From the 1 of 19 linked papers with an AI index.
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Theoretical Foundations of Conformal Prediction
Anastasios N. Angelopoulos, Rina Foygel Barber, Stephen Bates
This book is about conformal prediction and related inferential techniques that build on permutation tests and exchangeability. These techniques are useful in a diverse array of ta…
False positive control in time series coincidence detection
Ruiting Liang, Samuel Dyson, Rina Foygel Barber +1
We study the problem of coincidence detection in time series data, where we aim to determine whether the appearance of simultaneous or near-simultaneous events in two time series i…
Stability and Accuracy Trade-offs in Statistical Estimation
Abhinav Chakraborty, Yuetian Luo, Rina Foygel Barber
Algorithmic stability is a central concept in statistics and learning theory that measures how sensitive an algorithm's output is to small changes in the training data. Stability p…
The Limits of Assumption-free Tests for Algorithm Performance
Yuetian Luo, Rina Foygel Barber
Algorithm evaluation and comparison are fundamental questions in machine learning and statistics -- how well does an algorithm perform at a given modeling task, and which algorithm…
False discovery rate control with compound p-values
Rina Foygel Barber, Richard J Samworth
In the setting of multiple testing, compound p-values generalize p-values by asking for superuniformity to hold only \emph{on average} across all true nulls. We study the propertie…
Are all models wrong? Fundamental limits in distribution-free empirical model falsification
Manuel M. Müller, Yuetian Luo, Rina Foygel Barber
In statistics and machine learning, when we train a fitted model on available data, we typically want to ensure that we are searching within a model class that contains at least on…