Pith Number

pith:JUGEBUZR

pith:2026:JUGEBUZRR6DH553GPJVZDBVW2N

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Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach

Ajinkya Naik, Ashutosh Gupta, Chaitanya Garg, Kuldeep S. Meel, S. Akshay

Decision tree ensembles can have their sensitivity to small input changes quantified by discretizing the space and counting susceptible regions via algebraic decision diagrams.

arxiv:2605.13830 v1 · 2026-05-13 · cs.AI · cs.LG

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Claims

C1strongest claim

We propose a novel algorithmic technique that can perform this computation efficiently, within a certified error and confidence bound. Our approach is based on encoding the problem as an algebraic decision diagram (ADD), and further splitting it into subproblems that can be solved efficiently and make the computation compositional and scalable.

C2weakest assumption

The discretization of the input space combined with the ADD encoding accurately captures all sensitivity regions without introducing unaccounted approximation errors that affect the certified bounds.

C3one line summary

A compositional algebraic decision diagram algorithm quantifies sensitivity in decision tree ensembles with certified error and confidence bounds, outperforming model counters on benchmarks.

References

47 extracted · 47 resolved · 0 Pith anchors

[1] In: The Thirteenth International Conference on Learning Representations,ICLR2025,Singapore,April24-28,2025.OpenReview.net(2025), https://openreview.net/forum?id=h0vC0fm1q7 2025

[2] In: Gurfinkel, A., Ganesh, V 2024 · doi:10.1007/978-3-031-65630-9

[3] Formal methods in system design10(2), 171–206 (1997) 1997

[4] In2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) 2023 · doi:10.1109/icse48619.2023.00134

[5] Journal of Computational Physics493, 112455 (2023) https://doi.org/10.1016/j.jcp.2023.112455 2023 · doi:10.3389/frai.2023.1124553

Receipt and verification

First computed	2026-05-18T02:44:15.073786Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4d0c40d3318f867ef7667a6b9186b6d3403f017414d064cab9ca56a2ab6f1ba2

Aliases

arxiv: 2605.13830 · arxiv_version: 2605.13830v1 · doi: 10.48550/arxiv.2605.13830 · pith_short_12: JUGEBUZRR6DH · pith_short_16: JUGEBUZRR6DH553G · pith_short_8: JUGEBUZR

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JUGEBUZRR6DH553GPJVZDBVW2N \
  | 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: 4d0c40d3318f867ef7667a6b9186b6d3403f017414d064cab9ca56a2ab6f1ba2

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "1685f66660259bde9ccaaddc866196073e35e37331d821d4210b695b65937b5d",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-13T17:52:19Z",
    "title_canon_sha256": "7cdca6a47c21d818a73a33d19a1e6c3cc3394e85214f076cea826e887fa3a76d"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13830",
    "kind": "arxiv",
    "version": 1
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}