Publications (51)
Empirical Power Analysis of a Statistical Test to Quantify Gerrymandering
Ranthony A. Clark, Susan Glenn, Harlin Lee +1
Gerrymandering is a pervasive problem within the US political system. In the past decade, methods based on Markov Chain Monte Carlo (MCMC) sampling and statistical outlier tests ha…
Utility Ghost: Gamified redistricting with partisan symmetry
Dustin G. Mixon, Soledad Villar
Inspired by the word game Ghost, we propose a new protocol for bipartisan redistricting in which partisan players take turns assigning precincts to districts. We prove that in an i…
pscaling small models: Principled warm starts and hyperparameter transfer
Yuxin Ma, Nan Chen, Mateo DÃaz +3
Modern large-scale neural networks are often trained and released in multiple sizes to accommodate diverse inference budgets. To improve efficiency, recent work has explored model…
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning
Sharut Gupta, Joshua Robinson, Derek Lim +2
Self-supervised learning converts raw perceptual data such as images to a compact space where simple Euclidean distances measure meaningful variations in data. In this paper, we ex…
Deep Learning is Provably Robust to Symmetric Label Noise
Carey E. Priebe, Ningyuan Huang, Soledad Villar +2
Deep neural networks (DNNs) are capable of perfectly fitting the training data, including memorizing noisy data. It is commonly believed that memorization hurts generalization. The…
Fitting very flexible models: Linear regression with large numbers of parameters
David W. Hogg, Soledad Villar
There are many uses for linear fitting; the context here is interpolation and denoising of data, as when you have calibration data and you want to fit a smooth, flexible function t…