Bayesian Active Learning with Fully Bayesian Gaussian Processes
Authors: Christoffer Riis, Francisco Antunes, Frederik Hüttel, Carlos Lima Azevedo, Francisco Pereira
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Across six simulators, we empirically show that B-QBC, on average, achieves the best marginal likelihood, whereas QB-MGP achieves the best predictive performance. We show that incorporating the bias-variance trade-off in the acquisition functions mitigates unnecessary and expensive data labeling. |
| Researcher Affiliation | Academia | Christoffer Riis Francisco Antunes Frederik Boe Hüttel Carlos Lima Azevedo Francisco Câmara Pereira DTU Management Machine Learning for Smart Mobility {chrrii,franant,fbohy,climaz,camara}@dtu.dk |
| Pseudocode | Yes | See Appendix A.6 for the formulas and the pseudo-code. |
| Open Source Code | Yes | The code for reproducing the experiments is available on Git Hub.2 https://github.com/coriis/active-learning-fbgp |
| Open Datasets | Yes | All of them can be found at https://www.sfu.ca/~ssurjano/ [Surjanovic and Bingham, 2022]. |
| Dataset Splits | No | The paper describes data acquisition for active learning (e.g., initial data sets, unlabeled pool) but does not specify a conventional train/validation/test dataset split. |
| Hardware Specification | Yes | With seven simulators and five acquisition functions, this gives 350 active learning runs, each with a running time on approximately one hour, using five CPU cores on a Threadripper 3960X. |
| Software Dependencies | No | The paper mentions software like NUTS, Pyro, and GPy Torch, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | In each iteration of the active learning loop, the inputs are rescaled to the unit cube [0, 1]d, and the outputs are standardized to have zero mean and unit variance. Following Lalchand and Rasmussen [2020], we give all the hyperparameters relatively uninformative N(0, 3) priors in log space. The initial data sets consist of three data points chosen by maximin Latin Hypercube Sampling [Pedregosa et al., 2011], and in each iteration, one data point is queried. ... The inference in FBGP is carried out using NUTS [Hoffman and Gelman, 2014] in Pyro [Bingham et al., 2019] with five chains and 500 samples, including a warm-up period with 200 samples. The remaining 1500 samples are all used for the acquisition functions. |