activity
20132021
most citedFairer and more accurate, but for whom?

32 citations · 43 across the 4 of their papers we have counts for

collaborators

6 papers

stat.AP2021

Inference with generalizable classifier predictions

Ciaran Evans, Zara Y. Weinberg, Manojkumar A. Puthenveedu +1

This paper addresses the problem of making statistical inference about a population that can only be identified through classifier predictions. The problem is motivated by scientif…

cs.LG20201 cited

A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing

Kartik Goyal, Chris Dyer, Christopher Warren +2

We propose a deep and interpretable probabilistic generative model to analyze glyph shapes in printed Early Modern documents. We focus on clustering extracted glyph images into und…

stat.ME2020

Fairness Evaluation in Presence of Biased Noisy Labels

Riccardo Fogliato, Max G'Sell, Alexandra Chouldechova

Risk assessment tools are widely used around the country to inform decision making within the criminal justice system. Recently, considerable attention has been devoted to the ques…

stat.ME2018

Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Data

Sangwon Hyun, Kevin Lin, Max G'Sell +1

Changepoint detection methods are used in many areas of science and engineering, e.g., in the analysis of copy number variation data, to detect abnormalities in copy numbers along…

stat.AP201732 cited

Fairer and more accurate, but for whom?

Alexandra Chouldechova, Max G'Sell

Complex statistical machine learning models are increasingly being used or considered for use in high-stakes decision-making pipelines in domains such as financial services, health…

stat.ME201310 cited

False Variable Selection Rates in Regression

Max Grazier G'Sell, Trevor Hastie, Robert Tibshirani

There has been recent interest in extending the ideas of False Discovery Rates (FDR) to variable selection in regression settings. Traditionally the FDR in these settings has been…