Search Results for author: Max G'Sell

Found 5 papers, 2 papers with code

Sequential changepoint detection in classification data under label shift

no code implementations18 Sep 2020 Ciaran Evans, Max G'Sell

We reduce this problem to the problem of detecting a change in the one-dimensional classifier scores, leading to simple nonparametric sequential changepoint detection procedures.

Classification General Classification

A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing

no code implementations ACL 2020 Kartik Goyal, Chris Dyer, Christopher Warren, Max G'Sell, Taylor Berg-Kirkpatrick

We show that our approach outperforms rigid interpretable clustering baselines (Ocular) and overly-flexible deep generative models (VAE) alike on the task of completely unsupervised discovery of typefaces in mixed-font documents.

Fairer and more accurate, but for whom?

no code implementations30 Jun 2017 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 care, criminal justice and human services.

Decision Making Fairness

Exact Post-Selection Inference for Changepoint Detection and Other Generalized Lasso Problems

1 code implementation11 Jun 2016 Sangwon Hyun, Max G'Sell, Ryan J. Tibshirani

Leveraging a sequential characterization of this path from Tibshirani & Taylor (2011), and recent advances in post-selection inference from Lee et al. (2016), Tibshirani et al. (2016), we develop exact hypothesis tests and confidence intervals for linear contrasts of the underlying mean vector, conditioned on any model selection event along the generalized lasso path (assuming Gaussian errors in the observations).

Methodology

Distribution-Free Predictive Inference For Regression

5 code implementations14 Apr 2016 Jing Lei, Max G'Sell, Alessandro Rinaldo, Ryan J. Tibshirani, Larry Wasserman

In the spirit of reproducibility, all of our empirical results can also be easily (re)generated using this package.

Prediction Intervals

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