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..
Teaching the Old Dog New Tricks: Supervised Learning with Constraints
Authors: Fabrizio Detassis, Michele Lombardi, Michela Milano3742-3749
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical Evaluation Here we describe our experimentation, which is designed around a few main questions: 1) How does the method work on a variety of constraints, tasks, and datasets? ... Our code and results are publicly available. |
| Researcher Affiliation | Academia | Fabrizio Detassis,1 Michele Lombardi, 1 Michela Milano 1, 2 1 DISI, University of Bologna 2 Alma Mater Research Institute for Human-Centered Artificial Intelligence |
| Pseudocode | Yes | Algorithm 1 MOVING TARGETS |
| Open Source Code | Yes | Our code and results are publicly available1. 1Code available at: github.com/fabdet/moving-targets |
| Open Datasets | Yes | We test our method on seven datasets from the UCI Machine Learning repository (Dua and Graff 2017) |
| Dataset Splits | Yes | For each experiment, we perform a 5-fold cross validation (with a fixed seed). Hence, the training set for each fold will include 80% of the data. |
| Hardware Specification | Yes | All our experiments are run on an Intel Core i7 laptop with 16GB RAM and no GPU acceleration |
| Software Dependencies | Yes | we use Cplex 12.8 to solve the master problems. The network is trained with 100 epochs of RMSProp in Keras/Tensorflow 2.0 (default parameters, batch size 64). We train this approach to convergence using the CVXPY 1.1 library (with default configuration). |
| Experiment Setup | Yes | The network is trained with 100 epochs of RMSProp in Keras/Tensorflow 2.0 (default parameters, batch size 64). Empirically, α = 1, β = 0.1 seems to works well and is used for all subsequent experiments. As a ML model, we use a fully-connected, feed-forward Neural Network (NN) with two hidden layers with 32-Rectifier Linear Units. |