Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
An Information-Theoretic Analysis for Thompson Sampling with Many Actions
Authors: Shi Dong, Benjamin Van Roy
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We establish new bounds that depend instead on a notion of rate-distortion. Among other things, this allows us to recover through information-theoretic arguments a near-optimal bound for the linear bandit. We also offer a bound for the logistic bandit that dramatically improves on the best previously available, though this bound depends on an information-theoretic statistic that we have only been able to quantify via computation. and Theorem 1. Let {Θk}K k=1 be any partition of Θ such that for any k = 1, . . . , K and θ, θ Θk, d(θ, θ ) ϵ. Let ψ be defined as in (4) and let θ t and θt satisfy the conditions in Proposition 2. We have Bayes Regret(T; πTS) q Γ I(θ ; ψ) T + ϵ T, |
| Researcher Affiliation | Academia | Shi Dong Stanford University EMAIL Benjamin Van Roy Stanford University EMAIL |
| Pseudocode | No | The paper describes theoretical concepts and proofs without including any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and defines problem settings (linear bandits, logistic bandits) rather than using specific, named datasets. The 'simulated information ratio values' in Figure 1 are results from a computation to support a conjecture, not data from a publicly accessible dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation on datasets with explicit splits. |
| Hardware Specification | No | The paper does not specify any hardware details for its theoretical analysis or the computational results presented in Figure 1. |
| Software Dependencies | No | The paper does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, hyperparameters, or training configurations. |