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..
FasterRisk: Fast and Accurate Interpretable Risk Scores
Authors: Jiachang Liu, Chudi Zhong, Boxuan Li, Margo Seltzer, Cynthia Rudin
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we show that our proposed method is computationally fast and produces high-quality integer solutions. We experimentally focus on two questions: (1) How good is Faster Risk s solution quality compared to baselines? ( 4.1) (2) How fast is Faster Risk compared with the state-of-the-art? ( 4.2) |
| Researcher Affiliation | Academia | 1 Duke Univeristy 2 University of British Columbia |
| Pseudocode | Yes | Algorithm 1 Faster Risk(D,k,C,B, ,T,Nm) ! {(w+t, w+t0 , mt)}t |
| Open Source Code | Yes | Code Availability: Implementations of Faster Risk discussed in this paper are available at https://github.com/ jiachangliu/Faster Risk. |
| Open Datasets | Yes | The datasets used are mammo [15], adult, bank, mushroom, COMPAS [21], FICO [17], and Netherlands. All datasets are publicly available from the UCI Machine Learning Repository [20], except COMPAS which is from ProPublica [21], and FICO which is from the FICO Explainable Machine Learning Challenge [17]. |
| Dataset Splits | Yes | For each dataset, we perform 5-fold cross validation and report training and test AUC. |
| Hardware Specification | Yes | Our experiments are run on a Linux machine with 16 Intel Xeon E5-2630 v4 CPUs and 2 NVIDIA Titan X Pascal GPUs. |
| Software Dependencies | Yes | All experiments are conducted on Python 3.8.10. We use numpy 1.21.5 and scipy 1.7.3 for numerical operations, scikit-learn 1.0.2 for baselines, and Gurobi 9.5.1 as the MIP solver. |
| Experiment Setup | Yes | Input: dataset D (consisting of feature matrix X 2 Rn p and labels y 2 Rn), sparsity constraint k, coefficient constraint C = 5, beam search size B = 10, tolerance level = 0.3, number of attempts T = 50, number of multipliers to try Nm = 20. |