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..
Anytime Exploration for Multi-armed Bandits using Confidence Information
Authors: Kwang-Sung Jun, Robert Nowak
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our analysis shows that the sample complexity of AT-LUCB is competitive to anytime variants of existing algorithms. Moreover, our empirical evaluation on AT-LUCB shows that AT-LUCB performs as well as or better than state-of-the-art baseline methods for anytime Explore-m. |
| Researcher Affiliation | Academia | Kwang-Sung Jun EMAIL Wisconsin Institutes for Discovery, UW-Madison, 330 N. Orchard St., Madison, WI 53715 USA Robert Nowak EMAIL Wisconsin Institutes for Discovery, UW-Madison, 330 N. Orchard St., Madison, WI 53715 USA |
| Pseudocode | Yes | Algorithm 1 AT-LUCB |
| Open Source Code | No | The paper does not provide an explicit link to open-source code for the methodology presented. |
| Open Datasets | Yes | We use the New Yorker dataset.6 The data consists of n = 496 captions with 100K ratings. Footnote 6: Dataset number 499 from https://github.com/nextml/NEXT-data/. |
| Dataset Splits | No | The paper describes the datasets used (toy MAB instances and New Yorker dataset) but does not specify training, validation, or test splits in detail for reproducibility. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU, CPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not mention specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | We run AT-LUCB with δ1 = 1/2, = .99, and = 0. We set the exploration parameter of UCB as 2. We run each method 200 times. |