528 citations · 4.4k across the 100 of their papers we have counts for
100 papers
Function-Space Regularization in Neural Networks: A Probabilistic Perspective
Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu +1
Parameter-space regularization in neural network optimization is a fundamental tool for improving generalization. However, standard parameter-space regularization methods make it c…
Simple and Fast Group Robustness by Automatic Feature Reweighting
Shikai Qiu, Andres Potapczynski, Pavel Izmailov +1
A major challenge to out-of-distribution generalization is reliance on spurious features -- patterns that are predictive of the class label in the training data distribution, but n…
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 Study of Bayesian Neural Network Surrogates for Bayesian Optimization
Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query. These objectives are typically represented by Gaussian process…
Protein Design with Guided Discrete Diffusion
Nate Gruver, Samuel Stanton, Nathan C. Frey +6
A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. The generative model samples plausible sequences while t…
Automated Few-shot Classification with Instruction-Finetuned Language Models
Rami Aly, Xingjian Shi, Kaixiang Lin +2
A particularly successful class of approaches for few-shot learning combines language models with prompts -- hand-crafted task descriptions that complement data samples. However, d…