2015)

Every Team Deserves a Second Chance: Identifying when Things Go Wrong. - Vaishnavh Nagarajan; Leandro Soriano Marcolino; Milind Tambe (AAMAS, 2015)

A Reinforcement Learning Approach to Online Learning of Decision Trees. - Abhinav Garlapati; Aditi Raghunathan; Vaishnavh Nagarajan; Balaraman Ravindran (CoRR, 2015)

Incorporating Side Information in Tensor Completion. - Hemank Lamba; Vaishnavh Nagarajan; Kijung Shin; Naji Shajarisales (WWW (Companion Volume), 2016)

Learning the best algorithm for max-cut, clustering, and other partitioning problems. - Maria-Florina Balcan; Vaishnavh Nagarajan; Ellen Vitercik; Colin White (CoRR, 2016)

Lifelong Learning in Costly Feature Spaces. - Maria-Florina Balcan; Avrim Blum; Vaishnavh Nagarajan (ALT, 2017)

Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems. - Maria-Florina Balcan; Vaishnavh Nagarajan; Ellen Vitercik; Colin White (COLT, 2017)

Gradient descent GAN optimization is locally stable. - Vaishnavh Nagarajan; J. Zico Kolter (NIPS, 2017)

Every team deserves a second chance: an extended study on predicting team performance. - Leandro Soriano Marcolino; Aravind S. Lakshminarayanan; Vaishnavh Nagarajan; Milind Tambe (Auton. Agents Multi Agent Syst., 2017)

Geriatrix: Aging what you see and what you don't see. A file system aging approach for modern storage systems. - Saurabh Kadekodi; Vaishnavh Nagarajan; Gregory R. Ganger (USENIX ATC, 2018)

On Adversarial Risk and Training. - Arun Sai Suggala; Adarsh Prasad; Vaishnavh Nagarajan; Pradeep Ravikumar (CoRR, 2018)

Revisiting Adversarial Risk. - Arun Sai Suggala; Adarsh Prasad; Vaishnavh Nagarajan; Pradeep Ravikumar (AISTATS, 2019)

Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience. - Vaishnavh Nagarajan; J. Zico Kolter (ICLR (Poster), 2019)

Uniform convergence may be unable to explain generalization in deep learning. - Vaishnavh Nagarajan; J. Zico Kolter (NeurIPS, 2019)

Generalization in Deep Networks: The Role of Distance from Initialization. - Vaishnavh Nagarajan; J. Zico Kolter (CoRR, 2019)

Provably Safe PAC-MDP Exploration Using Analogies. - Melrose Roderick; Vaishnavh Nagarajan; J. Zico Kolter (AISTATS, 2021)

A Learning Theoretic Perspective on Local Explainability. - Jeffrey Li; Vaishnavh Nagarajan; Gregory Plumb; Ameet Talwalkar (ICLR, 2021)

Understanding the failure modes of out-of-distribution generalization. - Vaishnavh Nagarajan; Anders Andreassen; Behnam Neyshabur (ICLR, 2021)

Explaining generalization in deep learning: progress and fundamental limits. - Vaishnavh Nagarajan (CoRR, 2021)

Assessing Generalization of SGD via Disagreement. - Yiding Jiang; Vaishnavh Nagarajan; Christina Baek; J. Zico Kolter (ICLR, 2022)

On student-teacher deviations in distillation: does it pay to disobey? - Vaishnavh Nagarajan; Aditya Krishna Menon; Srinadh Bhojanapalli; Hossein Mobahi; Sanjiv Kumar (NeurIPS, 2023)

ResMem: Learn what you can and memorize the rest. - Zitong Yang; Michal Lukasik; Vaishnavh Nagarajan; Zonglin Li; Ankit Singh Rawat; Manzil Zaheer; Aditya Krishna Menon; Sanjiv Kumar (NeurIPS, 2023)

The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning. - Tian Jin; Nolan Clement; Xin Dong; Vaishnavh Nagarajan; Michael Carbin; Jonathan Ragan-Kelley; Gintare Karolina Dziugaite (CoRR, 2023)

Think before you speak: Training Language Models With Pause Tokens. - Sachin Goyal; Ziwei Ji; Ankit Singh Rawat; Aditya Krishna Menon; Sanjiv Kumar; Vaishnavh Nagarajan (ICLR, 2024)

The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning. - Tian Jin; Nolan Clement; Xin Dong; Vaishnavh Nagarajan; Michael Carbin; Jonathan Ragan-Kelley; Gintare Karolina Dziugaite (ICLR, 2024)

Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning. - Jacob Mitchell Springer; Vaishnavh Nagarajan; Aditi Raghunathan (ICLR, 2024)

The Pitfalls of Next-Token Prediction. - Gregor Bachmann; Vaishnavh Nagarajan (ICML, 2024)

What do larger image classifiers memorise? - Michal Lukasik; Vaishnavh Nagarajan; Ankit Singh Rawat; Aditya Krishna Menon; Sanjiv Kumar (Trans. Mach. Learn. Res., 2024)

Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction. - Vaishnavh Nagarajan; Chen Henry Wu; Charles Ding; Aditi Raghunathan (CoRR, 2025)

Catch Your Breath: Adaptive Computation for Self-Paced Sequence Production. - Alexandre Galashov; Matt Jones 001; Nan Rosemary Ke; Yuan Cao; Vaishnavh Nagarajan; Michael C. Mozer (CoRR, 2025)

Deep sequence models tend to memorize geometrically; it is unclear why. - Shahriar Noroozizadeh; Vaishnavh Nagarajan; Elan Rosenfeld; Sanjiv Kumar (CoRR, 2025)