Open AccessPosted Content
Classification by Set Cover: The Prototype Vector Machine
Jacob Bien,Robert Tibshirani +1 more
TLDR
This work introduces a new nearest-prototype classifier, the prototype vector machine (PVM), which arises from a combinatorial optimization problem which is cast as a variant of the set cover problem and proposes two algorithms for approximating its solution.Abstract:
We introduce a new nearest-prototype classifier, the prototype vector machine (PVM). It arises from a combinatorial optimization problem which we cast as a variant of the set cover problem. We propose two algorithms for approximating its solution. The PVM selects a relatively small number of representative points which can then be used for classification. It contains 1-NN as a special case. The method is compatible with any dissimilarity measure, making it amenable to situations in which the data are not embedded in an underlying feature space or in which using a non-Euclidean metric is desirable. Indeed, we demonstrate on the much studied ZIP code data how the PVM can reap the benefits of a problem-specific metric. In this example, the PVM outperforms the highly successful 1-NN with tangent distance, and does so retaining fewer than half of the data points. This example highlights the strengths of the PVM in yielding a low-error, highly interpretable model. Additionally, we apply the PVM to a protein classification problem in which a kernel-based distance is used.read more
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Proceedings ArticleDOI
Interpretable Decision Sets: A Joint Framework for Description and Prediction
TL;DR: This work proposes interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable, and provides a new approach to interpretable machine learning that balances accuracy, interpretability, and computational efficiency.
Journal ArticleDOI
A Survey on the Explainability of Supervised Machine Learning
Nadia Burkart,Marco F. Huber +1 more
TL;DR: This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning, and a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions.
Journal Article
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Satya Sai Krishna,Tessa Han,Alex Gu,Javin Pombra,Shahin Jabbari,Steven C. Wu,Himabindu Lakkaraju +6 more
TL;DR: This work introduces and study the disagreement problem in explainable machine learning, formalizes the notion of disagreement between explanations, and analyzes how often such disagreements occur in practice, and how do practitioners resolve these disagreements.
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Learning Cost-Effective and Interpretable Treatment Regimes
TL;DR: This work proposes a novel objective to construct a decision list which maximizes outcomes for the population, and minimizes overall costs, and employs a variant of the Upper Confidence Bound for Trees strategy which leverages customized checks for pruning the search space effectively.
Proceedings ArticleDOI
OpenXAI: Towards a Transparent Evaluation of Model Explanations
Chirag Agarwal,Eshika Saxena,Satya Sai Krishna,Martin Pawelczyk,Nari Johnson,Isha Puri,Marinka Zitnik,Himabindu Lakkaraju +7 more
TL;DR: Overall, OpenXAI provides an automated end-to-end pipeline that not only simplifies and standardizes the evaluation of post hoc explanation methods, but also promotes transparency and reproducibility in benchmarking these methods.
References
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
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Self-Organizing Maps
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
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Pattern recognition and neural networks
Brian D. Ripley,N. L. Hjort +1 more
TL;DR: Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks in this self-contained account.
Proceedings Article
Distance Metric Learning for Large Margin Nearest Neighbor Classification
TL;DR: In this article, a Mahanalobis distance metric for k-NN classification is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin.
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Approximation Algorithms
TL;DR: Covering the basic techniques used in the latest research work, the author consolidates progress made so far, including some very recent and promising results, and conveys the beauty and excitement of work in the field.