Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Particle Semi-Implicit Variational Inference
Authors: Jen Ning Lim, Adam Johansen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results demonstrate that PVI performs favourably compared to other SIVI methods across various tasks. In this section, we compare PVI against other semi-implicit VI methods. |
| Researcher Affiliation | Academia | Jen Ning Lim University of Warwick Coventry, United Kingdom EMAIL Adam M. Johansen University of Warwick Coventry, United Kingdom EMAIL |
| Pseudocode | Yes | Algorithm 1 Particle Variational Inference (PVI) |
| Open Source Code | Yes | The code is available at https://github.com/jenninglim/pvi. |
| Open Datasets | Yes | We consider a Bayesian logistic regression problem on the waveform dataset (Breiman and Stone, 1984). Concrete (Yeh, 2007) Protein (Rana, 2013) Yacht (Gerritsma et al., 2013) |
| Dataset Splits | No | The paper does not explicitly state validation dataset splits for its experiments. |
| Hardware Specification | Yes | The code was written in JAX (Bradbury et al., 2018) and executed on a NVIDIA Ge Force RTX 4090. |
| Software Dependencies | No | The code was written in JAX (Bradbury et al., 2018). (No version number for JAX or other specific libraries). |
| Experiment Setup | Yes | PVI. We use M = 100, λθ = 0, λr = 10^-8, hx = 10^-2, hθ = 10^-4, Ψθ we use the RMSProp preconditoner, Ψr = Idz, and L = 250. |