Finzi, Marc Anton

13 publications

NeurIPS 2025 Antidistillation Sampling Yash Savani, Asher Trockman, Zhili Feng, Yixuan Even Xu, Avi Schwarzschild, Alexander Robey, Marc Anton Finzi, J Zico Kolter

ICLR 2025 Compute-Optimal LLMs Provably Generalize Better with Scale Marc Anton Finzi, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J Zico Kolter, Andrew Gordon Wilson

NeurIPS 2025 Predicting the Performance of Black-Box Language Models with Follow-up Queries Dylan Sam, Marc Anton Finzi, J Zico Kolter

ICML 2024 Compute Better Spent: Replacing Dense Layers with Structured Matrices Shikai Qiu, Andres Potapczynski, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson

ICMLW 2024 Eliciting Black-Box Representations from LLMs Through Self-Queries Dylan Sam, Marc Anton Finzi

ICML 2024 Non-Vacuous Generalization Bounds for Large Language Models Sanae Lotfi, Marc Anton Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson

ICML 2024 Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning Micah Goldblum, Marc Anton Finzi, Keefer Rowan, Andrew Gordon Wilson

ICMLW 2024 Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models Sanae Lotfi, Yilun Kuang, Marc Anton Finzi, Brandon Amos, Micah Goldblum, Andrew Gordon Wilson

ICLR 2023 A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks Marc Anton Finzi, Andres Potapczynski, Matthew Choptuik, Andrew Gordon Wilson

ICLR 2023 The Lie Derivative for Measuring Learned Equivariance Nate Gruver, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson

ICML 2023 User-Defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Nunez

ICLR 2022 Deconstructing the Inductive Biases of Hamiltonian Neural Networks Nate Gruver, Marc Anton Finzi, Samuel Don Stanton, Andrew Gordon Wilson

ICLR 2021 Probabilistic Numeric Convolutional Neural Networks Marc Anton Finzi, Roberto Bondesan, Max Welling