Hypergraph partitioning is an important problem in machine learning, computer vision and
network analytics. A widely used method for hypergraph partitioning relies on minimizing a …

This paper explores the preference-based top-K rank aggregation problem. Suppose that a
collection of items is repeatedly compared in pairs, and one wishes to recover a consistent …

We consider relative error low rank approximation of tensors with respect to the Frobenius
norm. Namely, given an order-q tensor A∊ ℝ∏ i= 1 q ni, output a rank-k tensor B for which …

We consider the problem of learning mixtures of generalized linear models (GLM) which
arise in classification and regression problems. Typical learning approaches such as …

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 consider the problem of subset selection where one is given multiple rankings of items
and the goal is to select the highest" quality" subset. Score functions from the multiwinner …

Tensor Attention, a multi-view attention that is able to capture high-order correlations among
multiple modalities, can overcome the representational limitations of classical matrix …

The Mallows model is a popular distribution for ranked data. We empirically and theoretically
analyze how the properties of rankings sampled from the Mallows model change when …

Group decision making is an important, long‐standing, and ubiquitous problem in all
societies, where collective decisions must be made by a group of agents despite individual …

The assortment problem in revenue management is the problem of deciding which subset of
products to offer to consumers in order to maximise revenue. A simple and natural strategy is …