Interpretable Regional Descriptors: Hyperbox-Based Local Explanations

• URL: http://arxiv.org/abs/2305.02780v1
• Date: Thu, 4 May 2023 12:26:07 GMT
• Title: Interpretable Regional Descriptors: Hyperbox-Based Local Explanations
• Authors: Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann
• Abstract summary: This work introduces interpretable regional descriptors, or IRDs, for local, model-agnostic interpretations. IRDs are hyperboxes that describe how an observation's feature values can be changed without affecting its prediction.
• Score: 0.7349727826230864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces interpretable regional descriptors, or IRDs, for local, model-agnostic interpretations. IRDs are hyperboxes that describe how an observation's feature values can be changed without affecting its prediction. They justify a prediction by providing a set of "even if" arguments (semi-factual explanations), and they indicate which features affect a prediction and whether pointwise biases or implausibilities exist. A concrete use case shows that this is valuable for both machine learning modelers and persons subject to a decision. We formalize the search for IRDs as an optimization problem and introduce a unifying framework for computing IRDs that covers desiderata, initialization techniques, and a post-processing method. We show how existing hyperbox methods can be adapted to fit into this unified framework. A benchmark study compares the methods based on several quality measures and identifies two strategies to improve IRDs.

Related papers

• Comprehensive OOD Detection Improvements [46.46252643210326]
  Out-of-distribution (OOD) detection methods have been created for this task. We employ dimensionality reduction on feature embeddings in representation-based methods for both time speedups and improved performance. We demonstrate the effectiveness of our methods on the OpenOODv1.5 benchmark framework.
  arXiv  Detail & Related papers  (2024-01-18T18:05:35Z)
• An Additive Instance-Wise Approach to Multi-class Model Interpretation [53.87578024052922]
  Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. Existing methods mainly focus on selecting explanatory input features, which follow either locally additive or instance-wise approaches. This work exploits the strengths of both methods and proposes a global framework for learning local explanations simultaneously for multiple target classes.
  arXiv  Detail & Related papers  (2022-07-07T06:50:27Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• Benchmarking Deep Models for Salient Object Detection [67.07247772280212]
  We construct a general SALient Object Detection (SALOD) benchmark to conduct a comprehensive comparison among several representative SOD methods. In the above experiments, we find that existing loss functions usually specialized in some metrics but reported inferior results on the others. We propose a novel Edge-Aware (EA) loss that promotes deep networks to learn more discriminative features by integrating both pixel- and image-level supervision signals.
  arXiv  Detail & Related papers  (2022-02-07T03:43:16Z)
• Evaluating Explanations for Reading Comprehension with Realistic Counterfactuals [26.641834518599303]
  We propose a methodology to evaluate explanations for machine reading comprehension tasks. An explanation should allow us to understand the RC model's high-level behavior with respect to a set of realistic counterfactual input scenarios. Our analysis suggests that pairwise explanation techniques are better suited to RC than token-level attributions.
  arXiv  Detail & Related papers  (2021-04-09T17:55:21Z)
• Interpretable Multi-dataset Evaluation for Named Entity Recognition [110.64368106131062]
  We present a general methodology for interpretable evaluation for the named entity recognition (NER) task. The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them. By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area.
  arXiv  Detail & Related papers  (2020-11-13T10:53:27Z)
• Toward Scalable and Unified Example-based Explanation and Outlier Detection [128.23117182137418]
  We argue for a broader adoption of prototype-based student networks capable of providing an example-based explanation for their prediction. We show that our prototype-based networks beyond similarity kernels deliver meaningful explanations and promising outlier detection results without compromising classification accuracy.
  arXiv  Detail & Related papers  (2020-11-11T05:58:17Z)
• MAIRE -- A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers [5.02231401459109]
  The paper introduces a novel framework for extracting model-agnostic human interpretable rules to explain a classifier's output. The framework is model agnostic, can be applied to any arbitrary classifier, and all types of attributes (including continuous, ordered, and unordered discrete)
  arXiv  Detail & Related papers  (2020-11-03T06:53:06Z)
• A Diagnostic Study of Explainability Techniques for Text Classification [52.879658637466605]
  We develop a list of diagnostic properties for evaluating existing explainability techniques. We compare the saliency scores assigned by the explainability techniques with human annotations of salient input regions to find relations between a model's performance and the agreement of its rationales with human ones.
  arXiv  Detail & Related papers  (2020-09-25T12:01:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.