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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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10/16/2022
Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making
As a powerful Bayesian non-parameterized algorithm, the Gaussian process...
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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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09/27/2021
Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning
Transfer learning where the behavior of extracting transferable knowledg...
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09/20/2021
Deep Bayesian Estimation for Dynamic Treatment Regimes with a Long Follow-up Time
Causal effect estimation for dynamic treatment regimes (DTRs) contribute...
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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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05/04/2021
Automatic Learning to Detect Concept Drift
Many methods have been proposed to detect concept drift, i.e., the chang...
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02/07/2021
PAC-Bayes Bounds for Meta-learning with Data-Dependent Prior
By leveraging experience from previous tasks, meta-learning algorithms c...
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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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08/09/2020
Concept Drift Detection: Dealing with MissingValues via Fuzzy Distance Estimations
In data streams, the data distribution of arriving observations at diffe...
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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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04/13/2020
Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation
Concept drift refers to changes in the distribution of underlying data a...
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04/13/2020
Learning under Concept Drift: A Review
Concept drift describes unforeseeable changes in the underlying distribu...
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02/21/2020
Learning Deep Kernels for Non-Parametric Two-Sample Tests
We propose a class of kernel-based two-sample tests, which aim to determ...
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08/01/2019
Cross-domain Network Representations
The purpose of network representation is to learn a set of latent featur...
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07/19/2019
Open Set Domain Adaptation: Theoretical Bound and Algorithm
Unsupervised domain adaptation for classification tasks has achieved gre...
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05/19/2019
Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation
In unsupervised domain adaptation (UDA), classifiers for the target doma...
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05/19/2019
Butterfly: Robust One-step Approach towards Wildly-unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) trains with clean labeled data in s...
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12/22/2018
Deep Uncertainty Learning: A Machine Learning Approach for Weather Forecasting
This paper uses the weather forecasting as an application background to ...
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07/18/2017
Cooperative Hierarchical Dirichlet Processes: Superposition vs. Maximization
The cooperative hierarchical structure is a common and significant data ...
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03/30/2015
Nonparametric Relational Topic Models through Dependent Gamma Processes
Traditional Relational Topic Models provide a way to discover the hidden...
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03/30/2015
