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..
Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields
Authors: Yue Zhang, Arti Ramesh
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the ability of the models to learn semantically meaningful structures that also achieve better prediction performance when compared with a greedy search algorithm, a path-based algorithm, and manually deο¬ned clauses on two computational social science applications: i) modeling recovery in alcohol use disorder, and ii) detecting bullying. |
| Researcher Affiliation | Academia | Yue Zhang and Arti Ramesh SUNY Binghamton EMAIL |
| Pseudocode | Yes | The description of A3SL is given in Algorithm 1 and the details are given in Algorithm 2. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for their methodology. |
| Open Datasets | Yes | To model recovery from AUD, we use the dataset in Zhang et al. [2018]. |
| Dataset Splits | Yes | Table 1 shows the 5-fold cross-validation results on predicting recovery and relapse of AA-attending users. |
| Hardware Specification | No | The paper mentions running 'multiple agents in parallel' and 'distributed implementation using multi-threading' but provides no specific details about the hardware (e.g., GPU/CPU models, memory) used for experiments. |
| Software Dependencies | No | The paper mentions using 'asynchronous advantage actor-critic (A3C)' but does not provide specific version numbers for any software dependencies, libraries, or programming languages used. |
| Experiment Setup | Yes | In our experiments, the value and the policy network both use a 3-layer feed-forward neural network architecture with tanh as the activation function. We set tmax = 6 and Tmax = 100, 000. |