145 citations · 448 across the 24 of their papers we have counts for
6 papers · 1 filter
Non-Vacuous Generalization Bounds for Large Language Models
Sanae Lotfi, Marc Finzi, Yilun Kuang +3
Modern language models can contain billions of parameters, raising the question of whether they can generalize beyond the training data or simply parrot their training corpora. We…
Large Language Models Are Zero-Shot Time Series Forecasters
Nate Gruver, Marc Finzi, Shikai Qiu +1
By encoding time series as a string of numerical digits, we can frame time series forecasting as next-token prediction in text. Developing this approach, we find that large languag…
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
Andres Potapczynski, Marc Finzi, Geoff Pleiss +1
Many areas of machine learning and science involve large linear algebra problems, such as eigendecompositions, solving linear systems, computing matrix exponentials, and trace esti…
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems
Marc Finzi, Anudhyan Boral, Andrew Gordon Wilson +2
Diffusion models are a class of probabilistic generative models that have been widely used as a prior for image processing tasks like text conditional generation and inpainting. We…
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks
Marc Finzi, Andres Potapczynski, Matthew Choptuik +1
Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providin…
The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning
Micah Goldblum, Marc Finzi, Keefer Rowan +1
No free lunch theorems for supervised learning state that no learner can solve all problems or that all learners achieve exactly the same accuracy on average over a uniform distrib…