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].
Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit
Authors: Seok-Jin Kim, Min-hwan Oh
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted numerical experiments to evaluate the performance of the greedy algorithm and compare it with existing bandit algorithms, Lin UCB from Abbasi-Yadkori et al. [1] and Lin TS from Agrawal and Goyal [4]. |
| Researcher Affiliation | Academia | Seok-Jin Kim Columbia University New York, NY, USA EMAIL Min-hwan Oh Seoul National Univeristy Seoul, South Korea EMAIL |
| Pseudocode | Yes | Algorithm 1 Lin Greedy: Greedy Linear Contextual Bandit |
| Open Source Code | Yes | We provide code in supplementary material. |
| Open Datasets | No | We conducted numerical experiments to evaluate the performance of the greedy algorithm and compare it with existing bandit algorithms, Lin UCB from Abbasi-Yadkori et al. [1] and Lin TS from Agrawal and Goyal [4]. We conducted experiments for three cases with varying parameters: d = 20, K = 20, T 1000, d = 100, K = 20, T 1000, and d = 20, K = 100, T 1000, and five different distributions of contexts: Uniform in a ball, truncated Student s t, Laplace, Gaussian, and exponential. |
| Dataset Splits | No | The paper describes generating contexts from various distributions and running the bandit algorithms for a fixed number of rounds (T), but it does not specify explicit train/validation/test dataset splits. |
| Hardware Specification | No | The paper describes the experimental settings (d, K, T, and context distributions) but does not specify any particular hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | We conducted numerical experiments to evaluate the performance of the greedy algorithm and compare it with existing bandit algorithms, Lin UCB from Abbasi-Yadkori et al. [1] and Lin TS from Agrawal and Goyal [4]. No specific software versions (e.g., Python version, library versions) are mentioned. |
| Experiment Setup | Yes | We conducted experiments for three cases with varying parameters: d = 20, K = 20, T 1000, d = 100, K = 20, T 1000, and d = 20, K = 100, T 1000, and five different distributions of contexts: Uniform in a ball, truncated Student s t, Laplace, Gaussian, and exponential. |