# Machine Learning-Powered Course Allocation

arXiv: https://arxiv.org/abs/2210.00954 · HTML: https://mlsanity.com/paper/2210.00954/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [NOMU: Neural Optimization-based Model Uncertainty](https://mlsanity.com/paper/2102.13640/) (2021)
- [Eliciting Single-Peaked Preferences Using Comparison Queries](https://mlsanity.com/paper/1401.3449/) (2014)
- [Deep Learning-powered Iterative Combinatorial Auctions](https://mlsanity.com/paper/1907.05771/) (2019)
- [Machine Learning-powered Iterative Combinatorial Auctions](https://mlsanity.com/paper/1911.08042/) (2019)
- [Bayesian Optimization-based Combinatorial Assignment](https://mlsanity.com/paper/2208.14698/) (2022)
- [Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment](https://mlsanity.com/paper/2109.15117/) (2021)
- [Fourier Analysis-based Iterative Combinatorial Auctions](https://mlsanity.com/paper/2009.10749/) (2020)
- [The Complexity of Fairness through Equilibrium](https://mlsanity.com/paper/1312.6249/) (2013)

## Built on by (most cited first, 3 of 3)

- [LLM-Powered Preference Elicitation in Combinatorial Assignment](https://mlsanity.com/paper/2502.10308/) (2025)
- [Machine Learning-Powered Combinatorial Clock Auction](https://mlsanity.com/paper/2308.10226/) (2023)
- [Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All](https://mlsanity.com/paper/2411.09355/) (2024)

## Abstract (arXiv)

We study the course allocation problem, where universities assign course schedules to students. The current state-of-the-art mechanism, Course Match, has one major shortcoming: students make significant mistakes when reporting their preferences, which negatively affects welfare and fairness. To address this issue, we introduce a new mechanism, Machine Learning-powered Course Match (MLCM). At the core of MLCM is a machine learning-powered preference elicitation module that iteratively asks personalized pairwise comparison queries to alleviate students' reporting mistakes. Extensive computational experiments, grounded in real-world data, demonstrate that MLCM, with only ten comparison queries, significantly increases both average and minimum student utility by 7%-11% and 17%-29%, respectively. Finally, we highlight MLCM's robustness to changes in the environment and show how our design minimizes the risk of upgrading to MLCM while making the upgrade process simple for universities and seamless for their students.