Challenges and Opportunities in High Dimensional Variational Inference
Authors: Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael R. Andersen, Jonathan Huggins, Aki Vehtari
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our framework through an extensive empirical study using simulated data and many commonly used real datasets with both Gaussian and non-Gaussian target distributions. |
| Researcher Affiliation | Collaboration | Akash Kumar Dhaka* Aalto University, Silo AI akash.dhaka@aalto.fi |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The code for the experiments will be made available after acceptance using MIT license. |
| Open Datasets | Yes | We compare variational approximations for models and datasets from posteriordb in terms of accuracy of the estimated moments and predictive likelihood. https://github.com/stan-dev/posteriordb |
| Dataset Splits | No | We used an 80/20 training/test split on all datasets to compute the predictive likelihoods. |
| Hardware Specification | No | The experiments were carried on a laptop and an internal cluster with only CPU capability. |
| Software Dependencies | Yes | We use Stan [26] for model construction. [26] Stan Development Team. Stan modeling language users guide and reference manual. 2.26, 2020. URL https://mc-stan.org. |
| Experiment Setup | Yes | For stochastic optimization we use RMSProp with initial step size of 10 3 run for either Tmax iterations or until convergence was detected using a modiļ¬ed version of the algorithm by Dhaka et al. [6]. For the exclusive KL we use 10 draws for gradient estimation per iteration, while for the other divergences we use 200 draws, and a warm start at the solution of the exclusive KL. We use Tmax = 10,000. |