The International Conference on Machine Learning

ICML 2024 Accepted Papers - ASSET Center Contributors

  1. A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks
    • Behrad Moniri, Donghwan Lee, Hamed Hassani, Edgar Dobriban
  2. Can Implicit Bias Imply Adversarial Robustness?
    • Hancheng Min, René Vidal 
  3. Complexity Matters: Feature Learning in the Presence of Spurious Correlations 
    • GuanWen Qiu, Da Kuang, Surbhi Goel

  4. Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks 
    • Rahul Ramesh, Ekdeep Singh Lubana, Mikail Khona, Robert P. Dick, Hidenori Tanaka
  5. Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth
    • Kevin Kögler, Alexander Shevchenko, Hamed Hassani, Marco Mondelli
  6. Conformal Prediction with Learned Features
    • Shayan Kiyani, George J. Pappas, Hamed Hassani
  7. Demystifying Doubly Stochastic Gradient Descent
    • Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner
  8. DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation
    • Yinjun Wu, Mayank Keoliya, Kan Chen, Neelay Velingker, Ziyang Li, Emily J Getzen, Qi Long, Mayur Naik, Ravi B Parikh, Eric Wong
  9. Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks
    • Lujing Zhang, Aaron Roth, Linjun Zhang
  10. Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
    • Thomas T-CK Zhang, Bruce D Lee, Ingvar Ziemann, George J. Pappas, Nikolai Matni
  11. Membership Inference Attacks on Diffusion Models via Quantile Regression
    • Shuai Tang, Zhiwei Steven Wu, Sergul Aydore, Michael Kearns, Aaron Roth
  12. Monotone Individual Fairness
    • Yahav Bechavod
  13. Multicalibration for Confidence Scoring in LLMs
    • Gianluca Detommaso, Martin Bertran, Riccardo Fogliato, Aaron Roth
  14. Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-Perfect Representation Learning
    • Chendi Wang, Yuqing Zhu, Weijie Su, Yu-Xiang Wang
  15. Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks
    • Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi, Aryan Mokhtari, Sanjay Shakkottai
  16. Provably Scalable Black-Box Variational Inference with Structured Variational Families
    • Joohwan Ko, Kyurae Kim, Woo Chang Kim, Jacob R. Gardner
  17. Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss
    • Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni
  18. Shifted Interpolation for Differential Privacy
    • Jinho Bok, Weijie Su, Jason Altschuler
  19. Stochastic Bandits with ReLU Neural Networks
    • Kan Xu, Hamsa Bastani, Surbhi Goel, Osbert Bastani

  20. T-Cal: An Optimal Test for the Calibration of Predictive Models
    • Donghwan Lee, Xinmeng Huang, Hamed Hassani, Edgar Dobriban
  21. Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model
    • Mikail Khona, Maya Okawa, Jan Hula, Rahul Ramesh, Kento Nishi, Robert Dick, Ekdeep Singh Lubana, Hidenori Tanaka
  22. Towards Compositionality in Concept Learning
    • Adam Stein, Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong
  23. Understanding Stochastic Natural Gradient Variational Inference
    • Kaiwen Wu, Jacob R. Gardner