Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Authors: Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, Francois-Xavier Briol
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 5 demonstrates strong empirical performance on a range of simulators and provides extensive simulation studies on benchmark simulators. For example, Table 1 shows 'Average and standard deviation (in parenthesis) of estimated MMD2...computed over 100 runs'. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Aalto University, Espoo, Finland 2Department of Statistical Science, University College London, London, United Kingdom 3Department of Computer Science, University of Manchester, Manchester, United Kingdom. |
| Pseudocode | Yes | The paper includes "Algorithm 1: Composite goodness-of-fit test" and "Algorithm 2: Random-restart optimiser" in Appendix B.4. |
| Open Source Code | Yes | Our code is available at https://github.com/bharti-ayush/optimally-weighted_MMD. |
| Open Datasets | Yes | The paper uses various benchmark simulators (e.g., g-and-k, Two moons, MA(2), M/G/1 queue, Lotka-Volterra) and real-world data (e.g., "US dollar to Canadian dollar exchange rate data (Verbeek, 2018) from the Ecdat R package", "multivariate g-and-k distribution introduced in (Drovandi & Pettitt, 2011)", "large scale offshore wind farm model (Niayifar & Porté-Agel, 2016; Kirby et al., 2023)"). |
| Dataset Splits | No | No explicit mention of specific train/validation/test dataset splits (e.g., percentages or counts) or cross-validation setup for model training/evaluation. The paper discusses 'n' observed data points and 'm' simulated data points for MMD estimation, but not data partitioning for model validation. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are provided for the experimental setup. The paper mentions 'CPU hours' but without specific hardware types. |
| Software Dependencies | Yes | For drawing iid or RQMC points, we use the implementation from Sci Py (Virtanen et al., 2020). |
| Experiment Setup | Yes | The paper provides specific experimental setup details in Table 4, including 'hyperparameter value alpha 0.05 level of the test B 200 number of bootstrap samples m 100 number of samples from the simulator n 500 number of observations in the data I 50 number of initial parameters sampled R 10 number of initial parameters to optimise S 200 number of gradient steps s 0.04 step size'. Algorithm 2 also details the adam_optimizer with 'S' and 's'. |