We study the combinatorial assignment domain, which includes combinatorial auctions and
course allocation. The main challenge in this domain is that the bundle space grows …

In this paper, we study the design of deep learning-powered iterative combinatorial auctions
(ICAs). We build on prior work where preference elicitation was done via kernelized support …

We propose a multi-agent learning approach for designing crowdsourcing contests and All-
Pay auctions. Prizes in contests incentivise contestants to expend effort on their entries, with …

We introduce a machine learning-powered course allocation mechanism. Concretely, we
extend the state-of-the-art Course Match mechanism with a machine learning-based …

The advent of a new breed of enhanced multimedia services has put network operators into
a position where they must support innovative services while ensuring both end-to-end …

Many important resource allocation problems involve the combinatorial assignment of items,
eg, auctions or course allocation. Because the bundle space grows exponentially in the …

Set functions are functions (or signals) indexed by the powerset (set of all subsets) of a finite
set N. They are fundamental and ubiquitous in many application domains and have been …

We present a machine learning-powered iterative combinatorial auction (MLCA). The main
goal of integrating machine learning (ML) into the auction is to improve preference …

The rapid development of drone technology is leading to more and more use cases being
proposed. In response, regulators are drawing up drone traffic management frameworks …

Iterative combinatorial auctions (CAs) are often used in multibillion dollar domains like
spectrum auctions, and speed of convergence is one of the crucial factors behind the choice …