ABSTRACT Various Position Embeddings (PEs) have been proposed in Transformer based
architectures (eg BERT) to model word order. These are empirically-driven and perform well …

Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the
drawbacks of group fairness. Despite its benefits, it depends on a task specific fair metric that …

This paper explores the potential of large language models (LLMs) to substitute for human
participants in market research. Such LLMs can be used to generate text given a prompt. We …

We propose improved fixed-design confidence bounds for the linear logistic model. Our
bounds significantly improve upon the state-of-the-art bound by Li et al.(2017) via recent …

The objective of ordinal embedding is to find a Euclidean representation of a set of abstract
items, using only answers to triplet comparisons of the form" Is item i closer to item j or item …

This paper investigates simultaneous preference and metric learning from a crowd of
respondents. A set of items represented by d-dimensional feature vectors and paired …

Determining the similarities and differences between humans and artificial intelligence is an
important goal both in machine learning and cognitive neuroscience. However, similarities …

Graph embedding, which represents real-world entities in a mathematical space, has
enabled numerous applications such as analyzing natural languages, social networks …

In recent years it has become popular to study machine learning problems in a setting of
ordinal distance information rather than numerical distance measurements. By ordinal …

We address the classical problem of hierarchical clustering, but in a framework where one
does not have access to a representation of the objects or their pairwise similarities. Instead …