An LP-based Sampling Policy for Multi-Armed Bandits with Side-Observations and Stochastic Availability

An LP-based Sampling Policy for Multi-Armed Bandits with Side-Observations and Stochastic Availability | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/an-lp-based-sampling-policy-for-multi-armed-bandits-with-side-observations-and-stochastic-availability

stale

Proof freshness: stale
Proof status: unverified
Display score: 4/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 22
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID an-lp-based-sampling-policy-for-multi-armed-bandits-with-side-observations-and-stochastic-availability | Route /signal-canvas/an-lp-based-sampling-policy-for-multi-armed-bandits-with-side-observations-and-stochastic-availability

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MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 7

References: 22

Proof: Verification pending

Freshness state: computing

Source paper: An LP-based Sampling Policy for Multi-Armed Bandits with Side-Observations and Stochastic Availability

PDF: https://arxiv.org/pdf/2603.26647v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T22:29:46.374Z

Signal Canvas receipt window

Not build-ready: An LP-based Sampling Policy for Multi-Armed Bandits with Side-Observations and Stochastic Availability

/buildability/an-lp-based-sampling-policy-for-multi-armed-bandits-with-side-observations-and-stochastic-availability

Ignoreblocked

Subject: An LP-based Sampling Policy for Multi-Armed Bandits with Side-Observations and Stochastic Availability

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
UCB-LP-A, a novel policy that leverages a Linear Programming (LP) approach to optimize exploration-exploitation trade-offs under stochastic availability.
Implicationpartial
The abstract explicitly states the novelty and the core methodology of the proposed policy.
Verificationpartial
partial
Evidencepartial
UCB-LP-A computes an optimal sampling distribution over the realizable activation sets, ensuring that the necessary observations are gathered using only the currently active arms.
Implicationpartial
The abstract clearly describes how the policy addresses the challenge of stochastic availability.
Verificationpartial
partial
Evidencepartial
We derive a theoretical upper bound on the regret of our policy, characterizing the impact of both the network structure and the activation probabilities.
Implicationpartial
The abstract explicitly mentions the derivation of a theoretical regret bound and its dependencies.
Verificationpartial
partial
Evidencepartial
Finally, we demonstrate through numerical simulations that UCB-LP-A significantly outperforms existing heuristics that ignore either the side-information or the availability constraints.
Implicationpartial
The abstract concludes with a strong statement about the performance of the proposed policy compared to existing methods.
Verificationpartial
partial
Evidencepartial
This framework models real-world systems with both structural dependencies and volatility, such as social networks where users provide side-information about their peers' preferences, yet are not always online to be queried.
Implicationpartial
The abstract provides a clear motivation and real-world analogy for the problem formulation.
Verificationpartial
partial
Evidencepartial
The objective function minimizes the total expected sampling frequency. The primary constraint imposes a global observability requirement, ensuring that every base-armiaccumu-lates at least unit mass of observation across all activation sets.
Implicationpartial
The description of the LP problem in the abstract and the subsequent section clearly outlines its objective and constraints.
Verificationpartial
partial
Evidencepartial
This demonstrates that information gain is not strictly bound by an arm’s instantaneous avail-ability. By strategically leveraging the network topology and weigning the occurrence probabilitiesp, our policy efficiently accumulates observations for specific arms through these side-channels, even when those arms are effectively ‘offline’.
Implicationpartial
The analysis excerpt clearly explains how side-observations overcome the limitation of stochastic availability.
Verificationpartial
partial

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An LP-based Sampling Policy for Multi-Armed Bandits with Side-Observations and Stochastic Availability

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