
Locally Adaptive Label Smoothing for Predictive Churn
Training modern neural networks is an inherently noisy process that can ...
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Label Smoothed Embedding Hypothesis for OutofDistribution Detection
Detecting outofdistribution (OOD) examples is critical in many applica...
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Stochastic Bandits with Linear Constraints
We study a constrained contextual linear bandit setting, where the goal ...
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Faster DBSCAN via subsampled similarity queries
DBSCAN is a popular densitybased clustering algorithm. It computes the ...
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Learning the Truth From Only One Side of the Story
Learning under onesided feedback (i.e., where examples arrive in an onl...
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Deep kNN for Noisy Labels
Modern machine learning models are often trained on examples with noisy ...
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Robustness Guarantees for Mode Estimation with an Application to Bandits
Mode estimation is a classical problem in statistics with a wide range o...
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Groupbased Fair Learning Leads to Counterintuitive Predictions
A number of machine learning (ML) methods have been proposed recently to...
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Wasserstein Fair Classification
We propose an approach to fair classification that enforces independence...
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MinimumMargin Active Learning
We present a new active sampling method we call minmargin which trains ...
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Identifying and Correcting Label Bias in Machine Learning
Datasets often contain biases which unfairly disadvantage certain groups...
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DBSCAN++: Towards fast and scalable density clustering
DBSCAN is a classical densitybased clustering procedure which has had t...
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Optimization with NonDifferentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
We show that many machine learning goals, such as improved fairness metr...
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Training WellGeneralizing Classifiers for Fairness Metrics and Other DataDependent Constraints
Classifiers can be trained with datadependent constraints to satisfy fa...
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Interpretable Set Functions
We propose learning flexible but interpretable functions that aggregate ...
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To Trust Or Not To Trust A Classifier
Knowing when a classifier's prediction can be trusted is useful in many ...
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Quickshift++: Provably Good Initializations for SampleBased Mean Shift
We provide initial seedings to the Quick Shift clustering algorithm, whi...
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TwoPlayer Games for Efficient NonConvex Constrained Optimization
In recent years, constrained optimization has become increasingly releva...
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Nonparametric Stochastic Contextual Bandits
We analyze the Karmed bandit problem where the reward for each arm is a...
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On the Consistency of Quick Shift
Quick Shift is a popular modeseeking and clustering algorithm. We prese...
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Rates of Uniform Consistency for kNN Regression
We derive highprobability finitesample uniform rates of consistency fo...
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Density Level Set Estimation on Manifolds with DBSCAN
We show that DBSCAN can estimate the connected components of the λdensi...
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Modalset estimation with an application to clustering
We present a first procedure that can estimate  with statistical consi...
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Heinrich Jiang
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