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..
Disposable Linear Bandits for Online Recommendations
Authors: Melda Korkut, Andrew Li4172-4180
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm s performance on a recommendation task based on synthetically generated data. Compared to a number of benchmarks, including Lin UCB and a natural modi๏ฌcation of Thompson sampling, our algorithm (solved via the preceding heuristic) achieves as much as 10% lower regret against all competing algorithms. |
| Researcher Affiliation | Academia | Melda Korkut, Andrew Li Tepper School of Business Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1: Generalized LINUCB (UCBG) and Algorithm 2: Alternating Heuristic |
| Open Source Code | No | The corresponding data can be found in https://github.com/Melda Kor/Disposable Linear Bandits. |
| Open Datasets | Yes | We generated ํพarms, ํ= 1, . . . , ํพin ํ dimensional space where ํพ= 5000 and ํ= 15. Similarly, we generated a set of ํs that lie in the same space, where the total number of ํs is 5000. 3The corresponding data can be found in https://github.com/Melda Kor/Disposable Linear Bandits. |
| Dataset Splits | No | The paper mentions synthetically generated data and number of instances but does not specify explicit training, validation, or test dataset splits or cross-validation details. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instance types) are provided for running the experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers are listed in the paper. |
| Experiment Setup | Yes | For all experiments, we set the tuning parameter ํผfor the heuristic the same as Lin UCB s ํ. In experiments, ํผ, ํ= (1/2)ํwhere ํ= 3, 4, 5. |