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..
Practical Contextual Bandits with Regression Oracles
Authors: Dylan Foster, Alekh Agarwal, Miroslav Dudik, Haipeng Luo, Robert Schapire
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In an extensive empirical evaluation, we find that our approach typically matches or outperforms both realizability-based and agnostic baselines. |
| Researcher Affiliation | Collaboration | 1Cornell University. Work performed while the author was an intern at Microsoft Research. 2Microsoft Research 3University of Southern California. |
| Pseudocode | Yes | Algorithm 1 REGCB.ELIMINATION ... Algorithm 2 REGCB.OPTIMISTIC ... Algorithm 3 BINSEARCH |
| Open Source Code | No | The paper references an implementation for baselines ("We use an implementation available at https://github. com/akshaykr/oracle_cb"), but does not state that the code for their own proposed methods (Reg CB) is open-source or available. |
| Open Datasets | Yes | We use two large-scale learning-to-rank datasets, Microsoft MSLRWEB30k (mslr) (Qin & Liu, 2010) and Yahoo! Learning to Rank Challenge V2.0 (yahoo) (Chapelle & Chang, 2011)... We also use eight classification datasets from the UCI repository (Lichman, 2013). |
| Dataset Splits | Yes | Each dataset is split into training data , for which algorithm receives one example at a time and must predict online, and a holdout validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions using specific software for baselines (e.g., scikit-learn implicitly, as it's cited generally), but does not list specific software dependencies with version numbers needed to replicate the experiments for their own methods. |
| Experiment Setup | Yes | Parameter Tuning: For -Greedy we tune the constant , and for ILTCB we tune a certain smoothing parameter (see Appendix B). For Algorithms 1 and 2 we set βm = β for all m and tune β. For Algorithm 2 we use a warm start of 0. We tune a confidence parameter similar to β for Bootstrap-TS. |