3 research outputs found
Machine Learning-powered Course Allocation
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 preference elicitation module. In an iterative,
asynchronous manner, this module generates pairwise comparison queries that are
tailored to each individual student. Regarding incentives, our machine
learning-powered course match (MLCM) mechanism retains the attractive
strategyproofness in the large property of Course Match. Regarding welfare, we
perform computational experiments using a simulator that was fitted to
real-world data. Our results show that, compared to Course Match, MLCM
increases average student utility by 4%-9% and minimum student utility by
10%-21%, even with only ten comparison queries. Finally, we highlight the
practicability of MLCM and the ease of piloting it for universities currently
using Course Match
Machine Learning-powered Combinatorial Clock Auction
We study the design of iterative combinatorial auctions (ICAs). The main
challenge in this domain is that the bundle space grows exponentially in the
number of items. To address this, several papers have recently proposed machine
learning (ML)-based preference elicitation algorithms that aim to elicit only
the most important information from bidders. However, from a practical point of
view, the main shortcoming of this prior work is that those designs elicit
bidders' preferences via value queries (i.e., ``What is your value for the
bundle ?''). In most real-world ICA domains, value queries are
considered impractical, since they impose an unrealistically high cognitive
burden on bidders, which is why they are not used in practice. In this paper,
we address this shortcoming by designing an ML-powered combinatorial clock
auction that elicits information from the bidders only via demand queries
(i.e., ``At prices , what is your most preferred bundle of items?''). We
make two key technical contributions: First, we present a novel method for
training an ML model on demand queries. Second, based on those trained ML
models, we introduce an efficient method for determining the demand query with
the highest clearing potential, for which we also provide a theoretical
foundation. We experimentally evaluate our ML-based demand query mechanism in
several spectrum auction domains and compare it against the most established
real-world ICA: the combinatorial clock auction (CCA). Our mechanism
significantly outperforms the CCA in terms of efficiency in all domains, it
achieves higher efficiency in a significantly reduced number of rounds, and,
using linear prices, it exhibits vastly higher clearing potential. Thus, with
this paper we bridge the gap between research and practice and propose the
first practical ML-powered ICA