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..
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. |