Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion
Authors: Kohei Miyaguchi, Takayuki Katsuki, Akira Koseki, Toshiya Iwamori
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have also empirically confirmed the effectiveness of the proposed method through numerical experiments, employing the variational autoencoders (VAEs) (Kingma & Welling, 2013) as the base generative model. |
| Researcher Affiliation | Industry | Kohei Miyaguchi, Takayuki Katsuki, Akira Koseki & Toshiya Iwamori IBM Research Tokyo miyaguchi@ibm.com, {kats,akoseki,iwamori}@jp.ibm.com |
| Pseudocode | Yes | Algorithm 1 Variational Inference for DIG (v DIG) |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available, nor does it include a link to a code repository. |
| Open Datasets | Yes | All the datasets used in the experiments are taken from UCI Machine Learning Repository. AQ-CO, AQ-NMHC and AQ-NOx are taken from the Air Quality dataset (De Vito et al., 2008)... Year Pred is a part of the Year Prediciton MSD dataset (Bertin-Mahieux, 2011)... Boston and Diabetes are respectively taken from the dataset of the same names. |
| Dataset Splits | Yes | We randomly hold 20% of the training split out for validation. Algorithms receive as the input only the remaining 80%. |
| Hardware Specification | Yes | CPU RAM GPU Py Torch Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz 64GB NVIDIA TITAN X 1.9.0 |
| Software Dependencies | Yes | Py Torch 1.9.0; Iterative Imputer from scikit-learn (ver. 0.24.2); Ada Belief optimizer (Zhuang et al., 2020) |
| Experiment Setup | Yes | The size of minibatch is always taken to be 512. In particular, for sampling-based methods (i.e., VAE, VAE*, CVAE, DVAE, DVAE*), the stochastic gradient is computed with the reparametrization trick. We iterate the loop 2000 times for each configuration. We choose α = 2 for the parameter of the R enyi divergence. |