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Chapter and Conference Paper
s-LIME: Reconciling Locality and Fidelity in Linear Explanations
The benefit of locality is one of the major premises of LIME, one of the most prominent methods to explain black-box machine learning models. This emphasis relies on the postulate that the more locally we look...
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Chapter and Conference Paper
Bandit Algorithm for both Unknown Best Position and Best Item Display on Web Pages
Multiple-play bandits aim at displaying relevant items at relevant positions on a web page. We introduce a new bandit-based algorithm, PB-MHB, for online recommender systems which uses the Thompson sampling fr...
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Chapter and Conference Paper
Sequential Collaborative Ranking Using (No-)Click Implicit Feedback
We study Recommender Systems in the context where they suggest a list of items to users. Several crucial issues are raised in such a setting: first, identify the relevant items to recommend; second, account fo...
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Chapter and Conference Paper
Large-Scale Bandit Recommender System
The main target of Recommender Systems (RS) is to propose to users one or several items in which they might be interested. However, as users provide more feedback, the recommendation process has to take these ...
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Chapter and Conference Paper
Bandits and Recommender Systems
This paper addresses the on-line recommendation problem facing new users and new items; we assume that no information is available neither about users, nor about the items. The only source of information is a ...
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Chapter and Conference Paper
A Principled Method for Exploiting Opening Books
In the past we used a great deal of computational power and human expertise for storing a rather big dataset of good 9x9 Go games, in order to build an opening book. We improved the algorithm used for generati...
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Chapter and Conference Paper
Clustering Rankings in the Fourier Domain
It is the purpose of this paper to introduce a novel approach to clustering rank data on a set of possibly large cardinality n ∈ ℕ*, relying upon Fourier representation of functions defined on the symmetric group...
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Chapter and Conference Paper
A Phase Transition-Based Perspective on Multiple Instance Kernels
This paper is concerned with Relational Support Vector Machines, at the intersection of Support Vector Machines (SVM) and Inductive Logic Programming or Relational Learning. The so-called phase transition fram...
