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math.ST2026
Algorithms for adaptive and heteroskedastic linear regression at the computational threshold
Spencer Compton, Tselil Schramm
We study finite-sample linear regression in the presence of varied and unknown label noise, focusing on the heteroskedastic and adaptive linear regression models. Heteroskedastic l…
cs.DS2026
Density estimation for Hellinger via minimum-distance estimators: mixtures of Gaussians, log-concave, and more
Spencer Compton, Jerry Li
We study the task of density estimation, where we hope to accurately estimate a probability density from samples. A textbook method for density estimation in total variation di…
math.ST2026
Ratio Covers of Convex Sets and Optimal Mixture Density Estimation
Spencer Compton, Gábor Lugosi, Jaouad Mourtada +2
We study density estimation in Kullback-Leibler divergence: given an i.i.d. sample from an unknown density , the goal is to construct an estimator such that…