Add to ORKGAbstract. In max-margin learning, the system aims at es-tablishing a solution as robust as possible. In this paper we extend the idea of max-margin learning to cases where the goal is to produce a set of solutions, instead of a single one. In particular, we focus on the problem of preference elicitation, but we believe our idea could be adapted to other situations. We present a MILP formulation for our “setwise ” max margin learner and discuss current ongoing works
Recently a promising research direction of statistical learning has been advocated, i.e., the optima...
International audienceWe consider the problem of eliciting a model for ordered classification. In pa...
We present a new margin-based approach to first-order rule learning. The approach addresses many of...
In a decision-making problem, where we need to choose a particular decision from a set of possible c...
In the task of preference learning, there can be natural invariance properties that one might often ...
In this paper we consider the problem of simultaneously eliciting the preferences of a group of user...
One natural way to express preferences over items is to represent them in the form of pairwise compa...
A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbala...
120 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.Third, we address an importan...
Despite the convexity of structured max-margin objectives (Taskar et al., 2004; Tsochantaridis et al...
Partial label learning deals with the problem that each training example is associated with a set of...
Abstract. We present a family of Perceptron-like algorithms with margin in which both the “effective...
We propose a new online learning algorithm which provably approximates maximum margin classifiers wi...
This paper studies dimensionality reduction in a weakly supervised setting, in which the prefer-ence...
This paper introduces Classification with Margin Constraints (CMC), a simple generalization of cost-...
Recently a promising research direction of statistical learning has been advocated, i.e., the optima...
International audienceWe consider the problem of eliciting a model for ordered classification. In pa...
We present a new margin-based approach to first-order rule learning. The approach addresses many of...
In a decision-making problem, where we need to choose a particular decision from a set of possible c...
In the task of preference learning, there can be natural invariance properties that one might often ...
In this paper we consider the problem of simultaneously eliciting the preferences of a group of user...
One natural way to express preferences over items is to represent them in the form of pairwise compa...
A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbala...
120 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.Third, we address an importan...
Despite the convexity of structured max-margin objectives (Taskar et al., 2004; Tsochantaridis et al...
Partial label learning deals with the problem that each training example is associated with a set of...
Abstract. We present a family of Perceptron-like algorithms with margin in which both the “effective...
We propose a new online learning algorithm which provably approximates maximum margin classifiers wi...
This paper studies dimensionality reduction in a weakly supervised setting, in which the prefer-ence...
This paper introduces Classification with Margin Constraints (CMC), a simple generalization of cost-...
Recently a promising research direction of statistical learning has been advocated, i.e., the optima...
International audienceWe consider the problem of eliciting a model for ordered classification. In pa...
We present a new margin-based approach to first-order rule learning. The approach addresses many of...