Explainable AI using expressive Boolean formulas

• URL: http://arxiv.org/abs/2306.03976v1
• Date: Tue, 6 Jun 2023 19:18:46 GMT
• Title: Explainable AI using expressive Boolean formulas
• Authors: Gili Rosenberg, J. Kyle Brubaker, Martin J. A. Schuetz, Grant Salton, Zhihuai Zhu, Elton Yechao Zhu, Serdar Kad{\i}o\u{g}lu, Sima E. Borujeni, Helmut G. Katzgraber
• Abstract summary: We implement an interpretable machine classification model for Explainable AI (XAI) based on expressive Boolean formulas. Potential applications include credit scoring and diagnosis of medical conditions.
• Score: 0.6323908398583082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose and implement an interpretable machine learning classification model for Explainable AI (XAI) based on expressive Boolean formulas. Potential applications include credit scoring and diagnosis of medical conditions. The Boolean formula defines a rule with tunable complexity (or interpretability), according to which input data are classified. Such a formula can include any operator that can be applied to one or more Boolean variables, thus providing higher expressivity compared to more rigid rule-based and tree-based approaches. The classifier is trained using native local optimization techniques, efficiently searching the space of feasible formulas. Shallow rules can be determined by fast Integer Linear Programming (ILP) or Quadratic Unconstrained Binary Optimization (QUBO) solvers, potentially powered by special purpose hardware or quantum devices. We combine the expressivity and efficiency of the native local optimizer with the fast operation of these devices by executing non-local moves that optimize over subtrees of the full Boolean formula. We provide extensive numerical benchmarking results featuring several baselines on well-known public datasets. Based on the results, we find that the native local rule classifier is generally competitive with the other classifiers. The addition of non-local moves achieves similar results with fewer iterations, and therefore using specialized or quantum hardware could lead to a speedup by fast proposal of non-local moves.

Related papers

• Training Neural Networks as Recognizers of Formal Languages [87.06906286950438]
  Formal language theory pertains specifically to recognizers. It is common to instead use proxy tasks that are similar in only an informal sense. We correct this mismatch by training and evaluating neural networks directly as binary classifiers of strings.
  arXiv  Detail & Related papers  (2024-11-11T16:33:25Z)
• Globally Interpretable Classifiers via Boolean Formulas with Dynamic Propositions [4.195816579137846]
  Our method is implemented using Answer Set Programming. We investigate seven datasets and compare our results to ones obtainable by state-of-the-art classifiers. The advantage of our classifiers in all cases is that they are very short and immediately human intelligible as opposed to the black-box nature of the reference methods.
  arXiv  Detail & Related papers  (2024-06-03T08:46:17Z)
• Bisimulation Learning [55.859538562698496]
  We compute finite bisimulations of state transition systems with large, possibly infinite state space. Our technique yields faster verification results than alternative state-of-the-art tools in practice.
  arXiv  Detail & Related papers  (2024-05-24T17:11:27Z)
• A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation [121.0693322732454]
  Contrastive Language-Image Pretraining (CLIP) has gained popularity for its remarkable zero-shot capacity. Recent research has focused on developing efficient fine-tuning methods to enhance CLIP's performance in downstream tasks. We revisit a classical algorithm, Gaussian Discriminant Analysis (GDA), and apply it to the downstream classification of CLIP.
  arXiv  Detail & Related papers  (2024-02-06T15:45:27Z)
• Obtaining Explainable Classification Models using Distributionally Robust Optimization [12.511155426574563]
  We study generalized linear models constructed using sets of feature value rules. An inherent trade-off exists between rule set sparsity and its prediction accuracy. We propose a new formulation to learn an ensemble of rule sets that simultaneously addresses these competing factors.
  arXiv  Detail & Related papers  (2023-11-03T15:45:34Z)
• Efficient distributed representations with linear-time attention scores normalization [3.8673630752805437]
  We propose a linear-time approximation of the attention score normalization constants for embedding vectors with bounded norms. The accuracy of our estimation formula surpasses competing kernel methods by even orders of magnitude. The proposed algorithm is highly interpretable and easily adapted to an arbitrary embedding problem.
  arXiv  Detail & Related papers  (2023-03-30T15:48:26Z)
• A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks [65.34977803841007]
  Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. We show how by simply changing the temporal scheduling of the update rule for the synaptic weights leads to an algorithm that is much more efficient and stable than the original one.
  arXiv  Detail & Related papers  (2022-11-16T00:11:04Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Efficient Learning of Interpretable Classification Rules [34.27987659227838]
  This paper contributes an interpretable learning framework IMLI, that is based on maximum satisfiability (MaxSAT) for classification rules expressible in proposition logic. In our experiments, IMLI achieves the best balance among prediction accuracy, interpretability, and scalability.
  arXiv  Detail & Related papers  (2022-05-14T00:36:38Z)
• Convolutional Sparse Coding Fast Approximation with Application to Seismic Reflectivity Estimation [9.005280130480308]
  We propose a speed-up upgraded version of the classic iterative thresholding algorithm, that produces a good approximation of the convolutional sparse code within 2-5 iterations. The performance of the proposed solution is demonstrated via the seismic inversion problem in both synthetic and real data scenarios.
  arXiv  Detail & Related papers  (2021-06-29T12:19:07Z)
• Predictive Coding Approximates Backprop along Arbitrary Computation Graphs [68.8204255655161]
  We develop a strategy to translate core machine learning architectures into their predictive coding equivalents. Our models perform equivalently to backprop on challenging machine learning benchmarks. Our method raises the potential that standard machine learning algorithms could in principle be directly implemented in neural circuitry.
  arXiv  Detail & Related papers  (2020-06-07T15:35:47Z)

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.