Optimal Recourse Summaries via Bi-Objective Decision Tree Learning

• URL: http://arxiv.org/abs/2605.07598v1
• Date: Fri, 08 May 2026 11:16:40 GMT
• Title: Optimal Recourse Summaries via Bi-Objective Decision Tree Learning
• Abstract summary: Actionable Recourse provides individuals with actions they can take to change an unfavorable outcome.<n>It is ill-suited for global auditing and bias detection, since aggregating local actions is costly and often inconsistent.<n>Recourse summaries address this limitation by partitioning the population and assigning one shared action per subgroup.
• Score: 9.108496029183568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Actionable Recourse provides individuals with actions they can take to change an unfavorable classifier outcome. While useful at the instance level, it is ill-suited for global auditing and bias detection, since aggregating local actions is costly and often inconsistent. Recourse Summaries address this limitation by partitioning the population and assigning one shared action per subgroup, enabling comparison across subgroups. Designing summaries involves a fundamental trade-off between recourse effectiveness and recourse cost, which existing methods do not adequately address. We introduce Summaries of Optimal and Global Actionable Recourse (SOGAR), which formulates recourse summary learning as an optimal decision tree learning problem and finds the Pareto front -- the complete set of solutions where improving one objective necessarily worsens the other. SOGAR enables post-hoc selection of the desired trade-off without retraining. Using shallow axis-parallel decision trees and sparse leaf actions, SOGAR produces stable, low-cost, and effective recourse summaries that outperform existing approaches across effectiveness and cost metrics.

Related papers

• TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning [52.67756371231985]
  Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.<n>We introduce Tree Rollout Allocation for Contrastive Exploration (TRACE), a unified rollout allocation framework.<n>Technically, TRACE allocates rollout budget to both prompt roots and intermediate prefixes that are most likely to yield mixed terminal rewards.
  arXiv  Detail & Related papers  (2026-06-09T17:16:03Z)
• Breaking $\ extit{Winner-Takes-All}$: Cooperative Policy Optimization Improves Diverse LLM Reasoning [53.42577591449649]
  Group Cooperative Policy Optimization shifts the training paradigm from rollout competition to team cooperation.<n>GCPO replaces independent rollout scoring with team-level credit assignment.<n>It redistributes the collective team reward to each single rollout according to its average marginal contribution to the team.
  arXiv  Detail & Related papers  (2026-05-12T03:20:24Z)
• Chain of Uncertain Rewards with Large Language Models for Reinforcement Learning [52.48243762705385]
  Chain of Uncertain Rewards (CoUR) is a novel framework that integrates large language models (LLMs) to streamline reward function design and evaluation.<n>We show that CoUR achieves better performance and significantly lowers the cost of reward evaluations.
  arXiv  Detail & Related papers  (2026-04-15T05:44:14Z)
• GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization [133.27496265096445]
  We show how to apply Group Relative Policy Optimization under multi-reward setting without examining its suitability.<n>We then introduce Group reward-Decoupled Normalization Policy Optimization (GDPO), a new policy optimization method to resolve these issues.<n>GDPO consistently outperforms GRPO, demonstrating its effectiveness and generalizability for multi-reward reinforcement learning optimization.
  arXiv  Detail & Related papers  (2026-01-08T18:59:24Z)
• Representative Action Selection for Large Action Space: From Bandits to MDPs [47.980675309210746]
  We study the problem of selecting a small, representative action subset from an extremely large action space shared across a family of reinforcement learning (RL) environments.<n>Our goal is to identify a fixed subset of actions that, for every environment in the family, contains a near-optimal action, thereby enabling efficient learning without exhaustively evaluating all actions.
  arXiv  Detail & Related papers  (2025-11-27T04:49:23Z)
• Recursive Reward Aggregation [60.51668865089082]
  We propose an alternative approach for flexible behavior alignment that eliminates the need to modify the reward function.<n>By introducing an algebraic perspective on Markov decision processes (MDPs), we show that the Bellman equations naturally emerge from the generation and aggregation of rewards.<n>Our approach applies to both deterministic and deterministic settings and seamlessly integrates with value-based and actor-critic algorithms.
  arXiv  Detail & Related papers  (2025-07-11T12:37:20Z)
• GLANCE: Global Actions in a Nutshell for Counterfactual Explainability [10.25011737760687]
  We introduce GLANCE, a versatile and adaptive framework, comprising two algorithms. C-GLANCE employs a clustering approach that considers both the feature space and the space of counterfactual actions. T-GLANCE provides additional features to enhance flexibility.
  arXiv  Detail & Related papers  (2024-05-29T09:24:25Z)
• Contextual Linear Optimization with Partial Feedback [35.38485630117593]
  We propose a class of offline learning algorithms for Contextual linear optimization (CLO) with different types of feedback.<n>We provide a novel fast-rate regret bound for IERM that allows for misspecified model classes and flexible choices of estimation methods.
  arXiv  Detail & Related papers  (2024-05-26T13:27:27Z)
• Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization [61.39201891894024]
  Group distributionally robust optimization (group DRO) can minimize the worst-case loss over pre-defined groups. We reformulate the group DRO framework by proposing Q-Diversity. Characterized by an interactive training mode, Q-Diversity relaxes the group identification from annotation into direct parameterization.
  arXiv  Detail & Related papers  (2023-05-20T07:02:27Z)
• Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse [34.39887495671287]
  We propose an objective function which simultaneously minimizes the gap between the achieved (resulting) and desired recourse invalidation rates. We develop novel theoretical results to characterize the recourse invalidation rates corresponding to any given instance. Experimental evaluation with multiple real world datasets demonstrates the efficacy of the proposed framework.
  arXiv  Detail & Related papers  (2022-03-13T21:39:24Z)

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.