Correcting Predictions for Approximate Bayesian Inference
Authors: Tomasz Kuśmierczyk, Joseph Sakaya, Arto Klami4511-4518
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the approach empirically in several problems, confirming its potential. ... We conduct a series of experiments, using variational approximation as q(θ). We first compare the method against the alternative of calibrating the posterior inference to account for the loss for a matrix factorization model, and then demonstrate improved decisions for a sparse regression model and a multilevel model for cases with approximations. |
| Researcher Affiliation | Academia | Tomasz Ku smierczyk, Joseph Sakaya, Arto Klami Helsinki Institute for Information Technology HIIT Department of Computer Science, University of Helsinki {tomasz.kusmierczyk, joseph.sakaya, arto.klami}@helsinki.fi |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The code to reproduce our experiments is available online.1 1https://github.com/tkusmierczyk/correcting approximate bayesian predictions |
| Open Datasets | Yes | on a subset of last.fm data (Bertin-Mahieux et al. 2011). ... We use the radon data and the multi-level model... (Gelman and Hill 2006). ... We apply the model on the corn data (Chen and Martin 2009). |
| Dataset Splits | Yes | we split the data randomly into equally sized training and test set. ... randomly split into equally sized training and test subsets. |
| Hardware Specification | No | The paper mentions 'computational resources' in the acknowledgements but does not specify any particular hardware components like CPU or GPU models, or memory details. |
| Software Dependencies | No | The paper mentions 'probabilistic programming tools, such as Stan (Carpenter et al. 2017) and Edward (Tran et al. 2016)' and that a model is 'implemented using the publicly available Stan code', but it does not provide specific version numbers for these or other software dependencies used in the experiments. |
| Experiment Setup | Yes | In our experiments, we use a simple feed-forward network with 3 hidden layers (with 20, 20 and 10 nodes) with Re LU activation and Adam optimizer with learning rate = 0.01... ...we use L = 5 in our empirical experiments. ... we use S = 1000 in other experiments. ... we use B = 20 quantiles. |