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Found 6 papers, 2 papers with code
Contrastive Attention Networks for Attribution of Early Modern Print
Specifically, we focus on matching uniquely damaged character type-imprints in anonymously printed books to works with known printers in order to provide evidence of their origins.
Sequential changepoint detection in classification data under label shift
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.
A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing
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?
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.
Exact Post-Selection Inference for Changepoint Detection and Other Generalized Lasso Problems
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
In the spirit of reproducibility, all of our empirical results can also be easily (re)generated using this package.
