Improving Consistency-Based Semi-Supervised Learning with Weight Averaging. - Ben Athiwaratkun; Marc Finzi; Pavel Izmailov; Andrew Gordon Wilson (CoRR, 2018)

There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average. - Ben Athiwaratkun; Marc Finzi; Pavel Izmailov; Andrew Gordon Wilson (ICLR (Poster), 2019)

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. - Marc Finzi; Samuel Stanton; Pavel Izmailov; Andrew Gordon Wilson (ICML, 2020)

Semi-Supervised Learning with Normalizing Flows. - Pavel Izmailov; Polina Kirichenko; Marc Finzi; Andrew Gordon Wilson (ICML, 2020)

Learning Invariances in Neural Networks from Training Data. - Gregory W. Benton; Marc Finzi; Pavel Izmailov; Andrew Gordon Wilson (NeurIPS, 2020)

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints. - Marc Finzi; Ke Alexander Wang; Andrew Gordon Wilson (NeurIPS, 2020)

Learning Invariances in Neural Networks. - Gregory W. Benton; Marc Finzi; Pavel Izmailov; Andrew Gordon Wilson (CoRR, 2020)

Probabilistic Numeric Convolutional Neural Networks. - Marc Anton Finzi; Roberto Bondesan; Max Welling (ICLR, 2021)

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups. - Marc Finzi; Max Welling; Andrew Gordon Wilson (ICML, 2021)

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes. - Sanyam Kapoor; Marc Finzi; Ke Alexander Wang; Andrew Gordon Wilson (ICML, 2021)

Residual Pathway Priors for Soft Equivariance Constraints. - Marc Finzi; Greg Benton; Andrew Gordon Wilson (NeurIPS, 2021)

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

PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. - Sanae Lotfi; Marc Finzi; Sanyam Kapoor; Andres Potapczynski; Micah Goldblum; Andrew Gordon Wilson (NeurIPS, 2022)

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 (ICLR, 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-Núñez (ICML, 2023)

Large Language Models Are Zero-Shot Time Series Forecasters. - Nate Gruver; Marc Finzi; Shikai Qiu; Andrew Gordon Wilson (NeurIPS, 2023)

CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra. - Andres Potapczynski; Marc Finzi; Geoff Pleiss; Andrew Gordon Wilson (NeurIPS, 2023)

The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning. - Micah Goldblum; Marc Finzi; Keefer Rowan; Andrew Gordon Wilson (CoRR, 2023)

Understanding and Incorporating Mathematical Inductive Biases in Neural Networks - Marc Finzi (2023)

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 (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)

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

Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models. - Sanae Lotfi; Yilun Kuang; Marc Finzi; Brandon Amos; Micah Goldblum; Andrew Gordon Wilson (NeurIPS, 2024)

Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. - Andres Potapczynski; Shikai Qiu; Marc Finzi; Christopher Ferri; Charlie Chen; Micah Goldblum; C. Bayan Bruss; Christopher De Sa; Andrew Gordon Wilson (NeurIPS, 2024)

Diffusing Differentiable Representations. - Yash Savani; Marc Finzi; J. Zico Kolter (NeurIPS, 2024)

Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. - Andres Potapczynski; Shikai Qiu; Marc Finzi; Christopher Ferri; Zixi Chen; Micah Goldblum; C. Bayan Bruss; Christopher De Sa; Andrew Gordon Wilson (CoRR, 2024)

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 (ICLR, 2025)

Predicting the Performance of Black-box LLMs through Self-Queries. - Dylan Sam; Marc Finzi; J. Zico Kolter (CoRR, 2025)

Antidistillation Sampling. - Yash Savani; Asher Trockman; Zhili Feng; Avi Schwarzschild; Alexander Robey; Marc Finzi; J. Zico Kolter (CoRR, 2025)