11 research outputs found
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules
The wide adoption of machine learning in the critical domains such as medical
diagnosis, law, education had propelled the need for interpretable techniques
due to the need for end users to understand the reasoning behind decisions due
to learning systems. The computational intractability of interpretable learning
led practitioners to design heuristic techniques, which fail to provide sound
handles to tradeoff accuracy and interpretability.
Motivated by the success of MaxSAT solvers over the past decade, recently
MaxSAT-based approach, called MLIC, was proposed that seeks to reduce the
problem of learning interpretable rules expressed in Conjunctive Normal Form
(CNF) to a MaxSAT query. While MLIC was shown to achieve accuracy similar to
that of other state of the art black-box classifiers while generating small
interpretable CNF formulas, the runtime performance of MLIC is significantly
lagging and renders approach unusable in practice. In this context, authors
raised the question: Is it possible to achieve the best of both worlds, i.e., a
sound framework for interpretable learning that can take advantage of MaxSAT
solvers while scaling to real-world instances?
In this paper, we take a step towards answering the above question in
affirmation. We propose IMLI: an incremental approach to MaxSAT based framework
that achieves scalable runtime performance via partition-based training
methodology. Extensive experiments on benchmarks arising from UCI repository
demonstrate that IMLI achieves up to three orders of magnitude runtime
improvement without loss of accuracy and interpretability.Comment: 10 pages, published in the proceedings of AAAI/ACM Conference on AI,
Ethics, and Society (AIES 2019
Interpretable machine learning for genomics
High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state-of-the-art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
In this review, we examine the problem of designing interpretable and
explainable machine learning models. Interpretability and explainability lie at
the core of many machine learning and statistical applications in medicine,
economics, law, and natural sciences. Although interpretability and
explainability have escaped a clear universal definition, many techniques
motivated by these properties have been developed over the recent 30 years with
the focus currently shifting towards deep learning methods. In this review, we
emphasise the divide between interpretability and explainability and illustrate
these two different research directions with concrete examples of the
state-of-the-art. The review is intended for a general machine learning
audience with interest in exploring the problems of interpretation and
explanation beyond logistic regression or random forest variable importance.
This work is not an exhaustive literature survey, but rather a primer focusing
selectively on certain lines of research which the authors found interesting or
informative
Logic-Based Explainability in Machine Learning
The last decade witnessed an ever-increasing stream of successes in Machine
Learning (ML). These successes offer clear evidence that ML is bound to become
pervasive in a wide range of practical uses, including many that directly
affect humans. Unfortunately, the operation of the most successful ML models is
incomprehensible for human decision makers. As a result, the use of ML models,
especially in high-risk and safety-critical settings is not without concern. In
recent years, there have been efforts on devising approaches for explaining ML
models. Most of these efforts have focused on so-called model-agnostic
approaches. However, all model-agnostic and related approaches offer no
guarantees of rigor, hence being referred to as non-formal. For example, such
non-formal explanations can be consistent with different predictions, which
renders them useless in practice. This paper overviews the ongoing research
efforts on computing rigorous model-based explanations of ML models; these
being referred to as formal explanations. These efforts encompass a variety of
topics, that include the actual definitions of explanations, the
characterization of the complexity of computing explanations, the currently
best logical encodings for reasoning about different ML models, and also how to
make explanations interpretable for human decision makers, among others