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..
Pure Exploration and Regret Minimization in Matching Bandits
Authors: Flore Sentenac, Jialin Yi, Clement Calauzenes, Vianney Perchet, Milan Vojnovic
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present numerical results in Section 5. They validate the tightness of our results and demonstrate competitiveness and performance gains obtained by our proposed algorithms over some state-of-the-art baseline algorithms. |
| Researcher Affiliation | Collaboration | 1CREST, ENSAE Paris, Palaiseau, France 2London School of Economics, London, UK 3Criteo AI Lab, Paris, France 4CREST, ENSAE Paris, Palaiseau, France & Criteo AI Lab, Paris, France. |
| Pseudocode | Yes | Algorithm Sketch 1 PAIR-ELIM, Algorithm Sketch 2 PAIR-ELIM-MONO, Algorithm Sketch 3 MATCHING-ID, and Algorithm 4 SIMPLE-ADAPTIVE-MATCHING are all present in the paper. |
| Open Source Code | Yes | All the code used for obtaining the results in this section is available from this public Gitlab repository: [anonymized] |
| Open Datasets | No | The experiments are conducted on 'problem instances' generated based on specified parameters (N, u1, etc.) and Bernoulli variables, rather than a named, publicly accessible dataset. There is no link, DOI, or formal citation provided for a public dataset. |
| Dataset Splits | No | The paper defines 'problem instances' for its simulations but does not describe specific train, validation, or test dataset splits (e.g., percentages or sample counts) for reproducibility of data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or libraries with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | We consider the bipartite case with N = M. Each problem instance is defined by a tuple (N, u1, ), where 0 u1 1, and assuming that u1 = v1. and The results are shown in Figure 1. We have further evaluated the effects of varying u1 which is discussed in Appendix A.1. and We consider a set of problem instances, each is defined by a tuple (N, ), where 0 < (N 1) 1 and is the gap between parameter values of adjacent matched pairs in the optimum matching, so that = min. The parameter values are defined by u2i 1 = u2i = (N i) for i [N]. and The results shown in Figure 2 suggest that the cumulative regret of SIMPLE-ADAPTIVE-MATCHING scales as (1/ min)N log(N) as established in Theorem 4.3. We next compare the performance of our algorithm and ESCB. We consider problem instances defined by a tuple (N, µ, ) where µ is the mean of parameter values and is the gap between parameter values. The values of item parameters are set as u2i 1 = u2i = µ+(N+1 2i) /2 [0, 1] for i [N]. Such problem instances allow us to vary µ while keeping other parameters fixed; we fix N = 4 and = 0.1. |