Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
Authors: Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul
JMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically evaluate the validity of this ordering when the target distribution p is not Gaussian. [...] In section 6, we empirically investigate the validity of this ordering, on a variety of targets and data sets, when q is Gaussian but p is not. |
| Researcher Affiliation | Academia | Charles C. Margossian EMAIL Department of Statistics University of British Columbia Vancouver, BC, Canada Loucas Pillaud-Vivien EMAIL CERMICS Laboratory Ecole des Ponts Paris Tech Champs-sur-Marne, France Lawrence K. Saul EMAIL Center for Computational Mathematics Flatiron Institute New York, NY, USA |
| Pseudocode | No | The paper describes algorithms and derivations but does not present any structured pseudocode or algorithm blocks with explicit labels like "Algorithm" or "Pseudocode". |
| Open Source Code | Yes | Code to reproduce the experiments can be found at https://github.com/charlesm93/VI-ordering. |
| Open Datasets | Yes | We next experiment with target distributions from the inference gym, a curated library of diverse models for the study of inference algorithms (Sountsov et al., 2020). [...] German Credit. (n=25) A logistic regression applied to a credit data set (Dua and Graff, 2017) |
| Dataset Splits | No | The paper discusses various target distributions and models, but does not specify any training, validation, or test splits for the datasets used. |
| Hardware Specification | No | In addition, we parallelize many calculations on GPU, keeping run times short. This statement is too general and does not provide specific hardware models (e.g., NVIDIA A100, RTX 3090). |
| Software Dependencies | No | Each method is implemented in Python and uses the JAX library to calculate derivatives (Bradbury et al., 2018). Specific version numbers for Python or JAX are not provided. |
| Experiment Setup | Yes | Optimization is performed with the Adam optimizer (Kingma and Ba, 2015). As a baseline, we run the optimizer for 500 iterations, and at each iteration, we use B = 10, 000 draws from q (or p when using oracle samples) to estimate the relevant objective function and its gradient. This large number of draws is overkill for many problems, but it helps stabilize the optimization of α-divergences. In addition, we parallelize many calculations on GPU, keeping run times short. For certain targets, we find it necessary to fine-tune the optimizer in order to avoid numerical instability. We do so by adjusting the learning rate of Adam, the number B of Monte Carlo draws, and the number of iterations. [...] We find a constant learning rate λt =1 to work well for the experiments in Section 6. |