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09/21/2023
Meta OOD Learning for Continuously Adaptive OOD Detection
Out-of-distribution (OOD) detection is crucial to modern deep learning a...
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07/18/2023
Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud
Multi-tenancy in public clouds may lead to co-location interference on s...
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07/16/2023
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
Achieving a reliable LiDAR-based object detector in autonomous driving i...
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04/27/2023
Moderately Distributional Exploration for Domain Generalization
Domain generalization (DG) aims to tackle the distribution shift between...
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10/26/2022
Is Out-of-Distribution Detection Learnable?
Supervised learning aims to train a classifier under the assumption that...
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06/09/2022
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms
The theoretical analysis of multi-class classification has proved that t...
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03/22/2022
Federated Class-Incremental Learning
Federated learning (FL) has attracted growing attention via data-private...
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06/30/2021
Learning Bounds for Open-Set Learning
Traditional supervised learning aims to train a classifier in the closed...
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12/30/2020
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?
Unsupervised domain adaptation (UDA) aims to train a target classifier w...
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08/04/2020
Learning from a Complementary-label Source Domain: Theory and Algorithms
In unsupervised domain adaptation (UDA), a classifier for the target dom...
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07/29/2020
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation
In unsupervised domain adaptation (UDA), classifiers for the target doma...
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06/23/2020
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation
In the unsupervised open set domain adaptation (UOSDA), the target domai...
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07/19/2019