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..
Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes
Authors: Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan Kankanhalli
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate using synthetic and real-world datasets that, with limited budget, our proposed approach outperforms state-of-the-art algorithms (Section 4). |
| Researcher Affiliation | Academia | Department of Computer Science, National University of Singapore, Republic of Singapore Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, USA |
| Pseudocode | Yes | Algorithm 1 h , i-BAL(z D0) |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper uses a 'simulated spatial phenomenon' and a 'Real-World Traffic Phenomenon' specific to the Tampines area, Singapore. It does not provide concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | Yes | All experiments are run on a Mac OS X machine with Intel Core i7 at 2.66 GHz. |
| Software Dependencies | No | The paper mentions 'Mac OS X' but does not provide specific software dependencies or library versions (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | The phenomenon is a realization of a GP (Section 2) parameterized by λ = {σλn = 0.25, σλs = 10.0, λ = 1.0}. For simplicity, we assume that σλs are known, but the true length-scale λ = 1 is not. So, a uniform prior belief b D0=; is maintained over a set L = {1, 6, 9, 12, 15, 18, 21} of 7 candidate length-scales λ. |