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..
Machine Learning for Online Algorithm Selection under Censored Feedback
Authors: Alexander Tornede, Viktor Bengs, Eyke Hüllermeier10370-10380
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In an extensive experimental evaluation on an adapted version of the ASlib benchmark, we demonstrate that theoretically well-founded methods based on Thompson sampling perform specifically strong and improve in comparison to existing methods. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Paderborn University 2Institute for Informatics, LMU Munich |
| Pseudocode | Yes | Alg. 1 provides the pseudo code for this revisited Thompson algorithm and a variant inspired by the Buckley-James estimate we discuss in the following. |
| Open Source Code | Yes | All code including detailed documentation and the appendix itself can be found on on Git Hub2. https://github.com/alexandertornede/online_as |
| Open Datasets | Yes | We base our evaluation on the standard algorithm selection benchmark library ASlib (v4.0) (Bischl et al. 2016) |
| Dataset Splits | No | Since ASlib was originally designed for offline AS, we do not use the train/test splits provided by the benchmark, but rather pass each instance one by one to the correspond-ing online approaches, ask them to select an algorithm and return the corresponding feedback. |
| Hardware Specification | Yes | All experiments were run on machines featuring Intel Xeon E5-2695v4@2.1GHz CPUs with 16 cores and 64GB RAM |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The corresponding hyperparameter settings used for the experiments can be found in Section F of the appendix and in the repository, parameter sensitivity analyses in Section G. |