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07/20/2023
Nonlinear Meta-Learning Can Guarantee Faster Rates
Many recent theoretical works on meta-learning aim to achieve guarantees...
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07/11/2023
Tracking Most Significant Shifts in Nonparametric Contextual Bandits
We study nonparametric contextual bandits where Lipschitz mean reward fu...
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05/07/2023
Classification Tree Pruning Under Covariate Shift
We consider the problem of pruning a classification tree, that is, selec...
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04/29/2023
Limits of Model Selection under Transfer Learning
Theoretical studies on transfer learning or domain adaptation have so fa...
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12/27/2021
Tracking Most Severe Arm Changes in Bandits
In bandits with distribution shifts, one aims to automatically detect an...
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10/16/2021
Nuances in Margin Conditions Determine Gains in Active Learning
We consider nonparametric classification with smooth regression function...
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04/22/2021
An Efficient One-Class SVM for Anomaly Detection in the Internet of Things
Insecure Internet of things (IoT) devices pose significant threats to cr...
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07/16/2020
Self-Tuning Bandits over Unknown Covariate-Shifts
Bandits with covariates, a.k.a. contextual bandits, address situations w...
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06/30/2020
A Comparative Study of Network Traffic Representations for Novelty Detection
Data representation plays a critical role in the performance of novelty ...
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06/29/2020
A No-Free-Lunch Theorem for MultiTask Learning
Multitask learning and related areas such as multi-source domain adaptat...
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02/12/2020
On the Value of Target Data in Transfer Learning
We aim to understand the value of additional labeled or unlabeled target...
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08/16/2019
Kernel Sketching yields Kernel JL
The main contribution of the paper is to show that Gaussian sketching of...
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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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03/05/2018
Marginal Singularity, and the Benefits of Labels in Covariate-Shift
We present new minimax results that concisely capture the relative benef...
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12/06/2017
Achieving the time of 1-NN, but the accuracy of k-NN
We propose a simple approach which, given distributed computing resource...
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11/25/2017
An Adaptive Strategy for Active Learning with Smooth Decision Boundary
We present the first adaptive strategy for active learning in the settin...
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03/16/2017
Adaptivity to Noise Parameters in Nonparametric Active Learning
This work addresses various open questions in the theory of active learn...
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06/13/2016
Modal-set estimation with an application to clustering
We present a first procedure that can estimate -- with statistical consi...
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12/19/2013
Consistency of Causal Inference under the Additive Noise Model
We analyze a family of methods for statistical causal inference from sam...
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05/09/2012
Which Spatial Partition Trees are Adaptive to Intrinsic Dimension?
Recent theory work has found that a special type of spatial partition tr...
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10/19/2011
k-NN Regression Adapts to Local Intrinsic Dimension
Many nonparametric regressors were recently shown to converge at rates t...
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05/03/2011