139 citations · 273 across the 16 of their papers we have counts for
7 papers · 1 filter
Fast rates in statistical and online learning
Tim van Erven, Peter D. Grünwald, Nishant A. Mehta +2
The speed with which a learning algorithm converges as it is presented with more data is a central problem in machine learning --- a fast rate of convergence means less data is nee…
Generalized Mixability via Entropic Duality
Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson +1
Mixability is a property of a loss which characterizes when fast convergence is possible in the game of prediction with expert advice. We show that a key property of mixability gen…
Generalised Mixability, Constant Regret, and Bayesian Updating
Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson
Mixability of a loss is known to characterise when constant regret bounds are achievable in games of prediction with expert advice through the use of Vovk's aggregating algorithm.…
A Hybrid Loss for Multiclass and Structured Prediction
Qinfeng Shi, Mark Reid, Tiberio Caetano +2
We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of a log loss for Conditional Random Fields (CRFs) and a multiclass hi…
Tighter Variational Representations of f-Divergences via Restriction to Probability Measures
Avraham Ruderman, Mark Reid, Dario Garcia-Garcia +1
We show that the variational representations for f-divergences currently used in the literature can be tightened. This has implications to a number of methods recently proposed bas…
The Convexity and Design of Composite Multiclass Losses
Mark Reid, Robert Williamson, Peng Sun
We consider composite loss functions for multiclass prediction comprising a proper (i.e., Fisher-consistent) loss over probability distributions and an inverse link function. We es…