26 citations · 30 across the 9 of their papers we have counts for
6 papers
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…
Reduced-Order Autodifferentiable Ensemble Kalman Filters
Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett
This paper introduces a computational framework to reconstruct and forecast a partially observed state that evolves according to an unknown or expensive-to-simulate dynamical syste…
Online Data Thinning via Multi-Subspace Tracking
Xin Jiang, Rebecca Willett
In an era of ubiquitous large-scale streaming data, the availability of data far exceeds the capacity of expert human analysts. In many settings, such data is either discarded or s…
Nonuniform Expansion of the Youngest Galactic Supernova Remnant G1.9+0.3
K. J. Borkowski, S. P. Reynolds, D. A. Green +4
We report measurements of X-ray expansion of the youngest Galactic supernova remnant, G1.9+0.3, using Chandra observations in 2007, 2009, and 2011. The measured rates strongly devi…
Minimax Optimal Rates for Poisson Inverse Problems with Physical Constraints
Xin Jiang, Garvesh Raskutti, Rebecca Willett
This paper considers fundamental limits for solving sparse inverse problems in the presence of Poisson noise with physical constraints. Such problems arise in a variety of applicat…
Fishing in Poisson streams: focusing on the whales, ignoring the minnows
Maxim Raginsky, Sina Jafarpour, Rebecca Willett +1
This paper describes a low-complexity approach for reconstructing average packet arrival rates and instantaneous packet counts at a router in a communication network, where the arr…