Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes

Authors: Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet, Mohan Kankanhalli

ICML 2014 | Conference PDF | Archive PDF | Plain Text | 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.