ICML 2023

List of papers by ASSET researchers:


  1. A Picture of the Space of Typical Learnable Tasks; Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang, Han Kheng Teoh, Mark Transtrum, James Sethna, Pratik Chaudhari
  2. Characterizing Multicalibration via Property Elicitation; Georgy Noarov, Aaron Roth
  3. Demystifying Disagreement-on-the-Line in High Dimensions; Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani
  4. Do Machine Learning Models Learn Statistical Rules Inferred from Data? Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong
  5. Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods; Aleksandr Shevchenko, Kevin Kögler, Hamed Hassani, Marco Mondelli
  6. Individually Fair Learning with One-Sided Feedback; Yahav Bechavod, Aaron Roth
  7. Learning Globally Smooth Functions on Manifolds, Juan Cervino, Luiz Chamon, Benjamin Haeffele, Rene Vidal, Alejandro Ribeiro
  8. LIV: Language-Image Representations and Rewards for Robotic Control; Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman
  9. Multicalibration as Boosting for Regression; Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell
  10. On Regularization and Inference with Label Constraints; Kaifu Wang, Hangfeng He, Tin Nguyen, Piyush Kumar, Dan Roth
  11. On the Convergence of Gradient Flow on Multi-layer Linear Models; Hancheng Min, Rene Vidal, Enrique Mallada
  12. PAC Prediction Sets for Large Language Models of Code; Adam Khakhar, Stephen Mell, Osbert Bastani
  13. Robust subtask learning for compositional generalization; Kishor Jothimurugan, Steve Hsu, Osbert Bastani, Rajeev Alur
  14. The Ideal Continual Learner: An Agent That Never Forgets; Liangzu Peng, Paris Giampouras, Rene Vidal
  15. The Implicit Regularization of Dynamical Stability in Stochastic Gradient Descent; Lei Wu, Weijie Su
  16. The Power of Learned Locally Linear Models for Nonlinear Policy Optimization; Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu
  17. The Value of Out-of-Distribution Data; Ashwin De Silva, Rahul Ramesh, Carey Priebe, Pratik Chaudhari, Joshua Vogelstein
  18. Variational Autoencoding Neural Operators; Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris