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..
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits
Authors: Pierre Perrault, Etienne Boursier, Michal Valko, Vianney Perchet
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments, We compare our CTS policies to CUCB and CUCB-KL, for the shortest path problem on the road chesapeake network [Rossi and Ahmed, 2015]., Our results are shown in Figure 2, where we observe that CLIP CTS-GAUSSIAN (resp. ESCB) is slightly better for c small (resp. large), thus reaching the best of both worlds., Table 2: Computation time per round (ms) |
| Researcher Affiliation | Collaboration | Pierre Perrault Inria Lille ENS Paris-Saclay EMAIL Etienne Boursier ENS Paris-Saclay EMAIL Vianney Perchet ENSAE Criteo AI Lab EMAIL Michal Valko Deep Mind Paris Inria Lille EMAIL |
| Pseudocode | Yes | Algorithm 1 CTS-BETA, Algorithm 2 CTS-GAUSSIAN |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodologies is publicly available. |
| Open Datasets | Yes | shortest path problem on the road chesapeake network [Rossi and Ahmed, 2015] |
| Dataset Splits | No | The paper describes an online learning setting and does not specify traditional train/validation/test dataset splits. It mentions averaged over 50 simulations but not data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or specialized solvers. |
| Experiment Setup | Yes | Before describing the experiments carried out, notice that in the CTS-GAUSSIAN policies, β > 1 is an artefact of the analysis and can in practice be taken equal to 1. This is what we did in our experiments. |