
ExKMC: Expanding Explainable kMeans Clustering
Despite the popularity of explainable AI, there is limited work on effec...
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NearlyTight and Oblivious Algorithms for Explainable Clustering
We study the problem of explainable clustering in the setting first form...
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Meta Decision Trees for Explainable Recommendation Systems
We tackle the problem of building explainable recommendation systems tha...
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NearOptimal Explainable kMeans for All Dimensions
Many clustering algorithms are guided by certain cost functions such as ...
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Nearoptimal Algorithms for Explainable kMedians and kMeans
We consider the problem of explainable kmedians and kmeans introduced ...
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Explainable kmeans. Don't be greedy, plant bigger trees!
We provide a new bicriteria Õ(log^2 k) competitive algorithm for explai...
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Explainable Deep Classification Models for Domain Generalization
Conventionally, AI models are thought to trade off explainability for lo...
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On the price of explainability for some clustering problems
The price of explainability for a clustering task can be defined as the unavoidable loss,in terms of the objective function, if we force the final partition to be explainable. Here, we study this price for the following clustering problems: kmeans, kmedians, kcenters and maximumspacing. We provide upper and lower bounds for a natural model where explainability is achieved via decision trees. For the kmeans and kmedians problems our upper bounds improve those obtained by [Moshkovitz et. al, ICML 20] for low dimensions. Another contribution is a simple and efficient algorithm for building explainable clusterings for the kmeans problem. We provide empirical evidence that its performance is better than the current state of the art for decisiontree based explainable clustering.
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