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…
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…
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…