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..
Latent Bandits.
Authors: Odalric-Ambrym Maillard, Shie Mannor
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments show that, in the most challenging agnostic case, the proposed algorithm achieves excellent performance in several difficult scenarios. |
| Researcher Affiliation | Academia | Odalric-Ambrym Maillard EMAIL The Technion, Faculty of Electrical Engineering 32000 Haifa, ISRAEL Shie Mannor EMAIL The Technion, Faculty of Electrical Engineering 32000 Haifa, ISRAEL |
| Pseudocode | Yes | Algorithm 1 The Single-K-UCB algorithm. ... Algorithm 2 The Multiple-K-UCB algorithm. ... Algorithm 3 The UCB on B algorithm ... Algorithm 4 The A-UCB algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It links to an extended version of the paper for proofs. |
| Open Datasets | No | The paper describes generating data for its experiments based on Bernoulli distributions and specified parameters for |A|, |B|, |C|, and Υ(b), but it does not use a named public dataset or provide access information for a generated dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., train/validation/test percentages or counts). It describes the parameters for the generated environments for numerical experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiments. |
| Experiment Setup | Yes | For each experiment, we show the number of actions |A|, of users |B|, of classes |C|, and the parameters {µa,c}a A,c C when there are not too many. We plot the regret of all algorithms on the same figure: A thick line is used for the mean regret and dashed lines for quantiles at levels 0.25, 0.5, 0.75, 0.95 and 0.99. In all experiments, the parameters {Υ(b)}b B are defined by Υ(b) = wb/ P b B wb, where the weights wb are drawn uniformly randomly in [0.1, 0.9]. Thus for each class, the distortion factor γc is less than 9, and we set the parameter γ of A-UCB to the value γ = 9. For one experiment with given fixed parameters, the algorithms are run over several trials (500) for a large time horizon N = 25000. |