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..
Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback
Authors: Yihan Du, Yuko Kuroki, Wei Chen7262-7270
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation demonstrates that our algorithms run orders of magnitude faster than the existing ones, and our CPE-BL algorithm is robust across different min settings while our CPE-PL algorithm is the first one returning correct answers for nonlinear reward functions. |
| Researcher Affiliation | Collaboration | Yihan Du,1 Yuko Kuroki,2 Wei Chen3 1IIIS, Tsinghua University, 2The University of Tokyo, RIKEN, 3Microsoft Research EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: ALBA(S, δ) (Tao, Blanco, and Zhou 2018), Algorithm 2: Elim Tilp(S, δ), Algorithm 3: Vector Est(λ, n), Algorithm 4: Poly ALBA, Algorithm 5: Computing a distribution λ, Algorithm 6: GCB-PE |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes generating data: 'θ1, . . . , θd is set as a geometric sequence in [0, 1]. We simulate the random feedback for action x by a Gaussian distribution with mean of x θ and unit variance.' This indicates a simulated or synthetic dataset, not a publicly available one with concrete access information. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages for train/validation/test, specific sample counts, or citations to predefined splits) to reproduce data partitioning. |
| Hardware Specification | Yes | We evaluate all the compared algorithms on Intel Xeon E5-2640 v3 CPU at 2.60GHz with 132GB RAM. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers. |
| Experiment Setup | No | The paper does not provide concrete hyperparameter values or detailed training configurations in the main text that would be typical for machine learning experiments (e.g., learning rate, batch size, epochs). |