4 papers
Density-Ratio Losses for Post-Hoc Learning to Defer
Alexander Soen, Ragnar Thobaben, Joakim Jaldén +1
We study post-hoc Learning to Defer (L2D) through the lens of ideal distributions: divergence-regularized reweightings of the data distribution under which a model attains low loss…
Seasoning Generative Models for a Generalization Aftertaste
Hisham Husain, Valentin De Bortoli, Richard Nock
The use of discriminators to train or fine-tune generative models has proven to be a rather successful framework. A notable example is Generative Adversarial Networks (GANs) that m…
How to Boost Any Loss Function
Richard Nock, Yishay Mansour
Boosting is a highly successful ML-born optimization setting in which one is required to computationally efficiently learn arbitrarily good models based on the access to a weak lea…
Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest Neighbors Label Spreading
Nathan Stromberg, Rohan Ayyagari, Sanmi Koyejo +2
Last-layer retraining methods have emerged as an efficient framework for correcting existing base models. Within this framework, several methods have been proposed to deal with cor…