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..
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits
Authors: Weitong Zhang, Jiafan He, Zhiyuan Fan, Quanquan Gu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world datasets corroborate our theoretical results. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of California, Los Angeles, California, USA 2IIIS, Tsinghua University, Beijing, China. Correspondence to: Quanquan Gu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Data Selection OFUL (DS-OFUL) Algorithm 2 Sup Lin UCB |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | To demonstrate that the proposed algorithm can be easily applied to modern machine learning tasks, we carried out experiments on the Asirra dataset (Elson et al., 2007). |
| Dataset Splits | No | The paper discusses the total number of rounds (K) for experiments but does not explicitly provide details on train/validation/test dataset splits, percentages, or methodology for reproducibility. |
| Hardware Specification | Yes | The experiment on synthetic dataset is conducted on Google Colab with a 2-core Intel Xeon CPU @ 2.20GHz. The experiment on the real-world Asirra dataset (Elson et al., 2007) is conducted on an AWS p2xlarge instance. |
| Software Dependencies | No | The paper mentions models like 'Res Net-18' but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or specific library versions). |
| Experiment Setup | Yes | We do a grid search for β = {1, 3, 10}, λ = {1, 3, 10} and report the cumulative regret of Algorithm 1 with different parameter Γ = {0, 0.02, 0.05, 0.08, 0.18} over 8 independent trials with total rounds K = 10000. For hyper-parameter tuning, we select β = {0.1, 0.3, 1} and λ = {1, 3, 10} by doing a grid search and repeat the experiments for 8 times over 1M rounds for each parameter configuration. |