4 papers · 1 filter
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…
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…
Attainability of Two-Point Testing Rates for Finite-Sample Location Estimation
Spencer Compton, Gregory Valiant
Le Cam's two-point testing method yields perhaps the simplest lower bound for estimating the mean of a distribution: roughly, if it is impossible to well-distinguish a distribution…
Near-Optimal Mean Estimation with Unknown, Heteroskedastic Variances
Spencer Compton, Gregory Valiant
Given data drawn from a collection of Gaussian variables with a common mean but different and unknown variances, what is the best algorithm for estimating their common mean? We pre…