289 citations · 1.2k across the 72 of their papers we have counts for
9 papers · 1 filter
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds
Owen Melia, Eric Jonas, Rebecca Willett
Rotational invariance is a popular inductive bias used by many fields in machine learning, such as computer vision and machine learning for quantum chemistry. Rotation-invariant ma…
Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guarantees
Yue Gao, Garvesh Raskutti, Rebecca Willet
This paper introduces a novel, computationally-efficient algorithm for predictive inference (PI) that requires no distributional assumptions on the data and can be computed faster…
Integrating Uncertainty Awareness into Conformalized Quantile Regression
Raphael Rossellini, Rina Foygel Barber, Rebecca Willett
Conformalized Quantile Regression (CQR) is a recently proposed method for constructing prediction intervals for a response given covariates , without making distributional a…
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…
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…
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 diffusio…