Pith Number

pith:KNGCKTCK

pith:2026:KNGCKTCKB3YDLDSEBQV4BW5ZKO

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Importance Sampling in Expensive Finite-Sum Optimization via Contextual Bandit Methods

Matt Menickelly

Framing subset selection for stochastic average model methods as a contextual bandit problem lets the Exp4 algorithm use side information to create better sampling distributions than uniform randomization.

arxiv:2604.20657 v2 · 2026-04-22 · math.OC

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\pithnumber{KNGCKTCKB3YDLDSEBQV4BW5ZKO}

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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

We consider the problem of generating sampling distributions for SAM methods as a contextual bandit problem and we apply the Exponential weights algorithm for Exploration and Exploitation with Experts (Exp4). We provide some preliminary numerical results on synthetic problems.

C2weakest assumption

That side information such as alternative lower-fidelity simulations, pre-trained emulators or domain expertise from humans or AI models can be effectively encoded as context for the Exp4 algorithm to produce sampling distributions that meaningfully improve upon standard randomized selection in SAM methods.

C3one line summary

The authors frame subset selection in SAM methods as a contextual bandit problem and apply the Exp4 algorithm, providing preliminary numerical results on synthetic problems.

Receipt and verification

First computed	2026-05-28T01:04:08.472109Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

534c254c4a0ef0358e440c2bc0dbb953a0026cda0a0ea3954c8052815b066a23

Aliases

arxiv: 2604.20657 · arxiv_version: 2604.20657v2 · doi: 10.48550/arxiv.2604.20657 · pith_short_12: KNGCKTCKB3YD · pith_short_16: KNGCKTCKB3YDLDSE · pith_short_8: KNGCKTCK

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/KNGCKTCKB3YDLDSEBQV4BW5ZKO \
  | 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: 534c254c4a0ef0358e440c2bc0dbb953a0026cda0a0ea3954c8052815b066a23

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "bcaac8bf0f6fbcf307c2e0ff8d7fa64bf6f4979c0b9ceb1a17170b52cc7cbc87",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "math.OC",
    "submitted_at": "2026-04-22T15:07:19Z",
    "title_canon_sha256": "b821ac3529e440f58bf1030309006d3f7ab9196103f727408fdb187255e7d057"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.20657",
    "kind": "arxiv",
    "version": 2
  }
}