Differentially Private Optimal Transport: Application to Domain Adaptation
Authors: Nam LeTien, Amaury Habrard, Marc Sebban
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform an extensive series of experiments on various benchmarks (Vis DA, Office-Home and Office-Caltech datasets) that demonstrates the efficiency of our method compared to non-private strategies. |
| Researcher Affiliation | Academia | 1Univ Lyon, UJM-Saint-Etienne, CNRS, Institut d Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, SAINT-ETIENNE, France {tien.le, amaury.habrard, marc.sebban}@univ-st-etienne.fr |
| Pseudocode | Yes | Algorithm 1 Differentially Private Optimal Transport. Algorithm 2 Differentially Private Domain Adaptation. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We evaluate our method on three domain adaptation benchmarks from the classical Office-Caltech dataset [Saenko et al., 2010] to the more recent and challenging Vis DA [Peng et al., 2017] and Office-Home [Venkateswara et al., 2017] datasets. |
| Dataset Splits | No | The paper discusses 'whole batch setting' and 'minibatch setting' for experiments but does not provide specific details on train/validation/test dataset splits, percentages, or absolute counts for reproducibility. It mentions 'minibatch of size 128' but not how the data is partitioned into splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processors, or memory used for running the experiments. |
| Software Dependencies | No | All methods are written in Keras [Chollet, 2015] with the same target model architecture (a 3-layer neural network) for fair comparison. The coupling matrices are computed using the POT library [Flamary and Courty, 2017]. For PATE and DPDA, we use the privacy accountant tool [Abadi et al., 2016]. However, specific version numbers for Keras, POT, or the privacy accountant tool are not provided. |
| Experiment Setup | Yes | For OTDA and our method DPDA, we set the hyper-parameters λe and λg of Eq. (3) to 0.01 and 0.1, respectively. In all benchmarks, we set the dimension of the subspace of our method ℓ= k /10 and the noise-ratio σ w = 1.1. For the privacy budget, we again follow the standard of [Abadi et al., 2016; Papernot et al., 2017] by setting δ = 1/1.2ns , ε = 2 for Vis DA and ε = 8 for the other datasets, except ε = 20 if the source is DSLR or Webcam in Office-Caltech since they have too few samples (150-200 in total). |