Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
Authors: François-Xavier Briol, Chris Oates, Mark Girolami, Michael A. Osborne
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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. |