28 citations · 52 across the 11 of their papers we have counts for
6 papers · 1 filter
Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data
Koby Bibas
Machine learning models have exhibited exceptional results in various domains. The most prevalent approach for learning is the empirical risk minimizer (ERM), which adapts the mode…
Beyond Ridge Regression for Distribution-Free Data
Koby Bibas, Meir Feder
In supervised batch learning, the predictive normalized maximum likelihood (pNML) has been proposed as the min-max regret solution for the distribution-free setting, where no distr…
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
Koby Bibas, Meir Feder, Tal Hassner
Detecting out-of-distribution (OOD) samples is vital for developing machine learning based models for critical safety systems. Common approaches for OOD detection assume access to…
Distribution Free Uncertainty for the Minimum Norm Solution of Over-parameterized Linear Regression
Koby Bibas, Meir Feder
A fundamental principle of learning theory is that there is a trade-off between the complexity of a prediction rule and its ability to generalize. Modern machine learning models do…
A New Look at an Old Problem: A Universal Learning Approach to Linear Regression
Koby Bibas, Yaniv Fogel, Meir Feder
Linear regression is a classical paradigm in statistics. A new look at it is provided via the lens of universal learning. In applying universal learning to linear regression the hy…
Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks
Koby Bibas, Yaniv Fogel, Meir Feder
The Predictive Normalized Maximum Likelihood (pNML) scheme has been recently suggested for universal learning in the individual setting, where both the training and test samples ar…