Discrete Probabilistic Inverse Optimal Transport
Authors: Wei-Ting Chiu, Pei Wang, Patrick Shafto
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical contributions include visualizations of cross-ratio equivalent effect on basic examples, simulations validating theoretical results and experiments on real world data. |
| Researcher Affiliation | Academia | 1Department of Mathematics and Computer Science, Rutgers University Newark, NJ 2School of Mathematics, Institute for Advanced Study (IAS), Princeton NJ. |
| Pseudocode | Yes | Algorithm 1 Metro MC |
| Open Source Code | No | No explicit statement or link providing concrete access to open-source code for the methodology described in this paper was found. |
| Open Datasets | Yes | Application on European Migration data. We apply PIOT to analyze European migration flow. The observed migration data T g between 9 European countries for the period 2002-2007 is shown in Fig. 6 top (Raymer et al., 2013). All coupling Ts in this section are reported in Appendix B.9. |
| Dataset Splits | No | No specific training/validation/test dataset splits were explicitly provided. The paper discusses 'burn-in steps' and 'samples with lags' for MCMC, which relates to the sampling process, not data partitioning for model evaluation. |
| Hardware Specification | No | The experiments are performed on computing clusters with nodes equipped with 40 Intel Xeon 2.10 GHz CPUs and 192 GB memory. |
| Software Dependencies | Yes | We compare posterior sampled by MHMC to a m-dimensional uniform symmetric Dirichlet distribution generated by the Sci Py package (Virtanen et al., 2020). |
| Experiment Setup | Yes | We choose α = 1 for the Dirichlet prior P0(K), σ0 = 0.5, γ = 3, and δ = 1.0. We run for 10,000 burn-in steps and take 10,000 samples with lags of 100. We use σ = 10−6 for the burn-in steps and σ = 10−7. |