We propose to solve a label ranking problem as a structured output regression task. In this
view, we adopt a least square surrogate loss approach that solves a supervised learning …

Label Ranking (LR) is the supervised task of learning a sorting function that maps feature
vectors $ x\in\mathbb {R}^ d $ to rankings $\sigma (x)\in\mathbb S_k $ over a finite set of $ k …

Top-error has become a popular metric for large-scale classification benchmarks due to the
inevitable semantic ambiguity among classes. Existing literature on top-optimization …

In this paper, we advocate the use of setwise contests for aggregating a set of input rankings
into an output ranking. We propose a generalization of the Kemeny rule where one …

In its most traditional setting, the main concern of optimization theory is the search for
optimal solutions for instances of a given computational problem. A recent trend of research …

We study in this paper single-peakedness on arbitrary graphs. Given a collection of
preferences (rankings of alternatives), we aim at determining a connected graph G on which …

In multiclass classification, the goal is to learn how to predict a random label $ Y $, valued in
$\mathcal {Y}=\{1,;\ldots,;{K}\} $ with $ K\geq 3$, based upon observing a rv $ X $, taking its …

Ranking data, ie, ordered list of items, naturally appears in a wide variety of situations,
especially when the data comes from human activities (ballots in political elections, survey …

The datasets used in machine learning and statistics are huge and often imperfect, eg, they
contain corrupted data, examples with wrong labels, or hidden biases. Most existing …

In this thesis we theoretically study questions in the area of Reliable Machine Learning in
order to design algorithms that are robust to bias and noise (Robust Machine Learning) and …