Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Diaz, Mateo"'
Classical results in asymptotic statistics show that the Fisher information matrix controls the difficulty of estimating a statistical model from observed data. In this work, we introduce a companion measure of robustness of an estimation problem: th
Externí odkaz:
http://arxiv.org/abs/2405.09676
Autor:
Díaz, Mateo, Chandrasekaran, Venkat
Controlling the false discovery rate (FDR) is a popular approach to multiple testing, variable selection, and related problems of simultaneous inference. In many contemporary applications, models are not specified by discrete variables, which necessi
Externí odkaz:
http://arxiv.org/abs/2404.09142
Autor:
Levin, Eitan, Díaz, Mateo
Publikováno v:
International Conference on Artificial Intelligence and Statistics. PMLR, 2024. Available from https://proceedings.mlr.press/v238/levin24a.html
Traditional supervised learning aims to learn an unknown mapping by fitting a function to a set of input-output pairs with a fixed dimension. The fitted function is then defined on inputs of the same dimension. However, in many settings, the unknown
Externí odkaz:
http://arxiv.org/abs/2306.06327
This paper investigates two randomized preconditioning techniques for solving kernel ridge regression (KRR) problems with a medium to large number of data points ($10^4 \leq N \leq 10^7$), and it introduces two new methods with state-of-the-art perfo
Externí odkaz:
http://arxiv.org/abs/2304.12465
Publikováno v:
Journal of Machine Learning Research, 25(90):1-49, 2024
We analyze a stochastic approximation algorithm for decision-dependent problems, wherein the data distribution used by the algorithm evolves along the iterate sequence. The primary examples of such problems appear in performative prediction and its m
Externí odkaz:
http://arxiv.org/abs/2207.04173
Autor:
Luo, Qi, Weightman, Ryan, McQuade, Sean T., Diaz, Mateo, Trélat, Emmanuel, Barbour, William, Work, Dan, Samaranayake, Samitha, Piccoli, Benedetto
During the Covid-19 pandemic a key role is played by vaccination to combat the virus. There are many possible policies for prioritizing vaccines, and different criteria for optimization: minimize death, time to herd immunity, functioning of the healt
Externí odkaz:
http://arxiv.org/abs/2203.09502
We investigate a clustering problem with data from a mixture of Gaussians that share a common but unknown, and potentially ill-conditioned, covariance matrix. We start by considering Gaussian mixtures with two equally-sized components and derive a Ma
Externí odkaz:
http://arxiv.org/abs/2110.01602
Recent work has shown that stochastically perturbed gradient methods can efficiently escape strict saddle points of smooth functions. We extend this body of work to nonsmooth optimization, by analyzing an inexact analogue of a stochastically perturbe
Externí odkaz:
http://arxiv.org/abs/2106.09815
Autor:
Applegate, David, Díaz, Mateo, Hinder, Oliver, Lu, Haihao, Lubin, Miles, O'Donoghue, Brendan, Schudy, Warren
We present PDLP, a practical first-order method for linear programming (LP) that can solve to the high levels of accuracy that are expected in traditional LP applications. In addition, it can scale to very large problems because its core operation is
Externí odkaz:
http://arxiv.org/abs/2106.04756
Autor:
Díaz, Mateo, Grimmer, Benjamin
We study convergence rates of the classic proximal bundle method for a variety of nonsmooth convex optimization problems. We show that, without any modification, this algorithm adapts to converge faster in the presence of smoothness or a H\"older gro
Externí odkaz:
http://arxiv.org/abs/2105.07874