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..
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
Authors: Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results in Section 5, and conclude with a discussion in Section 6. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India. |
| Pseudocode | Yes | Algorithm 1 LUCB-k-m: Algorithm to select k (ϵ, m)-optimal arms; Algorithm 2 P3: Algorithm to solve Q-P; Algorithm 3 KQP-1: Algorithm to solve an at most k-equiprobable (k, ρ) instances |
| Open Source Code | No | The paper does not provide a direct statement or link for the open-source code of the described methodology. |
| Open Datasets | No | We take five Bernoulli instance of sizes n = 10, 20, 50, 100, and 200, with the means linearly spaced between 0.999 and 0.001 (both inclusive), and sorted in descending order. No link or citation to a publicly available dataset is provided. |
| Dataset Splits | No | The paper describes bandit instances and sample complexities, but does not provide details on traditional training/validation/test dataset splits as it's not a supervised learning task. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'KL-divergence based confidence bounds' but does not specify any software packages or their version numbers. |
| Experiment Setup | Yes | Setting ϵ = 0.05, δ = 0.001, and m = 0.1 n, we run the experiments and Fixing A = I20, n = 20, m = 10, (k, m, n) instances are given by and varying k {1, 3, 5, 8, 10}. |