Search Results for author: Aaron Fisher

Found 7 papers, 1 papers with code

The Connection Between R-Learning and Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects

no code implementations19 Jul 2023 Aaron Fisher

Many methods for estimating conditional average treatment effects (CATEs) can be expressed as weighted pseudo-outcome regressions (PORs).

regression

Gaussian Latent Dirichlet Allocation for Discrete Human State Discovery

no code implementations28 Jun 2022 Congyu Wu, Aaron Fisher, David Schnyer

In this article we propose and validate an unsupervised probabilistic model, Gaussian Latent Dirichlet Allocation (GLDA), for the problem of discrete state discovery from repeated, multivariate psychophysiological samples collected from multiple, inherently distinct, individuals.

Clustering

Online False Discovery Rate Control for LORD & SAFFRON Under Positive, Local Dependence

no code implementations15 Oct 2021 Aaron Fisher

These three methods have been shown to provide online control of the "modified" false discovery rate (mFDR) under a condition known as conditional superuniformity.

Online Control of the False Discovery Rate under "Decision Deadlines"

no code implementations4 Oct 2021 Aaron Fisher

Online testing procedures aim to control the extent of false discoveries over a sequence of hypothesis tests, allowing for the possibility that early-stage test results influence the choice of hypotheses to be tested in later stages.

Optimizing Rescoring Rules with Interpretable Representations of Long-Term Information

no code implementations28 Apr 2021 Aaron Fisher

Given any initial moving window model, these features can be defined recursively, allowing for straightforward optimization of rescoring rules.

Binary Classification

Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the "Rashomon" Perspective

3 code implementations4 Jan 2018 Aaron Fisher, Cynthia Rudin, Francesca Dominici

Expanding on MR, we propose Model Class Reliance (MCR) as the upper and lower bounds on the degree to which any well-performing prediction model within a class may rely on a variable of interest, or set of variables of interest.

Methodology

Fast, Exact Bootstrap Principal Component Analysis for p>1 million

no code implementations5 May 2014 Aaron Fisher, Brian Caffo, Brian Schwartz, Vadim Zipunnikov

As a result, all bootstrap principal components are limited to the same $n$-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that subspace.

Methodology Applications Computation

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