Predictive Approximate Bayesian Computation via Saddle Points
Authors: Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical Experiment We test the performance of P-ABC and compare the result with K2and DR-ABC as representatives from samplingand regression-based ABC algorithms. Table 1: MSE for estimating the model parameter with different dimensions using K2-, DRand P-ABC. Figure 3: Statistics of MSEs for P-, K2and DRABC trained on 1000 sequences of length 30. |
| Researcher Affiliation | Collaboration | Yingxiang Yang Bo Dai Negar Kiyavash Niao He {yyang172,kiyavash,niaohe} @illinois.edu bohr.dai@gmail.com Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign. Google Brain. |
| Pseudocode | Yes | Algorithm 1 Predictive ABC (P-ABC) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating synthetic datasets rather than using publicly available ones with explicit access information. For the ecological dynamic system, it cites a previous study ('Park et al. [2016] for example') but does not provide specific access details (link, DOI, or repository) for the dataset itself. |
| Dataset Splits | No | The paper mentions training and test sets and their respective MSEs but does not provide explicit dataset split percentages, counts for validation sets, or citations to predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of neural networks and LSTM cells but does not specify any software libraries or their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Each neural network contains two fully connected layers of size 8 with exponential linear unit (ELU) activation functions, and the final output layer for f is activated using the hyperbolic tangent. We choose ξ R and p0(ξ) 1{ξ [ 1, 1]}, and use a learning rate of 10 4. In 2E5 iterations, P-ABC achieves 0.0413 mean square error (MSE) on the training set and 0.0416 MSE on the test set. For P-ABC, we set ξ R4, the size of thee LSTM cells to be 32, and the size of the fully connected layer to be 16. |