102 citations · 141 across the 12 of their papers we have counts for
12 papers
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion
Hossein Souri, Arpit Bansal, Hamid Kazemi +7
Modern neural networks are often trained on massive datasets that are web scraped with minimal human inspection. As a result of this insecure curation pipeline, an adversary can po…
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors
Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson +1
Machine learning models often perform poorly under subpopulation shifts in the data distribution. Developing methods that allow machine learning models to better generalize to such…
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…
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning
Valeriia Cherepanova, Roman Levin, Gowthami Somepalli +5
Academic tabular benchmarks often contain small sets of curated features. In contrast, data scientists typically collect as many features as possible into their datasets, and even…
Fortuna: A Library for Uncertainty Quantification in Deep Learning
Gianluca Detommaso, Alberto Gasparin, Michele Donini +3
We present Fortuna, an open-source library for uncertainty quantification in deep learning. Fortuna supports a range of calibration techniques, such as conformal prediction that ca…
Low-Precision Arithmetic for Fast Gaussian Processes
Wesley J. Maddox, Andres Potapczynski, Andrew Gordon Wilson
Low-precision arithmetic has had a transformative effect on the training of neural networks, reducing computation, memory and energy requirements. However, despite its promise, low…