pith:FAUZONQK
Classification Trees with Valid Inference via the Exponential Mechanism
Classification trees fitted via the exponential mechanism produce pivots for asymptotically valid inference on model parameters.
arxiv:2511.15068 v3 · 2025-11-19 · stat.ME · stat.ML
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Claims
Our method produces pivots directly from the sampling probabilities in the exponential mechanism. In theory, our pivots allow asymptotically valid inference on the parameters in the predictive fit, and in practice, our method delivers powerful inference without sacrificing predictive accuracy, in contrast to data splitting methods.
That the sampling probabilities from the exponential mechanism, when used to define pivots, correctly account for the adaptivity of the entire tree-growing process and yield asymptotically valid inference for the parameters in the final predictive fit.
Classification trees built with the exponential mechanism generate asymptotically valid inference pivots from sampling probabilities without major accuracy loss.
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| First computed | 2026-07-21T02:21:25.911903Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
282997360af71534403f127358d447634930f29cfb73e4663d5d022b10cf4276
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/FAUZONQK64KTIQB7CJZVRVCHMN \
| 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())"
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Canonical record JSON
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