38 citations · 158 across the 13 of their papers we have counts for
14 papers
Contrastive Attention Networks for Attribution of Early Modern Print
Nikolai Vogler, Kartik Goyal, Kishore PV Reddy +5
In this paper, we develop machine learning techniques to identify unknown printers in early modern (c.~1500--1800) English printed books. Specifically, we focus on matching uniquel…
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…
Sequential changepoint detection in classification data under label shift
Ciaran Evans, Max G'Sell
Classifier predictions often rely on the assumption that new observations come from the same distribution as training data. When the underlying distribution changes, so does the op…
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…
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…
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…
Sharp instruments for classifying compliers and generalizing causal effects
Edward H. Kennedy, Sivaraman Balakrishnan, Max G'Sell
It is well-known that, without restricting treatment effect heterogeneity, instrumental variable (IV) methods only identify "local" effects among compliers, i.e., those subjects wh…
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…
Bootstrapping and Sample Splitting For High-Dimensional, Assumption-Free Inference
Alessandro Rinaldo, Larry Wasserman, Max G'Sell +1
Several new methods have been proposed for performing valid inference after model selection. An older method is sampling splitting: use part of the data for model selection and par…
Distribution-Free Predictive Inference For Regression
Jing Lei, Max G'Sell, Alessandro Rinaldo +2
We develop a general framework for distribution-free predictive inference in regression, using conformal inference. The proposed methodology allows for the construction of a predic…
Sequential Selection Procedures and False Discovery Rate Control
Max Grazier G'Sell, Stefan Wager, Alexandra Chouldechova +1
We consider a multiple hypothesis testing setting where the hypotheses are ordered and one is only permitted to reject an initial contiguous block, H_1,\dots,H_k, of hypotheses. A…
Sensitivity Analysis for Inference with Partially Identifiable Covariance Matrices
Max Grazier G'Sell, Shai S. Shen-Orr, Robert Tibshirani
In some multivariate problems with missing data, pairs of variables exist that are never observed together. For example, some modern biological tools can produce data of this form.…
Adaptive testing for the graphical lasso
Max Grazier G'Sell, Jonathan Taylor, Robert Tibshirani
We consider tests of significance in the setting of the graphical lasso for inverse covariance matrix estimation. We propose a simple test statistic based on a subsequence of the k…
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…