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..
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
Authors: Aadirupa Saha, Pierre Gaillard, Michal Valko
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present the empirical evaluation of our proposed algorithms (Sec. 3 and 4) comparing their performances with the two existing sleeping bandit algorithms that apply to our problem setting, i.e. for adversarial losses and stochastic availabilities. |
| Researcher Affiliation | Collaboration | 1Indian Institute of Science, Bangalore, India. 2Sierra Team, Inria, Paris, France. 3Deep Mind, Paris, France. |
| Pseudocode | Yes | Algorithm 1 Sleeping-EXP3 |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link or explicit statement of code release) for its source code. |
| Open Datasets | No | The paper describes generating data for its experiments (e.g., 'We consider K = 20 and generate the probabilities of item availabilities {ai}i [K] independently and uniformly at random from the interval [0.3, 0.9].'), but it does not use a publicly available dataset or provide access information for the data generated for the experiments. |
| Dataset Splits | No | The paper mentions 'T = 5000 time steps' for the experimental runs but does not specify any explicit training, validation, or test dataset splits or splitting methodology. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, cloud resources) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The algorithm parameters η, λt, δ are set as defined in Thm7. In all cases, we report the cumulative regret of the algorithms for T = 5000 time steps, each averaged over 50 runs. We consider K = 20. |