7 papers
Selecting Informative Conformal Prediction Sets with an Optimized FCR-Controlled Approach
Israela Solomon, Etienne Roquain, Saharon Rosset +1
Conformal methods provide prediction sets for outcomes with confidence guarantees. We study their use in a selective inference setting, where inference is performed only when the p…
Improving Multi-Class Calibration through Normalization-Aware Isotonic Techniques
Alon Arad, Saharon Rosset
Accurate and reliable probability predictions are essential for multi-class supervised learning tasks, where well-calibrated models enable rational decision-making. While isotonic…
The Bottom-Up Approach for Powerful Testing with FWER Control
Rajesh Karmakar, Ruth Heller, Saharon Rosset
We seek to design novel multiple testing procedures, which take into account a relevant notion of ''power'' or true discovery on the one hand, and allow computationally efficient t…
Mixed Semi-Supervised Generalized-Linear-Regression with Applications to Deep-Learning and Interpolators
Oren Yuval, Saharon Rosset
We present a methodology for using unlabeled data to design semi-supervised learning (SSL) methods that improve the predictive performance of supervised learning for regression tas…
MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models
Giora Simchoni, Saharon Rosset
We present MMbeddings, a probabilistic embedding approach that reinterprets categorical embeddings through the lens of nonlinear mixed models, effectively bridging classical statis…
Cross Validation for Correlated Data in Regression and Classification Models, with Applications to Deep Learning
Oren Yuval, Saharon Rosset
We present a methodology for model evaluation and selection where the sampling mechanism violates the i.i.d. assumption. Our methodology involves a formulation of the bias between…