8 citations · 10 across the 3 of their papers we have counts for
5 papers · 1 filter
AMF: Aggregated Mondrian Forests for Online Learning
Jaouad Mourtada, Stéphane Gaïffas, Erwan Scornet
Random Forests (RF) is one of the algorithms of choice in many supervised learning applications, be it classification or regression. The appeal of such tree-ensemble methods comes…
On the optimality of the Hedge algorithm in the stochastic regime
Jaouad Mourtada, Stéphane Gaïffas
In this paper, we study the behavior of the Hedge algorithm in the online stochastic setting. We prove that anytime Hedge with decreasing learning rate, which is one of the simples…
Minimax optimal rates for Mondrian trees and forests
Jaouad Mourtada, Stéphane Gaïffas, Erwan Scornet
Introduced by Breiman, Random Forests are widely used classification and regression algorithms. While being initially designed as batch algorithms, several variants have been propo…
Universal consistency and minimax rates for online Mondrian Forests
Jaouad Mourtada, Stéphane Gaïffas, Erwan Scornet
We establish the consistency of an algorithm of Mondrian Forests, a randomized classification algorithm that can be implemented online. First, we amend the original Mondrian Forest…
Efficient tracking of a growing number of experts
Jaouad Mourtada, Odalric-Ambrym Maillard
We consider a variation on the problem of prediction with expert advice, where new forecasters that were unknown until then may appear at each round. As often in prediction with ex…