Parameter Estimation in DAGs from Incomplete Data via Optimal Transport
Authors: Vy Vo, Trung Le, Long Tung Vuong, He Zhao, Edwin V. Bonilla, Dinh Phung
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further provide empirical evidence demonstrating the versatility of our method on various graphical structures, where OTP-DAG is shown to successfully recover the ground-truth parameters and achieve comparable or better performance than competing methods across a range of downstream applications. 5. Applications In this section, we illustrate the practical applications of the OTP-DAG algorithm. We consider various directed probabilistic models with different types of latent variables (continuous and discrete) and for different types of data (texts, images, and time series). In all tables, we report the average results over 5 random initializations and the best/second-best ones are bold/underlined. , indicate higher/lower performance is better, respectively. |
| Researcher Affiliation | Collaboration | 1Monash University, Australia 2CSIRO s Data61, Australia 3Vin AI Research, Vietnam. |
| Pseudocode | Yes | Algorithm 1 OTP-DAG Algorithm |
| Open Source Code | Yes | 1Our code is published at https://github.com/ is Vy08/OTP. |
| Open Datasets | Yes | We here use OTPDAG to infer the topics of 3 real-world datasets: 20 News Group3, BBC News (Greene & Cunningham, 2006) and DBLP4. We experiment with images in this application and compare OTP-DAG with VQ-VAE on CIFAR105, MNIST (Le Cun et al., 1998), SVHN (Netzer et al., 2011) and CELEBA datasets (Liu et al., 2015). |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits, only mentioning dataset generation and number of training epochs. |
| Hardware Specification | Yes | All models are run on 4 RTX 6000 GPU cores using Adam optimizer with a fixed learning rate of 1e 3. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and implies the use of 'PythonOT' and neural network architectures (LSTM, perceptron), but does not provide specific version numbers for programming languages or libraries. |
| Experiment Setup | Yes | The hyperparameters are: D = M = 64, K = 512, η = 1e 3, ηr = 1.0, batch size of 32 and 100 training epochs. |