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pith:XPMPOXGZ

pith:2026:XPMPOXGZJZYTJVRAQ7KV3RZVPX
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Optimal Recourse Summaries via Bi-Objective Decision Tree Learning

Athanasios Voulodimos, Giorgos Stamou, Ioannis Chatzis, Jason Liartis

SOGAR finds the complete Pareto front of recourse summaries by learning bi-objective decision trees that balance effectiveness against cost.

arxiv:2605.07598 v2 · 2026-05-08 · cs.LG

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Claims

C1strongest claim

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.

C2weakest assumption

That shallow axis-parallel decision trees combined with sparse leaf actions are sufficient to produce stable, globally effective recourse summaries without losing critical population structure or requiring more expressive models.

C3one line summary

SOGAR learns Pareto-optimal recourse summaries by solving a bi-objective decision tree problem, yielding stable low-cost effective group actions that outperform prior methods on effectiveness and cost.

Formal links

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Receipt and verification
First computed 2026-05-22T01:04:05.286712Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

bbd8f75cd94e7134d62087d55dc7357dd007b0819d29a41ead0cc815ec31cd54

Aliases

arxiv: 2605.07598 · arxiv_version: 2605.07598v2 · doi: 10.48550/arxiv.2605.07598 · pith_short_12: XPMPOXGZJZYT · pith_short_16: XPMPOXGZJZYTJVRA · pith_short_8: XPMPOXGZ
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/XPMPOXGZJZYTJVRAQ7KV3RZVPX \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: bbd8f75cd94e7134d62087d55dc7357dd007b0819d29a41ead0cc815ec31cd54
Canonical record JSON
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-08T11:16:40Z",
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