This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit
the input data. This is achieved using a mixture model where the mixing coefficients are …

Abstract We introduce Battling-Bandits–an online learning framework where given a set of n
arms, the learner needs to select a subset of k≥ 2 arms in each round and subsequently …

Pairwise preference information, which involves users expressing their preferences by
comparing items, plays a crucial role in decision-making and has recently found application …

In group decision-making (GDM), fuzzy preference relations (FPRs) refer to pairwise
preferences in the form of a matrix. Within the field of GDM, the problem of estimating …

We propose a scalable Bayesian preference learning method for jointly predicting the
preferences of individuals as well as the consensus of a crowd from pairwise labels …

In recent years, non-monotonic Inductive Logic Programming has received growing interest.
Specifically, several new learning frameworks and algorithms have been introduced for …

Abstract The goal of Inductive Logic Programming (ILP) is to find a hypothesis that explains
a set of examples in the context of some pre-existing background knowledge. Until recently …

We consider the problem of reward maximization in the dueling bandit setup along with
constraints on resource consumption. As in the classic dueling bandits, at each round the …

The ubiquitous challenge of learning and decision-making from rank data arises in
situations where intelligent systems collect preference and behavior data from humans …

Density estimation is a challenging unsupervised learning problem. Current maximum
likelihood approaches for density estimation are either restrictive or incapable of producing …