
Supervised PCA: A Multiobjective Approach
Methods for supervised principal component analysis (SPCA) aim to incorp...
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Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
Recent research has established sufficient conditions for finite mixture...
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Learning from Label Proportions: A Mutual Contamination Framework
Learning from label proportions (LLP) is a weakly supervised setting for...
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Calibrated Surrogate Losses for Adversarially Robust Classification
Adversarially robust classification seeks a classifier that is insensiti...
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Learning from Multiple Corrupted Sources, with Application to Learning from Label Proportions
We study binary classification in the setting where the learner is prese...
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PAC Reinforcement Learning without RealWorld Feedback
This work studies reinforcement learning in the SimtoReal setting, in ...
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A Generalization Error Bound for Multiclass Domain Generalization
Domain generalization is the problem of assigning labels to an unlabeled...
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Local inversionfree estimation of spatial Gaussian processes
Maximizing the likelihood has been widely used for estimating the unknow...
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Simple Regret Minimization for Contextual Bandits
There are two variants of the classical multiarmed bandit (MAB) problem...
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A Generalized NeymanPearson Criterion for Optimal Domain Adaptation
In the problem domain adaptation for binary classification, the learner ...
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Domain Generalization by Marginal Transfer Learning
Domain generalization is the problem of assigning class labels to an unl...
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DictionaryFree MRI PERK: Parameter Estimation via Regression with Kernels
This paper introduces a fast, general method for dictionaryfree paramet...
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Decontamination of Mutual Contamination Models
Many machine learning problems can be characterized by mutual contaminat...
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Consistent Kernel Density Estimation with NonVanishing Bandwidth
Consistency of the kernel density estimator requires that the kernel ban...
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Nonparametric Preference Completion
We consider the task of collaborative preference completion: given a poo...
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MultiTask Learning for Contextual Bandits
Contextual bandits are a form of multiarmed bandit in which the agent h...
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Adaptive Questionnaires for Direct Identification of Optimal Product Design
We consider the problem of identifying the most profitable product desig...
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Mixture Proportion Estimation via Kernel Embedding of Distributions
Mixture proportion estimation (MPE) is the problem of estimating the wei...
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A Mutual Contamination Analysis of Mixed Membership and Partial Label Models
Many machine learning problems can be characterized by mutual contaminat...
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On the consistency of inversionfree parameter estimation for Gaussian random fields
Gaussian random fields are a powerful tool for modeling environmental pr...
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Optimal change point detection in Gaussian processes
We study the problem of detecting a change in the mean of onedimensiona...
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Class Proportion Estimation with Application to Multiclass Anomaly Rejection
This work addresses two classification problems that fall under the head...
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Classification with Asymmetric Label Noise: Consistency and Maximal Denoising
In many realworld classification problems, the labels of training examp...
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Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks
The problem of active diagnosis arises in several applications such as d...
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Calibrated Surrogate Losses for Classification with LabelDependent Costs
We present surrogate regret bounds for arbitrary surrogate losses in the...
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Groupbased Query Learning for rapid diagnosis in timecritical situations
In query learning, the goal is to identify an unknown object while minim...
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Clayton Scott
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Associate Professor Electrical Engineering and Computer Science, Associate Professor Statistics at University of Michigan since 2006, Career Award from the National Science Foundation. 2010, PhD in Electrical Engineering from Rice University in 2004.