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..
A Constraint-Based Approach to Learning and Explanation
Authors: Gabriele Ciravegna, Francesco Giannini, Stefano Melacci, Marco Maggini, Marco Gori3658-3665
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An experimental evaluation is provided to support the proposed approach, in which we also explore the regularization effects introduced by the proposed Information Based Learning of Constraint (IBLC) algorithm. |
| Researcher Affiliation | Academia | 1Department of Information Engineering (DIINFO), University of Florence, Florence, Italy gabriele.ciravegna@unifi.it 2Department of Information Engineering and Science (DIISM), University of Siena, Siena, Italy EMAIL |
| Pseudocode | Yes | Algorithm 1 Stage-based Optimization procedure (superscripts indicate the iteration number). |
| Open Source Code | Yes | All experiments are reproducible by using the code available at https://github.com/gabrieleciravegna/Information-based-Constrained-Learning |
| Open Datasets | Yes | The datasets can be downloaded at http://yann.lecun.com/ exdb/mnist/ and https://www.cs.stanford.edu/ roozbeh/pascal-parts/pascal-parts.html |
| Dataset Splits | Yes | Training, validation and test sets are composed of 20k, 5k, and 10k digits, respectively, taken from a (class-balanced) subset of the MNIST data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions the 'Adam optimizer' but does not specify version numbers for it or any other software libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | The model parameters have been cross-validated ranging them in the following grids: learning rate of the Adam optimizer {0.01, 0.001, 0.0001}, γψ {1e 2, 1e 3, 1e 4}, γf = 1e 6 (we used the squared norm of the weights to implement f ). We also considered different scaling factors {0.5, 1, 2} of the penalty term associated to the MI (Eq. 4), and a convex combination of the two entropy terms, modulated by a coefficient {0.25, 0.5, 0.75}. All experiments are based on 1000 epochs. For the stage-based optimization, the task function learning stage lasts 200 epochs, while the constraint learning stage lasts 50 epochs. |