Semi-Parametric Uncertainty Bounds for Binary Classification
The paper studies binary classification and aims at estimating the underlying regression function which is the conditional expectation of the class labels given the inputs. The regression function is the key component of the Bayes optimal classifier, moreover, besides providing optimal predictions, it can also assess the risk of misclassification. We aim at building non-asymptotic confidence regions for the regression function and suggest three kernel-based semi-parametric resampling methods. We prove that all of them guarantee regions with exact coverage probabilities and they are strongly consistent.
Code (0)
등록된 구현이 없습니다.
Tasks
Binary ClassificationClassificationGeneral ClassificationregressionSimilar Papers 제목 키워드 기반
Minimum Excess Risk in Bayesian Learning
We analyze the best achievable performance of Bayesian learning under generative models by defining and upper-bounding the minimum excess risk (MER): the gap between the minimum expected loss attainable by learning from …
Binary Classificationparameter estimationEfficient Analytic Uncertainty Quantification for Multi-Modal Regression
Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning. Existing UQ methods for regression tasks mainly operate under the assumption that the conditional label marginal satisfies sing…
Bayesian InferenceActive LearningSemi-parametric dynamic contextual pricing
Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual information describing the customer's histo…
Empirically Estimable Classification Bounds Based on a New Divergence Measure
Information divergence functions play a critical role in statistics and information theory. In this paper we show that a non-parametric f-divergence measure can be used to provide improved bounds on the minimum binary cl…
Binary ClassificationClassificationfeature selectionGeneral ClassificationPolicy Optimization Using Semi-parametric Models for Dynamic Pricing
In this paper, we study the contextual dynamic pricing problem where the market value of a product is linear in its observed features plus some market noise. Products are sold one at a time, and only a binary response in…
Decision Making