Cooperative Distribution Alignment via JSD Upper Bound
Authors: Wonwoong Cho, ZIYU GONG, David I. Inouye
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirical results on both simulated and real-world datasets to demonstrate the benefits of our approach. |
| Researcher Affiliation | Academia | Wonwoong Cho Purdue University cho436@purdue.edu Purdue University gong123@purdue.edu David I. Inouye Purdue University dinouye@purdue.edu Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN |
| Pseudocode | Yes | Algorithm 1 Training algorithm for AUB |
| Open Source Code | Yes | Code is available at https://github.com/inouye-lab/alignment-upper-bound. |
| Open Datasets | Yes | In both experiments, we used four UCI tabular datasets [29] (MINIBOONE, GAS, HEPMASS, and POWER), following the same preprocessing as the MAF paper [30]. ... We perform an image translation task on MNIST dataset4 [31] ... Domain Adaptation on USPS-MNIST dataset |
| Dataset Splits | Yes | Train, validation, and test sets are 80%, 10%, and 10% of the data respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions software components like 'Real NVP' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper mentions 'Implementation details are provided in ??' which suggests they are outside the main text. Algorithm 1 lists some general parameters like 'batch size; learning rate η; maximum epoch Emax' but no specific values are given in the main body of the paper. |