5 citations · 17 across the 23 of their papers we have counts for
7 papers · 1 filter
Contextual Bandits and Optimistically Universal Learning
Moise Blanchard, Steve Hanneke, Patrick Jaillet
We consider the contextual bandit problem on general action and context spaces, where the learner's rewards depend on their selected actions and an observable context. This general…
Fréchet Mean Set Estimation in the Hausdorff Metric, via Relaxation
Moise Blanchard, Adam Quinn Jaffe
This work resolves the following question in non-Euclidean statistics: Is it possible to consistently estimate the Fréchet mean set of an unknown population distribution, with resp…
Additional Results and Extensions for the paper "Probabilistic bounds on the Traveling Salesman Problem and the Traveling Repairman Problem''
Moïse Blanchard, Alexandre Jacquillat, Patrick Jaillet
This technical report provides additional results for the main paper ``Probabilistic bounds on the Traveling Salesman Problem (TSP) and the Traveling Repairman Problem (TRP…
Probabilistic bounds on the Traveling Salesman Problem and the Traveling Repairman Problem
Moïse Blanchard, Alexandre Jacquillat, Patrick Jaillet
The traveling salesman problem (-TSP) seeks a tour of minimal length that visits a subset of points. The traveling repairman problem (TRP) seeks a complete tour wi…
Universal Regression with Adversarial Responses
Moïse Blanchard, Patrick Jaillet
We provide algorithms for regression with adversarial responses under large classes of non-i.i.d. instance sequences, on general separable metric spaces, with provably minimal assu…
Universal Online Learning with Unbounded Losses: Memory Is All You Need
Moise Blanchard, Romain Cosson, Steve Hanneke
We resolve an open problem of Hanneke on the subject of universally consistent online learning with non-i.i.d. processes and unbounded losses. The notion of an optimistically unive…
Universal Online Learning: an Optimistically Universal Learning Rule
Moïse Blanchard
We study the subject of universal online learning with non-i.i.d. processes for bounded losses. The notion of an universally consistent learning was defined by Hanneke in an effort…