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 …

We introduce a new type of query mechanism for collecting human feedback, called the
perceptual adjustment query (PAQ). Being both informative and cognitively lightweight, the …

We study metric learning from preference comparisons under the ideal point model, in which
a user prefers an item over another if it is closer to their latent ideal item. These items are …

We introduce the problem of learning a distribution of user preferences over a set of items
from noisy responses to distance queries. Rather than aiming to learn the preferences of …

Large foundation models pretrained on raw web-scale data are not readily deployable
without additional step of extensive alignment to human preferences. Such alignment is …

Large foundation models require extensive\textit {alignment} to human preferences before
deployment. Existing methods utilize the Bradley-Terry-Luce (BTL) model\cite …

Using triplet comparison queries of the form “Do you think item a is more similar to item b or
item c?” to learn a positive definite matrix to capture a distance metric in Rd has been a …

In this chapter 1, we show that the expressiveness of an extremely simple query, the paired
comparison, is much greater than established in previous work. In the context of human …

In interactive machine learning, learners utilize data collected from interacting with the
environment or with humans to better achieve their goals. Real-world applications often …

We introduce the problem of learning distribution of user preference over a population via
pairwise comparison of a set of items. We consider the setting where each individual only …