4 papers
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…
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…
Is Algorithmic Stability Testable? A Unified Framework under Computational Constraints
Yuetian Luo, Rina Foygel Barber
Algorithmic stability is a central notion in learning theory that quantifies the sensitivity of an algorithm to small changes in the training data. If a learning algorithm satisfie…
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…