# Explanations for Monotonic Classifiers

@inproceedings{MarquesSilva2021ExplanationsFM, title={Explanations for Monotonic Classifiers}, author={Joao Marques-Silva and Thomas S Gerspacher and Martin Cooper and Alexey Ignatiev and Nina Narodytska}, booktitle={ICML}, year={2021} }

In many classification tasks there is a requirement of monotonicity. Concretely, if all else remains constant, increasing (resp. decreasing) the value of one or more features must not decrease (resp. increase) the value of the prediction. Despite comprehensive efforts on learning monotonic classifiers, dedicated approaches for explaining monotonic classifiers are scarce and classifier-specific. This paper describes novel algorithms for the computation of one formal explanation of a (black-box…

## 4 Citations

On the Tractability of Explaining Decisions of Classifiers

- Computer ScienceCP
- 2021

This work investigates the computational complexity of providing a formally-correct and minimal explanation of a decision taken by a classifier and shows that tractable classes coincide for abductive and contrastive explanations in the constrained or unconstrained settings.

On Explaining Random Forests with SAT

- Computer ScienceIJCAI
- 2021

The paper proposes a propositional encoding for computing explanations of RFs, thus enabling finding PI-explanations with a SAT solver, and demonstrates that the proposed SAT-based approach significantly outperforms existing heuristic approaches.

$L_p$ Isotonic Regression Algorithms Using an $L_0$ Approach

- Computer Science
- 2021

For weighted points in d-dimensional space with coordinate-wise ordering, d ≥ 3, L0, L1 and L2 regressions can be found in only o(n 3 2 log n logU) time, improving on the previous best of Θ(n2 log n), and for unweighted points the time is O(n 4).

Towards Axiomatic, Hierarchical, and Symbolic Explanation for Deep Models

- Computer ScienceArXiv
- 2021

A hierarchical and symbolic And-Or graph is proposed to objectively explain the internal logic encoded by a well-trained deep model for inference to define the objectiveness of an explainer model in game theory.

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