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..
Variational Inference in Mixed Probabilistic Submodular Models
Authors: Josip Djolonga, Sebastian Tschiatschek, Andreas Krause
NeurIPS 2016 | Venue PDF | 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 EMAIL |
| 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). |