On Computing Probabilistic Explanations for Decision Trees
Authors: Marcelo Arenas, Pablo Barceló, Miguel Romero Orth, Bernardo Subercaseaux
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In spite of the intractability results we obtain in the paper, we show experimentally that our problems can be solved over practical instances by using SAT solvers. The appendix describes experimentation both over synthetic datasets and standard datasets (MNIST) and reports empirical results. |
| Researcher Affiliation | Academia | 1 Department of Computer Science, PUC Chile 2 Institute for Mathematical and Computational Engineering, PUC Chile 3 Faculty of Engineering and Science, UAI Chile 4 Millenium Institute for Foundational Research on Data, Chile 5 CENIA Chile 6 Carnegie Mellon University, Pittsburgh, USA |
| Pseudocode | Yes | Algorithm 1: Minimal Sufficient Reason Input: Decision tree T and instance x, both of dimension n Output: A minimal sufficient reason y for x under T. 2 while true do 3 reduced false 4 for i {1, . . . , n} do 7 if Check Sufficient Reason(T, ˆy, x) then 9 reduced true 11 if ( reduced) or |y| = n then 12 return y |
| Open Source Code | Yes | The appendix describes experimentation both over synthetic datasets and standard datasets (MNIST) and reports empirical results. A recent report by Izza et al. (2022) also uses automated reasoning for this problem, although through an SMT solver. Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | The appendix describes experimentation both over synthetic datasets and standard datasets (MNIST) and reports empirical results. L. Deng. The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, 29(6):141 142, 2012. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] |
| Software Dependencies | No | No specific version numbers for software dependencies are provided in the main paper text. |
| Experiment Setup | Yes | In order to encode that Prz[T(z) = T(x) | z COMP(y)] δ, we will directly encode that |{z COMP(y) | T(z) = T(x)}| δ2|y| , where we assume for simplicity that the ceiling can be take safely, in order to have a value we can represent by an integer. This assumption is not crucial. As before, we will have fi variables representing whether y[i] = or not, and enforce that Pn i=1 fi k. Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] |