Vaishnavh Nagarajan

CS PhD Student
Carnegie Mellon University (CMU)
E-Mail: vaishnavh at cs.cmu.edu

He/Him/His

I’m looking for full-time research positions in the industry starting spring/summer 2021.

Here’s my resume.


About me

I am a PhD student in the Computer Science Department of Carnegie Mellon University (CMU), extremely fortunate to be advised by Zico Kolter. I am interested in the theoretical foundations of machine learning, with a fascination for understanding when and why modern machine learning algorithms work (or do not work). My PhD thesis is on explaining why deep networks generalize well. Besides that, I’ve also studied questions like when and why models fail to generalize out-of-distribution, and when and why GANs converge to the desired saddle point.

I completed my undergraduate studies in the Department of Computer Science and Engineering at the Indian Institute of Technology, Chennai, India. Here I was advised by Balaraman Ravindran with whom I worked in reinforcement learning.


Updates

  • Jan 2021: Two papers accepted at ICLR ‘21, one on out-of-distribution generalization and the other on local explanability.
  • Summer 2020: Interned at Google with Behnam Neyshabur and Anders Andreassen.
  • Dec 2019: Paper with Zico Kolter won the Outstanding New Directions Paper Award at NeurIPS 2019.
  • Summer 2018: Interned at Google Brain with Ian Goodfellow and Colin Raffel.
  • Dec 2017: Paper with Zico Kolter accepted for oral presentation at NeurIPS 2017.

Publications (Google Scholar)


Full Conference Papers

  • Understanding the failure modes of out-of-distribution generalization,
    International Conference on Learning Representations (ICLR) 2021,
    Vaishnavh Nagarajan, Anders Andreassen and Behnam Neyshabur
    [arxiv]

  • A Learning Theoretic Perspective on Local Explainability,
    International Conference on Learning Representations (ICLR) 2021,
    (Double first author) Jeffrey Li*, Vaishnavh Nagarajan*, Gregory Plumb and Ameet Talwalkar
    [arxiv]

  • Provably Safe PAC-MDP exploration using analogies,
    International Conference on Artificial Intelligence and Statistics (AISTATS) 2021
    Melrose Roderick, Vaishnavh Nagarajan and J. Zico Kolter
    [arxiv]

  • Uniform convergence may be unable to explain generalization in deep learning,
    Neural Information Processing Systems (NeurIPS) 2019
    Vaishnavh Nagarajan and J. Zico Kolter
    Winner of The Outstanding New Directions Paper Award
    Accepted for Oral presentation, 0.54% acceptance
    [arxiv] [NeurIPS 19 oral slides] [Poster] [Blogpost] [Code]
    Also accepted for spotlight talk at:

  • Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience,
    International Conference of Learning Representations (ICLR) 2019
    Vaishnavh Nagarajan and J. Zico Kolter
    [Openreview] [Poster]

  • Gradient descent GAN optimization is locally stable,
    Neural Information Processing Systems (NeurIPS) 2017
    Vaishnavh Nagarajan and J. Zico Kolter
    Accepted for Oral presentation, 1.2% acceptance
    [arxiv] [1hr talk - slides] [NeurIPS Oral - Slides] [Poster] [3 min video] [Code]

  • Lifelong Learning in Costly Feature Spaces,
    Algorithmic Learning Theory (ALT) 2017
    with Maria-Florina Balcan and Avrim Blum
    Also an invited journal publication in Theoretical Computer Science (TCS)
    [arxiv][Slides]

  • Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems,
    Conference On Learning Theory (COLT), 2017
    with Maria-Florina Balcan, Ellen Vitercik and Colin White
    [arxiv] [Slides] [Talk]

  • Every team deserves a second chance: Identifying when things go wrong,
    Autonomous Agents and Multiagent Systems (AAMAS) 2015
    (Double 1st author) Vaishnavh Nagarajan*, Leandro S. Marcolino* and Milind Tambe
    [PDF] [Appendix]


Workshop Papers

  • Theoretical Insights into Memorization in GANs,
    Neural Information Processing Systems (NeurIPS) 2017 - Integration of Deep Learning Theories Workshop
    Vaishnavh Nagarajan, Colin Raffel, Ian Goodfellow.
    [PDF]

  • Generalization in Deep Networks: The Role of Distance from Initialization,
    Neural Information Processing Systems (NeurIPS) 2017 - Deep Learning: Bridging Theory and Practice
    Vaishnavh Nagarajan and J. Zico Kolter.
    Accepted for Spotlight talk
    [arxiv] [Poster]

  • A Reinforcement Learning Approach to Online Learning of Decision Trees,
    European Workshop on Reinforcement Learning (EWRL 2015 - ICML)
    (Triple 1st author) Abhinav Garlapati, Aditi Raghunathan, Vaishnavh Nagarajan and Balaraman Ravindran.
    [arxiv]

  • Knows-What-It-Knows Inverse Reinforcement Learning,
    Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM) 2015
    Vaishnavh Nagarajan and Balaraman Ravindran
    [PDF]


Professional Service

Reviewer for

  • ICLR 2021 (outstanding reviewer award)
  • NeurIPS 2020 (top 10%), 2019 (top 50%), 2018 (top 30%)
  • ICML 2021 (Expert reviewier), 2020 (top 33%), 2019 (top 5%)
  • ALT 2021
  • COLT 2019
  • AISTATS 2019

Last Updated: Mar 19th, 2021