pith:6U3QYYO4
MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound
Streaming decision trees achieve reliable splits for online class-incremental learning by using a K-independent McDiarmid bound on Gini impurity together with Bayesian inheritance and quantile sketches.
arxiv:2605.11617 v2 · 2026-05-12 · cs.LG · math.ST · stat.TH
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Claims
MIST resolves both failures through three integrated components: (i) a tight, K-independent McDiarmid confidence radius for Gini splitting that acts as a structural regulariser; (ii) a Bayesian inheritance protocol that projects parent statistics to child nodes via truncated-Gaussian moments, with variance reduction guarantees strongest precisely when splitting is most conservative; and (iii) per-leaf KLL quantile sketches that support both continuous threshold evaluation and geometry-adaptive leaf prediction from a single data structure.
The assumption that a McDiarmid bound applied to Gini impurity yields a practically tight and K-independent radius under the streaming, non-stationary data regime, and that the truncated-Gaussian moment projection accurately captures the statistical relationship between parent and child nodes without introducing bias that grows with class count.
MIST fixes unreliable splits in streaming decision trees for class-incremental learning by using a K-independent McDiarmid bound on Gini impurity, Bayesian moment projection for knowledge transfer, and KLL quantile sketches for adaptive leaf predictions.
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| First computed | 2026-05-20T00:05:47.060164Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
f5370c61dcdb4b532ef43d6a8c8265356527366844fcd37282ed1fd1862a1637
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Canonical record JSON
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