AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …

Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

Cross-entropy is a widely used loss function in applications. It coincides with the logistic loss
applied to the outputs of a neural network, when the softmax is used. But, what guarantees …

Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a
suitably chosen reward function. However, designing such a reward function often requires …

Ranking has been extensively studied in information retrieval, machine learning and
statistics. A central problem in ranking is to design a ranking measure for evaluation of …

The question of aggregating pairwise comparisons to obtain a global ranking over a
collection of objects has been of interest for a very long time: be it ranking of online gamers …

We present a detailed study of $ H $-consistency bounds for score-based ranking. These
are upper bounds on the target loss estimation error of a predictor in a hypothesis set $ H …

Over the past decades, numerous loss functions have been been proposed for a variety of
supervised learning tasks, including regression, classification, ranking, and more generally …

We introduce a novel framework of ranking with abstention, where the learner can abstain
from making prediction at some limited cost $ c $. We present a extensive theoretical …

There has been much interest recently in the problem of rank aggregation from pairwise
data. A natural question that arises is: under what sorts of statistical assumptions do various …