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02/15/2022
Predicting on the Edge: Identifying Where a Larger Model Does Better
Much effort has been devoted to making large and more accurate models, b...
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06/29/2021
SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption
Self-supervised contrastive representation learning has proved incredibl...
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06/04/2021
Churn Reduction via Distillation
In real-world systems, models are frequently updated as more data become...
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06/04/2021
Active Covering
We analyze the problem of active covering, where the learner is given an...
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04/01/2021
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking
MeanShift is a popular mode-seeking clustering algorithm used in a wide ...
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02/09/2021
Locally Adaptive Label Smoothing for Predictive Churn
Training modern neural networks is an inherently noisy process that can ...
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02/09/2021
Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection
Detecting out-of-distribution (OOD) examples is critical in many applica...
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06/17/2020
Stochastic Bandits with Linear Constraints
We study a constrained contextual linear bandit setting, where the goal ...
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06/11/2020
Faster DBSCAN via subsampled similarity queries
DBSCAN is a popular density-based clustering algorithm. It computes the ...
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06/08/2020
Learning the Truth From Only One Side of the Story
Learning under one-sided feedback (i.e., where examples arrive in an onl...
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04/26/2020
Deep k-NN for Noisy Labels
Modern machine learning models are often trained on examples with noisy ...
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03/05/2020
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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10/04/2019
Group-based Fair Learning Leads to Counter-intuitive Predictions
A number of machine learning (ML) methods have been proposed recently to...
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07/28/2019
Wasserstein Fair Classification
We propose an approach to fair classification that enforces independence...
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05/31/2019
Minimum-Margin Active Learning
We present a new active sampling method we call min-margin which trains ...
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01/15/2019
Identifying and Correcting Label Bias in Machine Learning
Datasets often contain biases which unfairly disadvantage certain groups...
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10/31/2018
DBSCAN++: Towards fast and scalable density clustering
DBSCAN is a classical density-based clustering procedure which has had t...
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09/11/2018
Optimization with Non-Differentiable 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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06/29/2018
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Classifiers can be trained with data-dependent constraints to satisfy fa...
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05/31/2018
Interpretable Set Functions
We propose learning flexible but interpretable functions that aggregate ...
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05/30/2018
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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05/21/2018
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
We provide initial seedings to the Quick Shift clustering algorithm, whi...
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04/17/2018
Two-Player Games for Efficient Non-Convex Constrained Optimization
In recent years, constrained optimization has become increasingly releva...
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01/05/2018
Nonparametric Stochastic Contextual Bandits
We analyze the K-armed bandit problem where the reward for each arm is a...
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10/29/2017
On the Consistency of Quick Shift
Quick Shift is a popular mode-seeking and clustering algorithm. We prese...
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07/19/2017
Rates of Uniform Consistency for k-NN Regression
We derive high-probability finite-sample uniform rates of consistency fo...
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03/10/2017
Density Level Set Estimation on Manifolds with DBSCAN
We show that DBSCAN can estimate the connected components of the λ-densi...
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06/13/2016
