Generalized Sobolev Transport for Probability Measures on a Graph
Authors: Tam Le, Truyen Nguyen, Kenji Fukumizu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically illustrate that GST is several-order faster than the OW. Moreover, we provide preliminary evidences on the advantages of GST for document classification and for several tasks in topological data analysis. |
| Researcher Affiliation | Academia | 1Department of Advanced Data Science, The Institute of Statistical Mathematics (ISM), Tokyo, Japan 2RIKEN AIP, Tokyo, Japan 3The University of Akron, Ohio, US. |
| Pseudocode | No | The paper does not include pseudocode or a clearly labeled algorithm block for its own method. |
| Open Source Code | Yes | We have also released code for our proposed approach.2 The code repository is on https://github.com/ lttam/Generalized-Sobolev-Transport |
| Open Datasets | Yes | We consider 4 traditional document datasets: TWITTER, RECIPE, CLASSIC, AMAZON. We consider two tasks: orbit recognition on the synthesis Orbit dataset (Adams et al., 2017), and object shape classification on MPEG7 dataset (Latecki et al., 2000) |
| Dataset Splits | Yes | For each dataset, we randomly split it into 70%/30% for training and test with 10 repeats. We typically choose hyperparameters via cross validation. For validation, we further randomly split the training set into 70%/30% for validation-training and validation with 10 repeats to choose hyper-paramters in our simulations. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU/CPU models, memory specifications) for running its experiments. |
| Software Dependencies | No | The paper mentions using "fmincon Trust Region Reflective solver in MATLAB" and "Libsvm" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For kernel hyperparameter, we choose 1/ t from {qs, 2qs, 5qs} with s = 10, 20, . . . , 90 where qs is the s% quantile of a subset of distances observed on a training set. For SVM regularization hyperparameter, we choose it from {0.01, 0.1, 1, 10}. For the root node z0 in graph G, we choose it from a random 10-root-node subset of V in G. |