Nagarajan, Vaishnavh

23 publications

ICLRW 2025 Multi-Token Prediction Boosts Creativity in Algorithmic Tasks Vaishnavh Nagarajan, Chen Henry Wu, Charles Ding, Aditi Raghunathan

ICML 2025 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

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

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

ICML 2024 The Pitfalls of Next-Token Prediction Gregor Bachmann, Vaishnavh Nagarajan

ICLRW 2024 The Pitfalls of Next-Token Prediction Gregor Bachmann, Vaishnavh Nagarajan

ICLR 2024 Think Before You Speak: Training Language Models with Pause Tokens Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan

TMLR 2024 What Do Larger Image Classifiers Memorise? Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar

NeurIPS 2023 On Student-Teacher Deviations in Distillation: Does It Pay to Disobey? Vaishnavh Nagarajan, Aditya K 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 Rawat, Manzil Zaheer, Aditya K Menon, Sanjiv Kumar

NeurIPSW 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 2022 Assessing Generalization of SGD via Disagreement Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, J Zico Kolter

AISTATS 2021 Provably Safe PAC-MDP Exploration Using Analogies Melrose Roderick, Vaishnavh Nagarajan, Zico Kolter

ICLR 2021 A Learning Theoretic Perspective on Local Explainability Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar

NeurIPSW 2021 Avoiding Spurious Correlations: Bridging Theory and Practice Thao Nguyen, Vaishnavh Nagarajan, Hanie Sedghi, Behnam Neyshabur

ICLR 2021 Understanding the Failure Modes of Out-of-Distribution Generalization Vaishnavh Nagarajan, Anders Andreassen, Behnam Neyshabur

ICLR 2019 Deterministic PAC-Bayesian Generalization Bounds for Deep Networks via Generalizing Noise-Resilience Vaishnavh Nagarajan, Zico Kolter

AISTATS 2019 Revisiting Adversarial Risk Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar

NeurIPS 2019 Uniform Convergence May Be Unable to Explain Generalization in Deep Learning Vaishnavh Nagarajan, J. Zico Kolter

NeurIPS 2017 Gradient Descent GAN Optimization Is Locally Stable Vaishnavh Nagarajan, J. Zico Kolter

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

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

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