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 [1].
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. |