6 papers
Mentored Decoding: Faster Inference meets Boosting
Vivien Tran-Thien, Richard Nock
Speculative decoding is a successful technique speeding up inference of a target autoregressive language model via a fast drafter model. Lossy speculative decoding allows a drift w…
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…
Boosting gets full Attention for Relational Learning
Mathieu Guillame-Bert, Richard Nock
More often than not in benchmark supervised ML, tabular data is flat, i.e. consists of a single (rows, columns) file, but cases abound in the real world where observat…