Jesse Read

Tech. Report Ayman Chaouki, Jesse Read and Albert Bifet. Branches: A Fast Dynamic Programming and Branch \& Bound Algorithm for Optimal Decision Trees. ArXiv. 2024.

Journal Ekaterina Antonenko, Ander Carreño and Jesse Read. Autoreplicative Random Forests with applications to missing value imputation. Machine Learning Journal. In Press. 2024.

Journal Fernando Llorente, Luca Martino, Jesse Read and David Delgado-Gómez. A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC. International Statistical Review. In Press. 2024.

Conference Ekaterina Antonenko, Michael Mechenich, Rita Beigaitė, Indrė Žliobaitė and Jesse Read. Backward inference in probabilistic Regressor Chains with distributional constraints. In Proc. of IDA 2024: Advances in Intelligent Data Analysis XXII, 22nd International Symposium. pp 43-55. 2024.

Conference Ayman Chaouki, Jesse Read and Albert Bifet. Online Learning of Decision Trees with Thompson Sampling. In Proc. of International Conference on Artificial Intelligence and Statistics (AIStats). pp 2944—2952. 2024.

Tech. Report Indrė Žliobaitė and Jesse Read. A Historical Context for Data Streams. ArXiv. 2023.

Workshop Ayman Chaouki, Jesse Read and Albert Bifet. Online Decision Tree Construction with Deep Reinforcement Learning. EWRL '23: Sixteenth European Workshop on Reinforcement Learning. 2023.

Conference Wafa Ayad Read, Thomas Bonnier, Benjamin Bosch, Jesse and Sonali Parbhoo. Which Explanation Makes Sense? A Critical Evaluation of Local Explanations for Assessing Cervical Cancer Risk Factors. To appear in Proc. of Machine Learning for Healthcare (MLHC '23). 2023.

Workshop Alban Puech and Jesse Read. Uncovering the Spatial and Temporal Variability of Wind Resources in Europe: A Web-Based Data-Mining Tool. Tackling Climate Change with Machine Learning workshop at ICLR. 2023.

Conference Mohamed Alami Chehboune, Rim Kaddah and Jesse Read. Transferable Deep Metric Learning for Clustering. In Proc. of IDA 2023: Advances in Intelligent Data Analysis XXI, 21st International Symposium. pp 15-28. 2023.

Journal Eran Zvuloni, Jesse Read, Antônio H. Ribeiro, Antonio Luiz P. Ribeiro and Joachim A. Behar. On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG. IEEE Transactions on Biomedical Engineering. In Press. 2023.

Journal Jesse Read. From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach. Applied Intelligence. Vol. 08(2023). pp 1537-7497. 2023.

Tech. Report Jesse Read and Indrė Žliobaitė. Learning from Data Streams: An Overview and Update. ArXiv. 2023.

Tech. Report Laurence A. F. Park and Jesse Read. Estimating Multi-label Accuracy using Labelset Distributions. ArXiv. 2022.

Conference Celia Wafa Ayad, Thomas Bonnier, Benjamin Bosch and Jesse Read. Shapley Chains: Extending Shapley Values to Classifier Chains. In Proc. of Discovery Science 2022. pp 541—555. 2022.

Conference Alban Puech and Jesse Read. An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning. In Proc. of ECML-PKDD 2022: 33rd European Conference on Machine Learning. pp 614—630. 2023.

Journal Rita Beigaite, Jesse Read and Indrė Žliobaitė. Multi-output Regression with Structurally Incomplete Target Labels: A Case Study of Modelling Global Vegetation Cover. Ecological Informatics. Vol. 72. pp 101849. 2022.

Workshop Ekaterina Antonenko and Jesse Read. Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets. \*Best Paper\* at MLLCTOC 22 (an ECML-PKDD workshop): Workshop on Multi-Label Learning Current Trends and Open Challenges. 2022.

Workshop Mohamed Alami Chehbourne, Jérémie Decock, Rim Kaddah and Jesse Read. Conv-NILM-Net, a causal and multi-appliance model for energy source separation. MLBEM 2022: ECML-PKDD Workshop on Machine Learning for Buildings Energy Management. 2022.

Conference Mohamed Alami Chehboune, Fernando Llorente, Rim Kaddah, Luca Martino and Jesse Read. CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies. To appear in Proc. of EUSIPCO 2022: The 30th European Signal Processing Conference. 2022.

Workshop Sonal Sannigrahi and Jesse Read. Isomorphic Cross-lingual Embeddings for Low-Resource Languages. RepL4NLP 2022 (an ACL workshop): The 7th Workshop on Representation Learning for NLP. 2022.

Journal Fernando Llorente, Luca Martino, Jesse Read and David Delgado-Gómez. Optimality in noisy importance sampling. Signal Processing. Vol. 194. pp 108455. 2022.

Journal Heitor Murilo Gomes, Maciej Grzenda, Rodrigo Mello, Jesse Read, Minh Huong Le Nguyen and Albert Bifet. A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams. ACM Computing Surveys. Vol. 55(75). pp 1-42. 2023.

Conference Ekaterina Antonenko and Jesse Read. Multi-Modal Ensembles of Regressor Chains for Multi-Output Prediction. In Proc. of IDA 2022: Advances in Intelligent Data Analysis XX, 20th International Symposium. pp 1-13. 2022.

Proceedings Nuria Oliver and Fernando Pérez-Cruz and Stefan Kramer and Jesse Read and Jose A. Lozano, editors. Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part I, II, and III. Lecture Notes in Computer Science 12975, 2021.

Journal Heitor M. Gomes, Jesse Read, Albert Bifet and Robert J. Durrant. Learning From Evolving Data Streams Through Ensembles of Random Patches. Knowledge and Information Systems (KAIS). Vol. 63(). pp pages 1597–1625. 2021.

Journal Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem and Albert Bifet. River: machine learning for streaming data in Python. Journal of Machine Learning Research. Vol. 22(110). pp 1—8. 2021.

Journal Luca Martino and Jesse Read. Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers. Information Fusion. Vol. 74. pp 17—38. 2021.

Journal Sammy Khalife, Jesse Read and Michalis Vazirgiannis. Structure and influence in a global capital-ownership network. Applied Network Science. Vol. 6(16). pp 656—667. 2021.

Journal Jesse Read, Bernhard Pfahringer, Geoff Holmes and Eibe Frank. Classifier Chains: A Review and Perspectives. Journal of Artificial Intelligence Research (JAIR). Vol. 70. pp 683—718. 2021.

Journal Olivier Pallanca and Jesse Read. Principes généraux et définitions en intelligence artificielle. Archives des Maladies du Coeur et des Vaisseaux – Pratique. Vol. 2021(294). pp 3-10. 2021.

Workshop Rita Beigaite, Jesse Read and Indre Zliobaite. Multi-output prediction of global vegetation distribution with incomplete data. Artemiss 2020 (an ICML workshop): The Art of Learning with Missing Values. 2020.

Conference Jesse Read, Ricardo Rios, Tatiane Nogueira and Rodrigo Mello. Data Streams are Time Series: Challenging Assumptions. In Proc. of 9th Brazilian Conference on Intelligent Systems (BRACIS 2020). pp 529-543. 2020.

Conference Kayo Yin and Jesse Read. Better Sign Language Translation with STMC-Transformer. In Proc. of COLING’2020 - International Conference on Computational Linguistics. pp 5975—5989. 2020.

Workshop Kayo Yin and Jesse Read. Attention is All You Sign: Sign Language Translation with Transformers. SLRTP workshop at ECCV 2020. 2020.

Journal Jesse Read and Luca Martino. Probabilistic Regressor Chains with Monte-Carlo Methods. Neurocomputing. Vol. 413. pp 471—486. 2020.

Journal Adriano Rivolli, Jesse Read, Carlos Soares, Bernhard Pfahringer and André C. P. L. F. de Carvalho. An empirical analysis of binary transformation strategies and base algorithms for multi-label learning. Machine Learning. Vol. 109(1573-0565). pp 1509-1563. 2020.

Proceedings Albert Bifet and Michele Berlingerio and João Gama and Jesse Read and Ana Rita Nogueira, editors. Proceedings of the 8th International Workshop on Big Data, IoT Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications co-located with 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Alaska, August 4-8, 2019. CEUR Workshop Proceedings 2579, 2020.

Workshop Rita Beigaite, Jesse Read and Indre Zliobaite. Multi-output prediction of global vegetation distribution with incomplete data. Northern Lights Deep Learning Workshop. 2020.

Journal Heitor Murilo Gomes, Jesse Read, Albert Bifet, Jean Paul Barddal and João Gama. Machine Learning for Streaming Data: State of the Art, Challenges, and Opportunities. SIGKDD Explorations Newsletter. Vol. 21(2). pp 6—22. 2019.

Conference Heitor M. Gomes, Albert Bifet, Philippe Fournier-Viger, Jones Granatyr and Jesse Read. Network of Experts: Learning from evolving data streams through network-based ensembles. In Proc. of ICONIP’19: International Conference on Neural Information Processing of the Asia-Pacific Neural Network Society. pp 704—716. 2019.

Conference Sammy Khalife, Jesse Read and Michalis Vazirgiannis. Empirical Analysis of a Global Capital-Ownership Network. In Proc. of International Conference on Complex Networks and Their Applications. pp 656—667. 2019.

Conference Laurence A. F. Park, Yi Guo and Jesse Read. Assessing the multi-labelness of multi-label data. In Proc. of ECML 2019: 30th European Conference on Machine Learning. pp 164—179. 2019.

Conference Heitor M. Gomes, Jesse Read and Albert Bifet. Streaming Random Patches for Evolving Data Stream Classification. In Proc. of ICDM’19: IEEE International Conference on Data Mining. pp 240—249. 2019.

Conference Antoine J.-P. Tixier, Maria Evgenia G. Rossi, Fragkiskos D. Malliaros, Jesse Read and Michalis Vazirgiannis. Perturb and Combine to Identify Influential Spreaders in Real-World Networks. In Proc. of ASONAM’19: IEEE/ACM International Conference on Social Networks Analysis and Mining. pp 73–80. 2019.

Proceedings Read, Jesse and Bifet, Albert and Fan, Wei and Yang, Qiang and Yu, Philip, editors. Introduction to the special issue on Big Data, IoT Streams and Heterogeneous Source Mining. International Journal of Data Science and Analytics 8, 2019.

Journal Khalida Douibi, Nesma Settouti, Mohammed El Amine Chikh, Jesse Read and Mohammed Malik Benabid. An Analysis of Ambulatory Blood Pressure Monitoring using multi-label classification. Australasian Physical and Engineering Sciences in Medicine. Vol. 42(1). pp 65—81. 2019.

Journal Jesse Read, Nikolaos Tziortziotis and Michalis Vazirgiannis. Error-space Representations for Multi-dimensional Data-Streams. Pattern Analysis and Applications. Vol. 22(3). pp 1211-1220. 2019.

Book Chapter Albert Bifet, Jesse Read, Geoff Holmes and Bernhard Pfahringer. Streaming Data Mining with Massive Online Analytics (MOA). In Mark Last, Horst Bunke, Abraham Kandel (Ed.), Series in Machine Perception and Artificial Intelligence. (pp. 1—25). World Scientific. 2018

Workshop Olivier Pallanca, Sammy Khalife and Jesse Read. Detection of sleep spindles in NREM 2 sleep stages: Preliminary study \& benchmarking of algorithms. IEEE BIBM 2018 workshop on Machine Learning for EEG Signal Processing. 2018.

Tech. Report Jesse Read. Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation. ArXiv. 2018.

Conference Jacob Montiel, Albert Bifet, Viktor Losing, Jesse Read and Talel Abdessalem. Learning Fast and Slow - A Unified Batch/Stream Framework. In Proc. of IEEE BigData 2018 International Conference on Big Data. pp 1065—1072. 2018.

Journal Jacob Montiel, Jesse Read, Albert Bifet and Talel Abdessalem. Scikit-MultiFlow: A Multi-output Streaming Framework. Journal of Machine Learning Research. Vol. 19(72). pp 1—5. 2018.

Conference Ngurah Agus Sanjaya, Jesse Read, Talel Abdessalem and Stephane Bressan. Set Labeling using Multi-label Classification. In Proc. of iiWAS2018: 20th International Conference on Information Integration and Web-based Applications \& Services. pp 216-220. 2018.

Conference Fei Song, Yanlei Diao, Jesse Read, Arnaud Stiegler and Albert Bifet. EXAD: A System for Explainable Anomaly Detection on Big Data Traces. In Proc. of ICDM’18: IEEE International Conference on Data Mining (Demo Session). pp 1435-1440. 2018.

Conference Laurence A. F. Park and Jesse Read. A Blended Metric for Multi-label Optimisation and Evaluation. In Proc. of ECML-PKDD 2018: 29th European Conference on Machine Learning. pp 719—734. 2018.

Workshop Lukas Kemmer, Henrik von Kleist, Diego María De Grimaudet De Rochebouët, Nikolaos Tziortziotis and Jesse Read. Reinforcement learning for supply chain optimization. EWRL14: European Workshop on Reinforcement Learning. 2018.

Conference Albert Bifet and Jesse Read. Ubiquitous Artificial Intelligence and Dynamic Data Streams. In Proc. of DEBS’18: 12th ACM International Conference on Distributed and Event-based Systems. pp 1—6. 2018.

Conference Jacob Montiel, Jesse Read, Albert Bifet and Talel Abdessalem. Scalable Model-based Cascaded Imputation of Missing Data. In Proc. of PAKDD 2018: 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining. pp 64—76. 2018.

Tech. Report Jesse Read and Jaakko Hollm'en. Multi-label Classification using Labels as Hidden Nodes. ArXiv. 2017.

Proceedings Yasemin Altun and Kamalika Das and Taneli Mielikäinen and Donato Malerba and Jerzy Stefanowski and Jesse Read and Marinka Žitnik and Michelangelo Ceci and Sašo Džeroski, editors. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18—22, 2017, Proceedings, Part III. Lecture Notes in Computer Science 10536, 2017.

Thesis Jesse Read. Contributions to the advancement of learning from multi-labelled data and data streams. Habilitation \`a diriger des recherches (HDR) en informatique. Université Paris-Sud, Orsay, France. 2017.

Conference Diego Marron, Jesse Read, Albert Bifet, Eduard Ayguadé and José R. Herrero. Low-latency Multi-threaded Ensemble Learning for Dynamic Big Data Streams. In Proc. of IEEE International Conference on Big Data (Big Data 2017). pp 223-232. 2017.

Journal Roelant A. Stegmann, Indre Zliobaite, Tuukka Tolvanen, Jaakko Hollmen and Jesse Read. A survey of evaluation methods for personal route and destination prediction from mobility traces. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. Vol. 2(8). pp e1237. 2017.

Journal Heitor M. Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfahringer, Geoff Holmes and Talel Abdessalem. Adaptive Random Forests for Evolving Data Stream Classification. Machine Learning Journal. Vol. 106(9-10). pp 1469-1495. 2017.

Journal Luca Martino, Jesse Read, Victor Elvira and Francisco Louzada. Cooperative Parallel Particle Filters for online model selection and applications to Urban Mobility. Digital Signal Processing. Vol. 60(January). pp 172—185. 2017.

Journal Jesse Read, Luca Martino and Jaakko Hollm'en. Multi-label Methods for Prediction with Sequential Data. Pattern Recognition. Vol. 63(March). pp 45—55. 2017.

Journal Liisa Kulmala, Jesse Read, Pekka Nöjd, Cyrille B.K. Rathgeber, Henri E. Cuny, Jaakko Hollmén and Harri Mäkinen. Identifying the main drivers for the production and maturation of Scots pine tracheids along a temperature gradient. Agricultural and Forest Meteorology. Vol. 232(January). pp 210—224. 2017.

Journal Diego Marron, Jesse Read and Albert Bifet. Data Stream Classification using Random Feature Functions and Novel Method Combinations. Journal of Systems and Software. Vol. 127(May). pp 195–204. 2017.

Journal Albert Bifet and Jesse Read. Internet de las Cosas: La miner'ia de flujos de datos masivos en tiempo real. Nov'atica (Revista de la Asociaci'on de T'ecnicos de Inform'atica), Monograf'ia Big Data. Vol. Julio-Octubre(237). pp 24-30. 2016.

Conference Diego Marron, Jesse Read, Albert Bifet, Talel Abdessalem, Eduard Ayguadé and José R. Herrero. Echo State Hoeffding Tree Learning. In Proc. of ACML 2016: The 8th Asian Conference on Machine Learning. pp 382—397. 2016.

Proceedings Wei Fan and Albert Bifet and Jesse Read and Qiang Yang and Philip S. Yu, editors. Proceedings of the 5th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, BigMine 2016, San Francisco, CA, USA, August 14, 2016. JMLR Workshop and Conference Proceedings 53, 2016.

Workshop Ngurah Agus Sanjaya Er, Jesse Read, Talel Abdessalem and Stephane Bressan. Set Labelling by Example. The 11th International Workshop on Information Search, Integration, and Personalization. 2016.

Journal Jesse Read, Peter Reutemann, Bernhard Pfahringer and Geoff Holmes. MEKA: A Multi-label/Multi-target Extension to Weka. Journal of Machine Learning Research. Vol. 17(21). pp 1—5. 2016.

Journal Jesse Read, Indre Zliobaite and Jaakko Hollmen. Labeling sensing data for mobility modeling. Information Systems. Vol. 57(April). pp 207—222. 2016.

Workshop Jesse Read and Albert Bifet. Data Stream Classification using Random Feature Functions and Novel Method Combinations. RTStreams2015: The 1st IEEE International Workshop on Real Time Data Stream Analytics held in conjunction with IEEE BigDataSE-15. 2015.

Conference Albert Bifet, Gianmarco De Francisci Morales, Jesse Read, Bernhard Pfahringer and Geoff Holmes. Efficient Online Evaluation of Big Data Stream Classifiers. In Proc. of ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’15). pp 59-68. 2015.

Journal Luca Martino, Jesse Read and David Luengo. Independent Doubly Adaptive Rejection Metropolis Sampling within Gibbs Sampling. Transactions on Signal Processing. Vol. 63(12). pp 3123 - 3138. 2015.

Conference Jesse Read, Fernando Perez-Cruz and Albert Bifet. Deep Learning in Partially-Labelled Data-Streams. In Proc. of SAC 2015: 30th ACM Symposium on Applied Computing. pp 954-959. 2015.

Journal Jesse Read, Luca Martino, Pablo M. Olmos and David Luengo. Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises. _Pattern Recognition _. Vol. 48(6). pp 2096—2109. 2015.

Tech. Report Jesse Read, Luca Martino and Francisco Louzada. Viterbi Classifier Chains for Multi-Dimensional Learning. ArXiv. 2015.

Conference Jesse Read, Antti Puurula and Albert Bifet. Multi-label Classification with Meta Labels. In Proc. of ICDM’14: IEEE International Conference on Data Mining. pp 941—946. 2014.

Conference Jesse Read and Jaakko Hollm'en. A Deep Interpretation of Classifier Chains. In Proc. of IDA 2014: Advances in Intelligent Data Analysis XIII, 13th International Symposium. pp 251—262. 2014.

Conference Grigorios Tsoumakas, Apostolos Papadopoulos, Weining Qian, Stavros Vologiannidis, Alexander D’yakonov, Antti Puurula, Jesse Read, Jan Svec and Stanislav Semenov. WISE 2014 Challenge: Multi-label Classification of Print Media Articles to Topics. In Proc. of WISE 2014: 15th International Conference on Web Information Systems Engineering. pp 541—548. 2014.

Journal Indre Zliobaite, Albert Bifet, Jesse Read, Bernhard Pfahringer and Geoff Holmes. Evaluation methods and decision theory for classification of streaming data with temporal dependence. Machine Learning. Vol. 98(3). pp 455-482. 2014.

Book Chapter Jesse Read and Albert Bifet. Multi-label Classification. In Wang, John (Ed.), Encyclopedia of Business Analytics and Optimization. (pp. 1581—1584). IGI Global. 2014

Book Chapter Albert Bifet and Jesse Read. Data Stream Mining. In Wang, John (Ed.), Encyclopedia of Business Analytics and Optimization. (pp. 664—666). IGI Global. 2014

Tech. Report Antti Puurula, Jesse Read and Albert Bifet. Kaggle LSHTC4 Winning Solution. Report on our winning solution to the LSHTC4 Kaggle Competition. 2014.

Conference Luca Martino, Jesse Read and David Luengo. Independent doubly Adaptive Rejection Metropolis Sampling. In Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp 7998—8002. 2014.

Journal Jesse Read, Luca Martino and David Luengo. Efficient Monte Carlo Methods for Multi-dimensional Learning with Classifier Chains. Pattern Recognition. Vol. 47(3). pp 1535—1546. 2014.

Workshop Jesse Read. Classifier Chains for Multi-target Prediction. MTP 2014: ECML-PKKD 2014 International Workshop on Multi-target Prediction (MTP '14).. 2014.

Journal Jesse Read, Katrin Achutegui and Joaqu'in M'iguez. A Distributed Particle Filter for Nonlinear Tracking in Wireless Sensor Networks. Signal Processing. Vol. 98(May 2014). pp 121—134. 2014.

Journal Jesse Read, Concha Bielza and Pedro Larra~naga. Multi-Dimensional Classification with Super-Classes. Transactions on Knowledge and Data Engineering. Vol. 26(7). pp 1720—1733. 2014.

Conference Albert Bifet, Jesse Read, Indre Zliobaite, Bernhard Pfahringer and Geoff Holmes. Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them. In Proc. of ECML-PKDD 2013: 24th European Conference on Machine Learning. pp 465—479. 2013.

Conference Jesse Read, Luca Martino and David Luengo. Efficient Monte Carlo Optimization for Multi-label Classifier Chains. In Proc. of ICASSP 2013: The 38th International Conference on Acoustics, Speech, and Signal Processing. pp 3457—3461. 2013.

Tech. Report Jesse Read and Fernando Perez-Cruz. Deep Learning for Multi-label Classification. ArXiv. 2013.

Conference Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes and Indre Zliobaite. CD-MOA: Change Detection Framework for Massive Online Analysis. In Proc. of IDA 2013: 12th International Symposium on Advances in Intelligent Data Analysis. pp 92-103. 2013.

Conference Albert Bifet, Jesse Read, Bernhard Pfahringer and Geoff Holmes. Efficient Data Stream Classification via Probabilistic Adaptive Windows. In Proc. of SAC 2013: 28th ACM Symposium on Applied Computing. pp 801—806. 2013.

Workshop Luca Martino, Jesse Read and David Luengo. Improved Adaptive Rejection Metropolis Sampling. 29th European Meeting of Statisticians (EMS 2013). 2013.

Journal Luca Martino and Jesse Read. On the flexibility of the design of multiple try Metropolis schemes. Computational Statistics. Vol. 28(6). pp 2797—2823. 2013.

Workshop Jesse Read, Albert Bifet, Geoff Holmes and Bernhard Pfahringer. Streaming Multi-label Classification. WAPA 2011: Workshop on Applications of Pattern Analysis. 2012.

Journal Luca Martino, Victor Pascual Del Olmo and Jesse Read. A multi-point Metropolis scheme with generic weight functions. Statistics and Probability Letters. Vol. 82(7). pp 1445-1453. 2012.

Conference Jesse Read, Albert Bifet, Bernhard Pfahringer and Geoff Holmes. Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data. In Proc. of IDA 2012: 11th International Symposium on Advances in Intelligent Data Analysis. pp 313-323. 2012.

Conference Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer and Jesse Read. Stream Data Mining Using the MOA Framework. In Proc. of DASFAA '12: 17th International Conference on Database Systems for Advanced Applications. pp 309-313. 2012.

Journal Jesse Read, Albert Bifet, Geoff Holmes and Bernhard Pfahringer. Scalable and efficient multi-label classification for evolving data streams. Machine Learning. Vol. 88(1-2). pp 243-272. 2012.

Conference Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Jesse Read, Philipp Kranen, Hardy Kremer, Timm Jansen and Thomas Seidl. MOA: a Real-time Analytics Open Source Framework. In Proc. of ECML PKDD '11: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. pp 617-620. 2011.

Journal Jesse Read, Bernhard Pfahringer, Geoff Holmes and Eibe Frank. Classifier Chains for Multi-label Classification. Machine Learning. Vol. 85(3). pp 333-359. 2011.

Tech. Report Albert Bifet, Jesse Read, Geoff Holmes and Bernhard Pfahringer. Efficient Multi-label Classification for Evolving Data Streams. Technical Report 2010/04. 2010.

Thesis Jesse Read. Scalable Multi-label Classification. PhD Thesis. University of Waikato, Hamilton, New Zealand. 2010.

Conference Jesse Read, Bernhard Pfahringer, Geoff Holmes and Eibe Frank. Classifier Chains for Multi-label Classification. In Proc. of ECML-PKDD 2009: 20th European Conference on Machine Learning. pp 254—269. 2009.

Workshop Jesse Read, Bernhard Pfahringer and Geoff Holmes. Generating Synthetic Multi-label Data Streams. MLD 2009: 1st ECML/PKDD 2009 Workshop on Learning from Multi-Label Data. 2009.

Conference Jesse Read, Bernhard Pfahringer and Geoff Holmes. Multi-label Classification Using Ensembles of Pruned Sets. In Proc. of ICDM 2008: Eighth IEEE International Conference on Data Mining. pp 995-1000. 2008.

Conference Jesse Read, Bernhard Pfahringer and Geoff Holmes. A Pruned Problem Transformation Method for Multi-label classification. In Proc. of NZCSRS 08: 2008 New Zealand Computer Science Research Student Conference. pp 143—150. 2008.

Journal Daniel Kuen Seong Su, Victoria Siew Yen Yee and Jesse Read. Exploring Text-based and Graphical-based Usable Interfaces for Mobile Chat Systems. eMinds International Journal on Human-Computer Interaction. Vol. 1(3). pp 37-53. 2007.

Thesis Jesse Read. Filtering Spam with Machine Learning. Bachelor Thesis. University of Waikato, Hamilton, New Zealand. 2005.