
What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features
While discriminative classifiers often yield strong predictive performan...
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Strudel: Learning StructuredDecomposable Probabilistic Circuits
Probabilistic circuits (PCs) represent a probability distribution as a c...
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Joints in Random Forests
Decision Trees (DTs) and Random Forests (RFs) are powerful discriminativ...
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Handling Missing Data in Decision Trees: A Probabilistic Approach
Decision trees are a popular family of models due to their attractive pr...
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On the Tractability of SHAP Explanations
SHAP explanations are a popular featureattribution mechanism for explai...
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Efficient Computation of Expectations under Spanning Tree Distributions
We give a general framework for inference in spanning tree models. We pr...
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On Constraint Definability in Tractable Probabilistic Models
Incorporating constraints is a major concern in probabilistic machine le...
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On Tractable Computation of Expected Predictions
Computing expected predictions has many interesting applications in areas such as fairness, handling missing values, and data analysis. Unfortunately, computing expectations of a discriminative model with respect to a probability distribution defined by an arbitrary generative model has been proven to be hard in general. In fact, the task is intractable even for simple models such as logistic regression and a naive Bayes distribution. In this paper, we identify a pair of generative and discriminative models that enables tractable computation of expectations of the latter with respect to the former, as well as moments of any order, in case of regression. Specifically, we consider expressive probabilistic circuits with certain structural constraints that support tractable probabilistic inference. Moreover, we exploit the tractable computation of highorder moments to derive an algorithm to approximate the expectations, for classification scenarios in which exact computations are intractable. We evaluate the effectiveness of our exact and approximate algorithms in handling missing data during prediction time where they prove to be competitive to standard imputation techniques on a variety of datasets. Finally, we illustrate how expected prediction framework can be used to reason about the behaviour of discriminative models.
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