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..
Improving Local Identifiability in Probabilistic Box Embeddings
Authors: Shib Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our model to a variety of synthetic problems and demonstrate that the new objective function correctly solves cases that are not solvable by any previous approaches to ο¬tting box models. In real-world data, we demonstrate improved performance on a Word Net completion task and a Movie Lens density estimation task. |
| Researcher Affiliation | Academia | Shib Sankar Dasgupta Department of Computer Science University of Massachusetts, Amherst EMAIL |
| Pseudocode | Yes | For ease of understanding, we provide a concrete instantiation of our algorithm for learning from pairwise conditional probabilities, incorporating all approximations, in Appendix D. |
| Open Source Code | Yes | Source code and data for the experiments are available at https://github.com/iesl/ gumbel-box-embeddings. |
| Open Datasets | Yes | Most of the recent geometric embeddings methods [26, 13, 5, 10, 18] use Word Net [14] to demonstrate the capability of their model to naturally represent real-world hierarchical data. |
| Dataset Splits | No | The paper discusses training and testing, but does not explicitly provide details about a separate validation split (e.g., percentages, sample counts, or cross-validation scheme) in the main text. |
| Hardware Specification | No | The paper does not specify the hardware used for running experiments (e.g., specific GPU or CPU models, memory details). |
| Software Dependencies | No | We use Weights & Biases package [2] to manage our experiments. The paper cites the software but does not specify its version number or other software dependencies with versions. |
| Experiment Setup | No | We train the embeddings via gradient descent, using KL-divergence loss. The paper mentions this general training approach and refers to appendices for 'full training details', but concrete hyperparameters are not detailed in the main text. |