Order Constraints in Optimal Transport
Authors: Yu Chin Fabian Lim, Laura Wynter, Shiau Hong Lim
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate experimentally that order constraints improve explainability using the e-SNLI (Stanford Natural Language Inference) dataset that includes human-annotated rationales as well as on several image color transfer examples. |
| Researcher Affiliation | Industry | IBM Research, Singapore. |
| Pseudocode | Yes | Algorithm 1 Iterative procedure for OT under OC Oij[k] with linear costs f(X) = tr DT X . ... Algorithm 2 e PAVA for C2 = Oij[k] for k [mn] ... Algorithm 3 Learning subtree b T (k1, k2, k3, τ1, τ2) of T (k3, τ1, τ2) and top-k2 candidate plans for linear costs f(Π) = tr DT Π . |
| Open Source Code | Yes | Optimal Transport with Order Constraints can be found in the AI Explainability 360 toolbox, which is part of the IBM Research Trusted AI library (Arya et al., 2019) at https://github.com/Trusted-AI/AIX360. |
| Open Datasets | Yes | We use an annotated dataset from the enhanced Stanford Natural Language Inference (e-SNLI) (Camburu et al., 2018; Swanson et al., 2020) ... We use source images from the SUN dataset (Xiao et al., 2010; Yu et al., 2016), and target images from Wiki Art (Tan et al., 2016). |
| Dataset Splits | Yes | We used sizes of (100K, 10K, 5K) for train, validation, and test, respectively. |
| Hardware Specification | Yes | Each computation run of Alg. 1 is measured on a single Intel x86 64 bit Xeon 2MHZ with 12GB memory per core. ... Classifier training was performed on a multi-core Ubuntu virtual machine and on a n Vidia Tesla P100-PCIE-16GB GPUs. |
| Software Dependencies | No | The paper mentions 'python-based algorithms', 'scipy.optimize', 'numpy', and 'C++-based cvxpy', but does not provide specific version numbers for these software components, which are crucial for reproducibility. |
| Experiment Setup | Yes | The thresholds that constrain T (k3, τ1, τ2) are set to (τ1, τ2) = (.5, .5). ... The iterations are set to terminate at 1e4 rounds or a max projection error of 1e-4, and these settings achieve an average functional approximation of 0.51% error (within .19). We use penalty ρ = 1.0. |