6 papers
Joint inference of weak lensing convergence map and cosmology with diffusion models
Benjamin Remy, Chihway Chang, Rebecca Willett
We present a method for joint inference of cosmological parameters and convergence maps from weak lensing observations, targeting the full posterior conditioned on the observed she…
Distribution-free inference with hierarchical data
Yonghoon Lee, Rina Foygel Barber, Rebecca Willett
This paper studies distribution-free inference in settings where the data set has a hierarchical structure -- for example, groups of observations, or repeated measurements. In such…
ReLU Neural Networks with Linear Layers are Biased Towards Single- and Multi-Index Models
Suzanna Parkinson, Greg Ongie, Rebecca Willett
Neural networks often operate in the overparameterized regime, in which there are far more parameters than training samples, allowing the training data to be fit perfectly. That is…
Deep Stochastic Mechanics
Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang +2
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schrödinger equation inspired by stochastic mechanics and generative diffusi…
Bagging Provides Assumption-free Stability
Jake A. Soloff, Rina Foygel Barber, Rebecca Willett
Bagging is an important technique for stabilizing machine learning models. In this paper, we derive a finite-sample guarantee on the stability of bagging for any model. Our result…
Training neural operators to preserve invariant measures of chaotic attractors
Ruoxi Jiang, Peter Y. Lu, Elena Orlova +1
Chaotic systems make long-horizon forecasts difficult because small perturbations in initial conditions cause trajectories to diverge at an exponential rate. In this setting, neura…