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..
On the Convergence of Black-Box Variational Inference
Authors: Kyurae Kim, Jisu Oh, Kaiwen Wu, Yian Ma, Jacob Gardner
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate this theoretical insight by comparing proximal SGD against other standard implementations of BBVI on large-scale Bayesian inference problems. In Section 5, we evaluate the utility of proximal SGD on large-scale Bayesian inference problems. |
| Researcher Affiliation | Academia | Kyurae Kim University of Pennsylvania EMAIL Jisu Oh North Carolina State University EMAIL Kaiwen Wu University of Pennsylvania EMAIL Yi-An Ma University of California, San Diego EMAIL Jacob R. Gardner University of Pennsylvania EMAIL |
| Pseudocode | Yes | Algorithm 1: Prox Gen-Adam for Black-Box Variational Inference |
| Open Source Code | No | The paper does not provide an explicit statement or link to its own open-source code for the methodology described. |
| Open Datasets | Yes | LME-election Linear Mixed Effects 1988 U.S. presidential election (Gelman & Hill, 2007); KEGG-undirected (Shannon et al., 2003); million songs (Bertin-Mahieux et al., 2011); The dataset was obtained from Posterior DB (Magnusson et al., 2022). |
| Dataset Splits | No | The paper mentions batch sizes and Monte Carlo samples, but does not provide specific training/validation/test dataset splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | Yes | Table 1: Computational Resources: System Topology 2 nodes with 2 sockets each with 24 logical threads (total 48 threads) Processor 1 Intel Xeon Silver 4310, 2.1 GHz (maximum 3.3 GHz) per socket Cache 1.1 Mi B L1, 30 Mi B L2, and 36 Mi B L3 Memory 250 Gi B RAM Accelerator 1 NVIDIA RTX A5000 per node, 2 GHZ, 24GB RAM |
| Software Dependencies | No | The paper mentions 'Turing (Ge et al., 2018)' and 'Adam (Kingma & Ba, 2015)' but does not provide specific version numbers for these or other software dependencies used in the experiments. |
| Experiment Setup | Yes | We run all algorithms with a fixed stepsize... We implement doubly stochastic subsampling (Titsias & Lรกzaro-Gredilla, 2014) with a batch size of B= 100 (B= 500 for BT-tennis) with M= 10 Monte Carlo samples. ... The results shown used a base stepsize of ฮณ= 10^3, while the initial point was m0 = 0, C0 = I. |