Jesse Read

Orcid: 0000-0002-1013-6724

According to our database1, Jesse Read authored at least 89 papers between 2008 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Autoreplicative random forests with applications to missing value imputation.
Mach. Learn., October, 2024

Branches: A Fast Dynamic Programming and Branch & Bound Algorithm for Optimal Decision Trees.
CoRR, 2024

Backward Inference in Probabilistic Regressor Chains with Distributional Constraints.
Proceedings of the Advances in Intelligent Data Analysis XXII, 2024

Online Learning of Decision Trees with Thompson Sampling.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
From multi-label learning to cross-domain transfer: a model-agnostic approach.
Appl. Intell., November, 2023

On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG.
IEEE Trans. Biomed. Eng., July, 2023

A Survey on Semi-supervised Learning for Delayed Partially Labelled Data Streams.
ACM Comput. Surv., 2023

A Historical Context for Data Streams.
CoRR, 2023

Uncovering the Spatial and Temporal Variability of Wind Resources in Europe: A Web-Based Data-Mining Tool.
CoRR, 2023

Transferable Deep Metric Learning for Clustering.
CoRR, 2023

Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets.
CoRR, 2023

Which Explanation Makes Sense? A Critical Evaluation of Local Explanations for Assessing Cervical Cancer Risk.
Proceedings of the Machine Learning for Healthcare Conference, 2023

Transferable Deep Metric Learning for Clustering.
Proceedings of the Advances in Intelligent Data Analysis XXI, 2023

2022
Optimality in noisy importance sampling.
Signal Process., 2022

Multi-output regression with structurally incomplete target labels: A case study of modelling global vegetation cover.
Ecol. Informatics, 2022

Learning from Data Streams: An Overview and Update.
CoRR, 2022

Linear TreeShap.
CoRR, 2022

Estimating Multi-label Accuracy using Labelset Distributions.
CoRR, 2022

Isomorphic Cross-lingual Embeddings for Low-Resource Languages.
Proceedings of the 7th Workshop on Representation Learning for NLP, 2022

An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Conv-NILM-Net, a Causal and Multi-appliance Model for Energy Source Separation.
Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022

Linear tree shap.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Multi-modal Ensembles of Regressor Chains for Multi-output Prediction.
Proceedings of the Advances in Intelligent Data Analysis XX, 2022

CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies.
Proceedings of the 30th European Signal Processing Conference, 2022

Shapley Chains: Extending Shapley Values to Classifier Chains.
Proceedings of the Discovery Science - 25th International Conference, 2022

2021
Learning from evolving data streams through ensembles of random patches.
Knowl. Inf. Syst., 2021

River: machine learning for streaming data in Python.
J. Mach. Learn. Res., 2021

Classifier Chains: A Review and Perspectives.
J. Artif. Intell. Res., 2021

A joint introduction to Gaussian Processes and Relevance Vector Machines with connections to Kalman filtering and other kernel smoothers.
Inf. Fusion, 2021

Structure and influence in a global capital-ownership network.
Appl. Netw. Sci., 2021

2020
An empirical analysis of binary transformation strategies and base algorithms for multi-label learning.
Mach. Learn., 2020

Probabilistic regressor chains with Monte Carlo methods.
Neurocomputing, 2020

Better Sign Language Translation with STMC-Transformer.
Proceedings of the 28th International Conference on Computational Linguistics, 2020

Data Streams Are Time Series: Challenging Assumptions.
Proceedings of the Intelligent Systems - 9th Brazilian Conference, 2020

2019
Machine learning for streaming data: state of the art, challenges, and opportunities.
SIGKDD Explor., 2019

Error-space representations for multi-dimensional data streams with temporal dependence.
Pattern Anal. Appl., 2019

Correction to: Adaptive random forests for evolving data stream classification.
Mach. Learn., 2019

Introduction to the special issue on Big Data, IoT Streams and Heterogeneous Source Mining.
Int. J. Data Sci. Anal., 2019

Assessing the Multi-labelness of Multi-label Data.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Network of Experts: Learning from Evolving Data Streams Through Network-Based Ensembles.
Proceedings of the Neural Information Processing - 26th International Conference, 2019

Streaming Random Patches for Evolving Data Stream Classification.
Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

Empirical Analysis of a Global Capital-Ownership Network.
Proceedings of the Complex Networks and Their Applications VIII, 2019

Perturb and combine to identify influential spreaders in real-world networks.
Proceedings of the ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining, 2019

2018
A survey of evaluation methods for personal route and destination prediction from mobility traces.
WIREs Data Mining Knowl. Discov., 2018

Scikit-Multiflow: A Multi-output Streaming Framework.
J. Mach. Learn. Res., 2018

Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation.
CoRR, 2018

A Blended Metric for Multi-label Optimisation and Evaluation.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Scalable Model-Based Cascaded Imputation of Missing Data.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

Set Labelling using Multi-label Classification.
Proceedings of the 20th International Conference on Information Integration and Web-based Applications & Services, 2018

EXAD: A System for Explainable Anomaly Detection on Big Data Traces.
Proceedings of the 2018 IEEE International Conference on Data Mining Workshops, 2018

Ubiquitous Artificial Intelligence and Dynamic Data Streams.
Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, 2018

Learning Fast and Slow: A Unified Batch/Stream Framework.
Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

Detection of sleep spindles in NREM 2 sleep stages: Preliminary study & benchmarking of algorithms.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2018

2017
Multi-label methods for prediction with sequential data.
Pattern Recognit., 2017

Adaptive random forests for evolving data stream classification.
Mach. Learn., 2017

Data stream classification using random feature functions and novel method combinations.
J. Syst. Softw., 2017

Cooperative parallel particle filters for online model selection and applications to urban mobility.
Digit. Signal Process., 2017

Low-latency multi-threaded ensemble learning for dynamic big data streams.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

2016
MEKA: A Multi-label/Multi-target Extension to WEKA.
J. Mach. Learn. Res., 2016

Labeling sensing data for mobility modeling.
Inf. Syst., 2016

Echo State Hoeffding Tree Learning.
Proceedings of The 8th Asian Conference on Machine Learning, 2016

2015
Independent Doubly Adaptive Rejection Metropolis Sampling Within Gibbs Sampling.
IEEE Trans. Signal Process., 2015

Scalable multi-output label prediction: From classifier chains to classifier trellises.
Pattern Recognit., 2015

Evaluation methods and decision theory for classification of streaming data with temporal dependence.
Mach. Learn., 2015

Deep Learning for Multi-label Classification.
CoRR, 2015

Multi-label Classification using Labels as Hidden Nodes.
CoRR, 2015

Data Stream Classification Using Random Feature Functions and Novel Method Combinations.
Proceedings of the 2015 IEEE TrustCom/BigDataSE/ISPA, 2015

Deep learning in partially-labeled data streams.
Proceedings of the 30th Annual ACM Symposium on Applied Computing, 2015

Efficient Online Evaluation of Big Data Stream Classifiers.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2014
Multi-Dimensional Classification with Super-Classes.
IEEE Trans. Knowl. Data Eng., 2014

A distributed particle filter for nonlinear tracking in wireless sensor networks.
Signal Process., 2014

Efficient monte carlo methods for multi-dimensional learning with classifier chains.
Pattern Recognit., 2014

Kaggle LSHTC4 Winning Solution.
CoRR, 2014

WISE 2014 Challenge: Multi-label Classification of Print Media Articles to Topics.
Proceedings of the Web Information Systems Engineering - WISE 2014, 2014

A Deep Interpretation of Classifier Chains.
Proceedings of the Advances in Intelligent Data Analysis XIII, 2014

Multi-label Classification with Meta-Labels.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

Independent doubly Adaptive Rejection Metropolis Sampling.
Proceedings of the IEEE International Conference on Acoustics, 2014

2013
On the flexibility of the design of multiple try Metropolis schemes.
Comput. Stat., 2013

Efficient data stream classification via probabilistic adaptive windows.
Proceedings of the 28th Annual ACM Symposium on Applied Computing, 2013

Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

CD-MOA: Change Detection Framework for Massive Online Analysis.
Proceedings of the Advances in Intelligent Data Analysis XII, 2013

Efficient monte carlo optimization for multi-label classifier chains.
Proceedings of the IEEE International Conference on Acoustics, 2013

2012
Scalable and efficient multi-label classification for evolving data streams.
Mach. Learn., 2012

Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data.
Proceedings of the Advances in Intelligent Data Analysis XI - 11th International Symposium, 2012

Stream Data Mining Using the MOA Framework.
Proceedings of the Database Systems for Advanced Applications, 2012

2011
Classifier chains for multi-label classification.
Mach. Learn., 2011

Streaming Multi-label Classification.
Proceedings of the Second Workshop on Applications of Pattern Analysis, 2011

MOA: A Real-Time Analytics Open Source Framework.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

2008
Multi-label Classification Using Ensembles of Pruned Sets.
Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008


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