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Found 26 papers, 11 papers with code
Online Boosting Adaptive Learning under Concept Drift for Multistream Classification
Specifically, OBAL operates in a dual-phase mechanism, in the first of which we design an Adaptive COvariate Shift Adaptation (AdaCOSA) algorithm to construct an initialized ensemble model using archived data from various source streams, thus mitigating the covariate shift while learning the dynamic correlations via an adaptive re-weighting strategy.
Meta OOD Learning for Continuously Adaptive OOD Detection
To address CAOOD, we develop meta OOD learning (MOL) by designing a learning-to-adapt diagram such that a good initialized OOD detection model is learned during the training process.
Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making
The framework offers both a guarantee of generalized performance and good accuracy.
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms
Hence, in this paper, we propose a novel framework to address a new realistic problem called multi-class classification with imprecise observations (MCIMO), where we need to train a classifier with fuzzy-feature observations.
Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning
Transfer learning where the behavior of extracting transferable knowledge from the source domain(s) and reusing this knowledge to target domain has become a research area of great interest in the field of artificial intelligence.
Deep Bayesian Estimation for Dynamic Treatment Regimes with a Long Follow-up Time
Causal effect estimation for dynamic treatment regimes (DTRs) contributes to sequential decision making.
Learning Bounds for Open-Set Learning
In this paper, we target a more challenging and realistic setting: open-set learning (OSL), where there exist test samples from the classes that are unseen during training.
Automatic Learning to Detect Concept Drift
Many methods have been proposed to detect concept drift, i. e., the change in the distribution of streaming data, due to concept drift causes a decrease in the prediction accuracy of algorithms.
PAC-Bayes Bounds for Meta-learning with Data-Dependent Prior
By leveraging experience from previous tasks, meta-learning algorithms can achieve effective fast adaptation ability when encountering new tasks.
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?
Experiments show that the proxy can effectively curb the increase of the combined risk when minimizing the source risk and distribution discrepancy.
Concept Drift Detection: Dealing with MissingValues via Fuzzy Distance Estimations
Our solution comprises a novel masked distance learning (MDL) algorithm to reduce the cumulative errors caused by iteratively estimating each missing value in an observation and a fuzzy-weighted frequency (FWF) method for identifying discrepancies in the data distribution.
Learning from a Complementary-label Source Domain: Theory and Algorithms
We consider two cases of this setting, one is that the source domain only contains complementary-label data (completely complementary unsupervised domain adaptation, CC-UDA), and the other is that the source domain has plenty of complementary-label data and a small amount of true-label data (partly complementary unsupervised domain adaptation, PC-UDA).
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation
To mitigate this problem, we consider a novel problem setting where the classifier for the target domain has to be trained with complementary-label data from the source domain and unlabeled data from the target domain named budget-friendly UDA (BFUDA).
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation
To achieve this aim, a previous study has proven an upper bound of the target-domain risk, and the open set difference, as an important term in the upper bound, is used to measure the risk on unknown target data.
Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation
Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams.
Learning under Concept Drift: A Review
To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted.
Learning Deep Kernels for Non-Parametric Two-Sample Tests
We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution.
Ranked #1 on
Two-sample testing
on HIGGS Data Set
Wildly Unsupervised Domain Adaptation and Its Powerful and Efficient Solution
Hence, we consider a new, more realistic and more challenging problem setting, where classifiers have to be trained with noisy labeled data from SD and unlabeled data from TD---we name it wildly UDA (WUDA).
Unsupervised Domain Adaptation
Wildly Unsupervised Domain Adaptation
Cross-domain Network Representations
By generating the random walks from a structural rich domain and transferring the knowledge on the random walks across domains, it enables a network representation for the structural scarce domain as well.
Open Set Domain Adaptation: Theoretical Bound and Algorithm
The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain -- the basic strategy being to mitigate the effects of discrepancies between the two distributions.
Ranked #19 on
Domain Adaptation
on Office-Home
Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation
Hence, we consider a new, more realistic and more challenging problem setting, where classifiers have to be trained with noisy labeled data from SD and unlabeled data from TD -- we name it wildly UDA (WUDA).
Unsupervised Domain Adaptation
Wildly Unsupervised Domain Adaptation
Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting
We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function.
Cooperative Hierarchical Dirichlet Processes: Superposition vs. Maximization
The cooperative hierarchical structure is a common and significant data structure observed in, or adopted by, many research areas, such as: text mining (author-paper-word) and multi-label classification (label-instance-feature).
Dependent Indian Buffet Process-based Sparse Nonparametric Nonnegative Matrix Factorization
Under this same framework, two classes of correlation function are proposed (1) using Bivariate beta distribution and (2) using Copula function.
Nonparametric Relational Topic Models through Dependent Gamma Processes
Traditional Relational Topic Models provide a way to discover the hidden topics from a document network.
Infinite Author Topic Model based on Mixed Gamma-Negative Binomial Process
One branch of these works is the so-called Author Topic Model (ATM), which incorporates the authors's interests as side information into the classical topic model.
