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..
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
Authors: François-Xavier Briol, Chris Oates, Mark Girolami, Michael A. Osborne
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In simulations, FWBQ is competitive with state-of-the-art methods and out-performs alternatives based on Frank-Wolfe optimisation. Our approach is applied to successfully quantify numerical error in the solution to a challenging Bayesian model choice problem in cellular biology. |
| Researcher Affiliation | Academia | Department of Statistics University of Warwick, School of Mathematical and Physical Sciences University of Technology, Sydney, Department of Engineering Science University of Oxford |
| Pseudocode | Yes | Algorithm 1 The Frank-Wolfe (FW) and Frank-Wolfe with Line-Search (FWLS) Algorithms. Require: function J, initial state g1 = g1 G (and, for FW only: step-size sequence {ρi}n i=1). 1: for i = 2, . . . , n do 2: Compute gi = argming G g, (DJ)(gi 1) 3: [For FWLS only, line search: ρi = argminρ [0,1]J (1 ρ)gi 1 + ρ gi ] 4: Update gi = (1 ρi)gi 1 + ρi gi 5: end for |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the release of their implementation code. |
| Open Datasets | No | The paper mentions using "a 20-component mixture of 2D-Gaussian distributions" for the simulation study and applies FWBQ to "one of the model selection tasks in [19]". While [19] is cited, the paper itself does not provide concrete access information (link, DOI, repository) for the specific data or simulation setup used in this paper, nor does it refer to a widely recognized public dataset with clear access. |
| Dataset Splits | No | The paper describes simulation setups and application details (e.g., "n = 10 design points"), but it does not specify any train/validation/test splits for any dataset. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory, specific cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using an "exponentiated-quadratic (EQ) kernel" and "Bayesian Quadrature", but does not list specific software libraries or their version numbers used for implementation (e.g., Python, PyTorch, TensorFlow, SciPy, etc. with versions). |
| Experiment Setup | Yes | Here, the same kernel hyper-parameters (λ, σ) = (1, 0.8) were employed for all methods to have a fair comparison. |