Ananya Uppal

I am currently a postdoc at the Institute for Foundations of Machine Learning (IFML) at the University of Texas at Austin.

I received my Ph.D. from Carnegie Mellon University, where I was advised by Barnabás Póczos. I received my bachelors degree from the University of Illinois at Urbana-Champaign.

Email  /  CV  /  Google Scholar

I have worked on statistical machine learning and theoretically understanding machine learning algorithms. My publications are listed below.

• Learning a 1-layer Conditional Generative Model in Total Variation
  Ajil Jalal, Justin Kang, Ananya Uppal, Kannan Ramchandran, Eric Price
  NeurIPS, 2023  
  


• Lovasz Theta Contrastive Learning
  Georgios Smyrnis, Matt Jordan, Ananya Uppal, Giannis Daras, Alex Dimakis
  Workshop: Self-Supervised Learning - Theory and Practice, NeurIPS, 2022  
  


• Robust Density Estimation under Besov IPM Losses
  Ananya Uppal, Shashank Singh, Barnabás Póczos
  NeurIPS, 2020   (Spotlight Presentation)
  


• Nonparametric Density Estimation & Convergence Rates for Gans Under Besov IPM Losses
  Ananya Uppal, Shashank Singh, Barnabás Póczos
  NeurIPS, 2019   (Oral Presentation)
  Honorable Mention for Outstanding Paper award at NeurIPS 2019


• Nonparametric Density Estimation Under Adversarial Losses
  Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabás Póczos
  NeurIPS, 2018