Pith Number

pith:DCFPL3RP

pith:2026:DCFPL3RPUACYOX6ZFC6UXDMQ7M

not attested not anchored not stored refs pending

ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization

Foo Hui-Mean, Yuan-chin I Chang

ALMAB-DC pairs Gaussian process active learning with multi-armed bandit allocation and asynchronous distributed scheduling to cut regret and wall-clock time on expensive black-box tasks.

arxiv:2603.21180 v4 · 2026-03-22 · cs.LG · stat.CO · stat.ME · stat.ML

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{DCFPL3RPUACYOX6ZFC6UXDMQ7M}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

ALMAB-DC achieves lower simple regret than Equal Spacing, Random, and D-optimal designs on statistical tasks, 93.4% CIFAR-10 accuracy outperforming BOHB and Optuna, 36.9% drag reduction, 50% RL improvement, and 7.5x speedup at K=16, with all advantages statistically significant.

C2weakest assumption

The Gaussian process surrogate accurately models the black-box objective and that the UCB/Thompson sampling bandit controller with asynchronous scheduler effectively allocates evaluations without significant overhead or synchronization issues.

C3one line summary

ALMAB-DC integrates Gaussian process active learning with multi-armed bandit allocation and distributed asynchronous computing to achieve lower regret and faster wall-clock performance in sequential experimental design.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-06-04T01:08:49.004877Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

188af5ee2fa005875fd928bd4b8d90fb1f845c086e8b0f82e22056954a0d88ad

Aliases

arxiv: 2603.21180 · arxiv_version: 2603.21180v4 · doi: 10.48550/arxiv.2603.21180 · pith_short_12: DCFPL3RPUACY · pith_short_16: DCFPL3RPUACYOX6Z · pith_short_8: DCFPL3RP

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DCFPL3RPUACYOX6ZFC6UXDMQ7M \
  | 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: 188af5ee2fa005875fd928bd4b8d90fb1f845c086e8b0f82e22056954a0d88ad

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7caab0afb98dae47b8fb199dfc217472078b99a0d41b937d354400297159a952",
    "cross_cats_sorted": [
      "stat.CO",
      "stat.ME",
      "stat.ML"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-03-22T11:47:20Z",
    "title_canon_sha256": "17c146c9b7af9956c5f049f3c259b94beb2b2bc5b1ee742e4c0ad14e5d18304f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.21180",
    "kind": "arxiv",
    "version": 4
  }
}