Sequential Cohort Selection
- URL: http://arxiv.org/abs/2508.16386v1
- Date: Fri, 22 Aug 2025 13:40:50 GMT
- Title: Sequential Cohort Selection
- Authors: Hortence Phalonne Nana, Christos Dimitrakakis,
- Abstract summary: We study the problem of fair cohort selection from an unknown population, with a focus on university admissions.<n>We start with the one-shot setting, where the admission policy must be fixed in advance and remain transparent.<n>In contrast, the sequential setting allows the policy to be updated across stages as new applicant data becomes available.
- Score: 6.66618805642802
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study the problem of fair cohort selection from an unknown population, with a focus on university admissions. We start with the one-shot setting, where the admission policy must be fixed in advance and remain transparent, before observing the actual applicant pool. In contrast, the sequential setting allows the policy to be updated across stages as new applicant data becomes available. This is achieved by optimizing admission policies using a population model, trained on data from previous admission cycles. We also study the fairness properties of the resulting policies in the one-shot setting, including meritocracy and group parity.
Related papers
- Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variability [18.289154814012996]
We show that removing race data from a developed applicant ranking algorithm reduces the diversity of the top-ranked pool without meaningfully increasing the academic merit of that pool.
We measure the impact of policy change on individuals by comparing the arbitrariness in applicant rank attributable to policy change to the arbitrariness attributable to randomness.
arXiv Detail & Related papers (2024-06-24T14:59:30Z) - Information Capacity Regret Bounds for Bandits with Mediator Feedback [55.269551124587224]
We introduce the policy set capacity as an information-theoretic measure for the complexity of the policy set.
Adopting the classical EXP4 algorithm, we provide new regret bounds depending on the policy set capacity.
For a selection of policy set families, we prove nearly-matching lower bounds, scaling similarly with the capacity.
arXiv Detail & Related papers (2024-02-15T19:18:47Z) - Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline
Reinforcement Learning [57.83919813698673]
Projected Off-Policy Q-Learning (POP-QL) is a novel actor-critic algorithm that simultaneously reweights off-policy samples and constrains the policy to prevent divergence and reduce value-approximation error.
In our experiments, POP-QL not only shows competitive performance on standard benchmarks, but also out-performs competing methods in tasks where the data-collection policy is significantly sub-optimal.
arXiv Detail & Related papers (2023-11-25T00:30:58Z) - Auditing Fairness by Betting [43.515287900510934]
We provide practical, efficient, and nonparametric methods for auditing the fairness of deployed classification and regression models.<n>Our methods are sequential and allow for the continuous monitoring of incoming data.<n>We demonstrate the efficacy of our approach on three benchmark fairness datasets.
arXiv Detail & Related papers (2023-05-27T20:14:11Z) - Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality [94.89246810243053]
This paper studies offline policy learning, which aims at utilizing observations collected a priori to learn an optimal individualized decision rule.<n>Existing policy learning methods rely on a uniform overlap assumption, i.e., the propensities of exploring all actions for all individual characteristics must be lower bounded.<n>We propose Pessimistic Policy Learning (PPL), a new algorithm that optimize lower confidence bounds (LCBs) instead of point estimates.
arXiv Detail & Related papers (2022-12-19T22:43:08Z) - Generalizing Off-Policy Learning under Sample Selection Bias [15.733136147164032]
We propose a novel framework for learning policies that generalize to the target population.
We prove that, if the uncertainty set is well-specified, our policies generalize to the target population as they can not do worse than on the training data.
arXiv Detail & Related papers (2021-12-02T16:18:16Z) - Identification of Subgroups With Similar Benefits in Off-Policy Policy
Evaluation [60.71312668265873]
We develop a method to balance the need for personalization with confident predictions.
We show that our method can be used to form accurate predictions of heterogeneous treatment effects.
arXiv Detail & Related papers (2021-11-28T23:19:12Z) - Fair Sequential Selection Using Supervised Learning Models [11.577534539649374]
We consider a selection problem where sequentially arrived applicants apply for a limited number of positions/jobs.
We show that even with a pre-trained model that satisfies the common fairness notions, the selection outcomes may still be biased against certain demographic groups.
We introduce a new fairness notion, Equal Selection (ES),'' suitable for sequential selection problems and propose a post-processing approach to satisfy the ES fairness notion.
arXiv Detail & Related papers (2021-10-26T19:45:26Z) - Supervised Off-Policy Ranking [145.3039527243585]
Off-policy evaluation (OPE) leverages data generated by other policies to evaluate a target policy.
We propose supervised off-policy ranking that learns a policy scoring model by correctly ranking training policies with known performance.
Our method outperforms strong baseline OPE methods in terms of both rank correlation and performance gap between the truly best and the best of the ranked top three policies.
arXiv Detail & Related papers (2021-07-03T07:01:23Z) - Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients [54.98496284653234]
We consider the task of training a policy that maximizes reward while minimizing disclosure of certain sensitive state variables through the actions.
We solve this problem by introducing a regularizer based on the mutual information between the sensitive state and the actions.
We develop a model-based estimator for optimization of privacy-constrained policies.
arXiv Detail & Related papers (2020-12-30T03:22:35Z) - Offline Policy Selection under Uncertainty [113.57441913299868]
We consider offline policy selection as learning preferences over a set of policy prospects given a fixed experience dataset.
Access to the full distribution over one's belief of the policy value enables more flexible selection algorithms under a wider range of downstream evaluation metrics.
We show how BayesDICE may be used to rank policies with respect to any arbitrary downstream policy selection metric.
arXiv Detail & Related papers (2020-12-12T23:09:21Z) - Intersectional Affirmative Action Policies for Top-k Candidates
Selection [3.4961413413444817]
We study the problem of selecting the top-k candidates from a pool of applicants, where each candidate is associated with a score indicating his/her aptitude.
We consider a situation in which some groups of candidates experience historical and present disadvantage that makes their chances of being accepted much lower than other groups.
We propose two algorithms to solve this problem, analyze them, and evaluate them experimentally using a dataset of university application scores and admissions to bachelor degrees in an OECD country.
arXiv Detail & Related papers (2020-07-29T12:27:18Z) - Towards Data-Driven Affirmative Action Policies under Uncertainty [3.9293125023197595]
We consider affirmative action policies that seek to increase the number of admitted applicants from underrepresented groups.
Since such a policy has to be announced before the start of the application period, there is uncertainty about the score distribution of the students applying to each program.
We explore the possibility of using a predictive model trained on historical data to help optimize the parameters of such policies.
arXiv Detail & Related papers (2020-07-02T15:37:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.