Bayesian Adaptive Calibration and Optimal Design
Authors: Rafael Oliveira, Dino Sejdinovic, David Howard, Edwin V. Bonilla
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the benefits of our method when compared to related approaches across synthetic and real-data problems. |
| Researcher Affiliation | Collaboration | Rafael Oliveira CSIRO s Data61 Sydney, Australia Dino Sejdinovic University of Adelaide Adelaide, Australia David Howard CSIRO s Data61 Brisbane, Australia Edwin V. Bonilla CSIRO s Data61 Sydney, Australia |
| Pseudocode | Yes | Algorithm 1 BACON |
| Open Source Code | Yes | 4Code available at: https://github.com/csiro-funml/bacon |
| Open Datasets | No | For this experiment, we are provided with a dataset containing R = 10 real measurements of the peak grasping force of soft robotic gripper designs on a range of testing objects (see Fig. 3). |
| Dataset Splits | No | The paper mentions initial data and test points for evaluation, but does not explicitly describe a distinct validation dataset split used for hyperparameter tuning or model selection during training. |
| Hardware Specification | No | The paper mentions that experiments run on a 'high-performance computing platform' and 'CSIRO IMT Scientific Computing' but does not provide specific hardware details like GPU/CPU models or memory. |
| Software Dependencies | No | Our implementation for BACON and most of the baselines, except for VBMC,6 is based on Pyro probabilistic programming models [50]. Gaussian process modelling code is based on Bo Torch7 [51]. The flow architecture is chosen for each synthetic-data problem by running hyper-parameter tuning with a simplified version of the problem. Most Gaussian process models are parameterised with Mat ern kernels [2, Ch. 4] and constant or zero mean functions. Pyro s MCMC with its default no-U-turn (NUTS) sampler [52] was applied to obtain samples from p(θ |Dt 1) at each iteration t. KL divergences are computed from samples using a nearest neighbours estimator implemented in the information theoretical estimators (ITE) package8 [41]. |
| Experiment Setup | Yes | We run each algorithm for T := 50 iterations using a batch of B := 4 designs per iteration. Each of the methods using GP approximations for the simulator are initialised with 20 observations and R = 5 real data points. ... Gradient-based optimisation is run using Adam with a learning rate 10 3 for the flow parameters and 0.05 for the simulation design points, both using cosine annealing with warm restarts as a learning rate scheduler. |