Ideal point based preference learning using pairwise comparisons of type" Do you prefer a
or b?" has emerged as a powerful tool for understanding how we make preferences. Existing …

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 …

Maximum likelihood estimation furnishes powerful insights into voting theory, and the design
of voting rules. However the MLE can usually be badly corrupted by a single outlying …

We consider a symmetric mixture of linear regressions with random samples from the
pairwise comparison design, which can be seen as a noisy version of a type of Euclidean …

Direct Preference Optimization (DPO) has recently emerged as a popular approach to
improve reinforcement learning with human feedback (RLHF), leading to better techniques …

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 …