Variational Inference in Mixed Probabilistic Submodular Models
Authors: Josip Djolonga, Sebastian Tschiatschek, Andreas Krause
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our approach in a large set of experiments, where our model allows reasoning about preferences over sets of items with complements and substitutes. 5 Experiments |
| Researcher Affiliation | Academia | Josip Djolonga Sebastian Tschiatschek Andreas Krause Department of Computer Science, ETH Z urich {josipd,tschiats,krausea}@inf.ethz.ch |
| Pseudocode | Yes | Algorithm 1 Modular upper bound for M -concave functions |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | Dataset. We use the Amazon baby registry dataset [21] for evaluating our proposed variational inference scheme. |
| Dataset Splits | Yes | We then used the trained models for the product recommendation task from the previous section and estimated the performance metrics using 10-fold cross-validation. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | We used stochastic gradient descent for optimizing the NCE objective, created 200.000 noise samples from the modular model and made 100 passes through the data and noise samples. We used K = 10, L = 10 dimensions for the weights (if applicable for the corresponding model). |