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 [1].
Thresholded Lasso Bandit
Authors: Kaito Ariu, Kenshi Abe, Alexandre Proutiere
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through numerical experiments, we confirm that our algorithm outperforms existing methods. ... In this section, we empirically evaluate the TH Lasso bandit algorithm. |
| Researcher Affiliation | Collaboration | 1EECS and Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden 2Cyberagent, Inc., Tokyo, Japan. |
| Pseudocode | Yes | Algorithm 1 TH Lasso Bandit |
| Open Source Code | Yes | An implementation of our method is available at https://github.com/Cyber Agent AILab/ thresholded-lasso-bandit. |
| Open Datasets | Yes | We use the R6A dataset3 that contains a part of the user view/click log for articles displayed on the Yahoo! s Today Module. 3https://webscope.sandbox.yahoo.com |
| Dataset Splits | No | For the real-world dataset, the paper states: "we subsampled the data so that each event is used with probability 0.9 for each instance." However, it does not provide specific training, validation, or test dataset splits for reproducibility, nor does it define such splits for the synthetic data generation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions using "Lasso Bandit," "Doubly-Robust Lasso bandit," and "SA Lasso bandit" algorithms and refers to hyperparameters tuned from GitHub repositories, but it does not specify version numbers for any key software components or libraries (e.g., Python, PyTorch, TensorFlow, etc.). |
| Experiment Setup | Yes | For the SA Lasso bandit and TH Lasso bandit algorithms, we tune the hyperparameter λ0 in [0.01, 0.5] to roughly optimize the algorithm performance when K = 2, d = 1000, Amax = 10, and s0 = 5. As a result, we set λ0 = 0.16 for SA Lasso bandit, and set λ0 = 0.02 for TH Lasso bandit. |