Fetching the paper…
Reading the bibliography…
Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream.
1904
Earlier work this paper cites.
1906
Earlier work this paper cites.
Friedman M (1940) A comparison of alternative tests of significance for the problem of m m rankings. The Annals of Mathematical Statistics 11:86–92. 10.1214/aoms/1177731944
1940
Earlier work this paper cites.
Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bulletin 1:80. 10.2307/3001968
1945
Earlier work this paper cites.
Shannon E (1948) A mathematical theory of communication. The Bell System Technical Journal
1948
Earlier work this paper cites.
Rosenblatt F (1958) The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65:386–408. 10.1037/h0042519
1958
Earlier work this paper cites.
Kiefer J (1959) Optimum experimental designs. Journal of the Royal Statistical Society Series B (Methodological) URL https://www.jstor.org/stable/2983802
1959
Earlier work this paper cites.
Steel RGD (1959) A multiple comparison sign test: Treatments versus control. Journal of the American Statistical Association 54:767. 10.2307/2282500
1959
Earlier work this paper cites.
Hodges J, Lehmann E (1962) Rank methods for combination of independent experiments in analysis of variance. The Annals of Mathematical Statistics
1962
Earlier work this paper cites.
Karlin S, Studden WJ (1966) Optimal experimental designs. The Annals of Mathematical Statistics 37:783–815. URL https://www.jstor.org/stable/2238570
1966
Earlier work this paper cites.
John RCS, Draper NR (1975) D-optimality for regression designs: A review. Technometrics 17:15–23. 10.1080/00401706.1975.10489266
1975
Earlier work this paper cites.
Quade D (1979) Using weighted rankings in the analysis of complete blocks with additive block effects. Journal of the American Statistical Association 74:680. 10.2307/2286991
1979
Earlier work this paper cites.
Iman RL, Davenport JM (1980) Approximations of the critical region of the fbietkan statistic. Communications in Statistics - Theory and Methods 9:571–595. 10.1080/03610928008827904
1980
Earlier work this paper cites.
Freeman PR (1983) The secretary problem and its extensions: A review. International Statistical Review 51:189–206. URL https://www.jstor.org/stable/1402748
1983
Earlier work this paper cites.
Baum E, Lang K (1992) Query learning can work poorly when a human oracle is used. Proceedings of the IEEE International Joint Conference on Neural Networks
1992
Earlier work this paper cites.
Seung HS, Opper M, Sompolinsky H (1992) Query by committee. Proceedings of the fifth annual workshop on Computational learning theory - COLT ’92 pp 287–294. 10.1145/130385.130417
1992
Earlier work this paper cites.
Cohn DA, Ghahramani Z, Jordan MI (1996) Active learning with statistical models. Journal of Artiicial Intelligence Research 4:129–145. 10.1613/jair.295
1996
Earlier work this paper cites.
Freund Y, Seung HS, Shamir E, et al (1997) Selective sampling using the query by committee algorithm. Machine Learning 28:133–168. 10.1023/a:1007330508534
1997
Earlier work this paper cites.
Pitman J, Yor M (1997) The two-parameter poisson-dirichlet distribution derived from a stable subordinator. The Annals of Probability 25. URL https://www.jstor.org/stable/20680193
1997
Earlier work this paper cites.
Mammen E, Tsybakov AB (1999) Smooth discrimination analysis. The Annals of Statistics 27. 10.1214/aos/1017939240
1999
Earlier work this paper cites.
Hulten G, Spencer L, Domingos P (2001) Mining time-changing data streams. Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’01 pp 97–106. 10.1145/502512.502529
2001
Earlier work this paper cites.
Minka TP (2001) A family of algorithms for approximate bayesian inference. Thesis URL https://hd.media.mit.edu/tech-reports/TR-533.pdf
2001
Earlier work this paper cites.
Polikar R, Upda L, Upda S, et al (2001) Learn++: an incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 31:497–508. 10.1109/5326.983933
2001
Earlier work this paper cites.
Roy N, Mccallum A (2001) Toward optimal active learning through sampling estimation of error reduction. Proceedings of the Eighteenth International Conference on Machine Learning URL https://dl.acm.org/doi/10.5555/645530.655646
2001
Earlier work this paper cites.
Asprey S, Macchietto S (2002) Designing robust optimal dynamic experiments. Journal of Process Control 12:545–556. 10.1016/S0959-1524(01)00020-8
2002
Earlier work this paper cites.
Tong S, Koller D (2002) Support vector machine active learning with applications to text classification. The Journal of Machine Learning Research 2. 10.1162/153244302760185243
2002
Earlier work this paper cites.
Cesa-Bianchi N, Gentile C, Zaniboni L (2004) Worst-case analysis of selective sampling for linear-threshold algorithms. Advances in Neural Information Processing Systems URL https://proceedings.neurips.cc/paper_files/paper/2004/hash/92426b262d11b0ade77387cf8416e153-Abstract.html
2004
Earlier work this paper cites.
Gama J, Medas P, Castillo G, et al (2004) Learning with drift detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3171:286–295. 10.1007/978-3-540-28645-5_29
2004
Earlier work this paper cites.
Kraskov A, Stögbauer H, Grassberger P (2004) Estimating mutual information. Physical Review E 69:066138. 10.1103/PhysRevE.69.066138
2004
Earlier work this paper cites.
Nguyen HT, Smeulders A (2004) Active learning using pre-clustering. Proceedings of the twenty-first international conference on Machine learning https://doi.org/10.1145/1015330.1015349
2004
Earlier work this paper cites.
Shilton A, Palaniswami M, Ralph D, et al (2005) Incremental training of support vector machines. IEEE Transactions on Neural Networks 16:114–131. 10.1109/TNN.2004.836201
2004
Earlier work this paper cites.
Tsybakov AB (2004) Optimal aggregation of classifiers in statistical learning. The Annals of Statistics 10.1214/aos/1079120131
2004
Earlier work this paper cites.
Zhang T (2004) Statistical behavior and consistency of classification methods based on convex risk minimization. The Annals of Statistics 32. 10.1214/aos/1079120130
2004
Earlier work this paper cites.
Bordes A, Ertekin S, Weston J, et al (2005) Fast kernel classifiers with online and active learning. The Journal of Machine Learning Research 6. URL https://jmlr.csail.mit.edu/papers/v6/bordes05a.html
2005
Earlier work this paper cites.
Dasgupta S, Kalai AT, Monteleoni C (2005) Analysis of perceptron-based active learning. COLT ’05 - International Conference on Computational Learning Theory pp 249–263. 10.1007/11503415_17
2005
Earlier work this paper cites.
Hua J, Xiong Z, Lowey J, et al (2005) Optimal number of features as a function of sample size for various classification rules. Bioinformatics 21:1509–1515. 10.1093/bioinformatics/bti171
2005
Earlier work this paper cites.
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: Theory and applications. Neurocomputing 70:489–501. 10.1016/j.neucom.2005.12.126
2005
Earlier work this paper cites.
Cesa-Bianchi N, Lugosi G (2006) Prediction, Learning, and Games. Cambridge University Press, 10.1017/CBO9780511546921
2006
Earlier work this paper cites.
Cesa-Bianchi N, Gentile C, Zaniboni L (2006) Worst-case analysis of selective sampling for linear classification. The Journal of Machine Learning Research 7. URL https://www.jmlr.org/papers/volume7/cesa-bianchi06b/cesa-bianchi06b.pdf
2006
Earlier work this paper cites.
Crammer K, Dekel O, Keshet J, et al (2006) Online passive-aggressive algorithms. The Journal of Machine Learning Research URL https://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf
2006
Earlier work this paper cites.
Hoffmann H (2007) Kernel pca for novelty detection. Pattern Recognition 40:863–874. 10.1016/j.patcog.2006.07.009
2006
Earlier work this paper cites.
Roth D, Small K (2006) Margin-based active learning for structured output spaces. Machine Learning: ECML 2006 pp 413–424. 10.1007/11871842_40
2006
Earlier work this paper cites.
Taylor G, Hinton G, Roweis S (2006) Modeling human motion using binary latent variables. Advances in Neural Information Processing Systems 19 (NIPS 2006) URL https://papers.nips.cc/paper_files/paper/2006/hash/1091660f3dff84fd648efe31391c5524-Abstract.html
2006
Earlier work this paper cites.
Tsymbal A, Pechenizkiy M, Cunningham P, et al (2008) Dynamic integration of classifiers for handling concept drift. Information Fusion 9:56–68. 10.1016/j.inffus.2006.11.002
2006
Earlier work this paper cites.
Yu K, Bi J, Tresp V (2006) Active learning via transductive experimental design. Proceedings of the 23rd International Conference on Machine Learning https://doi.org/10.1145/1143844.1143980
2006
Earlier work this paper cites.
Zheng Z, Padmanabhan B (2006) Selectively acquiring customer information: A new data acquisition problem and an active learning-based solution. Management Science 52(5):697–712
2006
Earlier work this paper cites.
Balcan MF, Broder A, Zhang T (2007) Margin based active learning. COLT - 23th Conference on Learning Theory 4739. https://doi.org/10.1007/978-3-540-72927-3_5
2007
Earlier work this paper cites.
Bifet A, Gavaldà R (2007) Learning from time-changing data with adaptive windowing. Proceedings of the 2007 SIAM International Conference on Data Mining pp 443–448. 10.1137/1.9781611972771.42
2007
Earlier work this paper cites.
Burbidge R, Rowland JJ, King RD (2007) Active learning for regression based on query by committee. 8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007 10.1007/978-3-540-77226-2_22
2007
Earlier work this paper cites.
Donmez P, Carbonell J, Bennet P (2007) Dual strategy active learning. 18th European Conference on Machine Learning, ECML 2007 4701. 10.1007/978-3-540-74958-5_14
2007
Earlier work this paper cites.
Fortuna L, Graziani S, Rizzo A, et al (2007) Soft sensors for monitoring and control of industrial processes, vol 22. Springer, URL https://link.springer.com/book/10.1007/978-1-84628-480-9
2007
Earlier work this paper cites.
McSherry F, Talwar K (2007) Mechanism design via differential privacy. 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS’07) pp 94–103. 10.1109/FOCS.2007.41
2007
Earlier work this paper cites.
Naranjo JE, Sotelo MA, Gonzalez C, et al (2007) Using fuzzy logic in automated vehicle control. IEEE intelligent systems 22(1):36–45
2007
Earlier work this paper cites.
Sculley D (2007) Online active learning methods for fast label efficient spam filtering. Proceedings of the Fourth Conference on Email and AntiSpam
2007
Earlier work this paper cites.
Zhu X, Zhang P, Lin X, et al (2007) Active learning from data streams. Proceedings - IEEE International Conference on Data Mining, ICDM pp 757–762. 10.1109/ICDM.2007.101
2007
Earlier work this paper cites.
2008
Earlier work this paper cites.
2008
Earlier work this paper cites.
Long J, Yin J, Zhao W, et al (2008) Graph-based active learning based on label propagation. MDAI 2008: Modeling Decisions for Artificial Intelligence pp 179–190. 10.1007/978-3-540-88269-5_17
2008
Earlier work this paper cites.
Sheng VS, Provost F, Ipeirotis PG (2008) Get another label? improving data quality and data mining using multiple, noisy labelers. Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08 p 614. 10.1145/1401890.1401965
2008
Earlier work this paper cites.
Suresh S, Sundararajan N, Saratchandran P (2008) Risk-sensitive loss functions for sparse multi-category classification problems. Information Sciences 178:2621–2638. 10.1016/j.ins.2008.02.009
2008
Earlier work this paper cites.
Bifet A, Gavaldà R (2009) Adaptive learning from evolving data streams. IDA 2009: Advances in Intelligent Data Analysis VIII pp 249–260. 10.1007/978-3-642-03915-7_22
2009
Earlier work this paper cites.
Gama J, Sebastiao R, Rodrigues PP (2009) Issues in evaluation of stream learning algorithms. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 329–338
2009
Earlier work this paper cites.
Joshi AJ, Porikli F, Papanikolopoulos N (2009) Multi-class active learning for image classification. 2009 IEEE Conference on Computer Vision and Pattern Recognition pp 2372–2379. 10.1109/CVPR.2009.5206627
2009
Earlier work this paper cites.
Settles B (2009) Active learning literature survey. Technical Report 1648, University of Wisconsin-Madison Department of Computer Sciences URL https://burrsettles.com/pub/settles.activelearning.pdf
2009
Earlier work this paper cites.
Taylor G, Hinton G (2009) Factored conditional restricted boltzmann machines for modeling motion style. Proceedings of the 26th International Conference on Machine Learning, Montreal, Canada, 2009 https://doi.org/10.1145/1553374.1553505
2009
Earlier work this paper cites.
Škrjanc I (2009) Confidence interval of fuzzy models: An example using a waste-water treatment plant. Chemometrics and Intelligent Laboratory Systems 96:182–187. 10.1016/j.chemolab.2009.01.009
2009
Earlier work this paper cites.
Audibert JY, Munos R (2010) Best arm identification in multi-armed bandits. COLT - 23th Conference on Learning Theory URL http://certis.enpc.fr/~audibert/Mes%20articles/COLT10.pdf
2010
Earlier work this paper cites.
Bifet A, Holmes G, Pfahringer B, et al (2010) Moa: Massive online analysis, a framework for stream classification and clustering. In: Proceedings of the first workshop on applications of pattern analysis, PMLR, pp 44–50
2010
Earlier work this paper cites.
Filippi S, Cappe O, Garivier A, et al (2010) Parametric bandits: The generalized linear case. Advances in Neural Information Processing Systems 23 (NIPS 2010) URL https://papers.nips.cc/paper_files/paper/2010/hash/c2626d850c80ea07e7511bbae4c76f4b-Abstract.html
2010
Earlier work this paper cites.
Galvanin F (2010) Optimal model-based design of experiments in dynamic systems: novel techniques and unconventional applications. Thesis URL https://hdl.handle.net/11577/3427095
2010
Earlier work this paper cites.
Jin R, Hoi S, Yang T (2010) Online multiple kernel learning: Algorithms and mistake bounds. Proceedings of the 21st International Conference on Algorithmic Learning Theory 10.1007/978-3-642-16108-7_31
2010
Earlier work this paper cites.
Bisgaard S, Kulahci M (2011) Time series analysis and forecasting by example. John Wiley & Sons
2011
Earlier work this paper cites.
Chu W, Zinkevich M, Li L, et al (2011) Unbiased online active learning in data streams. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’11 p 195. 10.1145/2020408.2020444
2011
Earlier work this paper cites.
Joyce JM (2011) Kullback-leibler divergence. 10.1007/978-3-642-04898-2_327
2011
Earlier work this paper cites.
Kurlej B, Woźniak M (2011) Learning curve in concept drift while using active learning paradigm. 10.1007/978-3-642-23857-4_13
2011
Earlier work this paper cites.
Lughofer E (2011) Evolving Fuzzy Systems – Methodologies, Advanced Concepts and Applications, vol 266. Springer Berlin Heidelberg, 10.1007/978-3-642-18087-3
2011
Earlier work this paper cites.
Wang L (2011) Smoothness, disagreement coefficient, and the label complexity of agnostic active learning. The Journal of Machine Learning Research URL https://www.jmlr.org/papers/volume12/wang11b/wang11b.pdf
2011
Earlier work this paper cites.
Lieber D, Konrad B, Deuse J, et al (2012) Sustainable interlinked manufacturing processes through real-time quality prediction. In: Leveraging Technology for a Sustainable World: Proceedings of the 19th CIRP Conference on Life Cycle Engineering, University of California at Berkeley, Berkeley, USA, May 23-25, 2012, Springer, pp 393–398
2012
Earlier work this paper cites.
Loy CC, Hospedales TM, Xiang T, et al (2012) Stream-based joint exploration-exploitation active learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp 1560–1567. 10.1109/CVPR.2012.6247847
2012
Earlier work this paper cites.
Lughofer E (2012) Single-pass active learning with conflict and ignorance. Evolving Systems 3:251–271. 10.1007/s12530-012-9060-7
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
Montgomery DC (2012) Design and Analysis of Experiments. John Wiley & Sons, Inc., 10.1002/9781118147634
2012
Earlier work this paper cites.
Zliobaite I, Bifet A, Pfahringer B, et al (2014) Active learning with drifting streaming data. IEEE Transactions on Neural Networks and Learning Systems 25:27–39. 10.1109/TNNLS.2012.2236570
2012
Cited alongside, same era.
Bouchachia A, Vanaret C (2014) Gt2fc: An online growing interval type-2 self-learning fuzzy classifier. IEEE Transactions on Fuzzy Systems 22:999–1018. 10.1109/TFUZZ.2013.2279554
2013
Cited alongside, same era.
Cai W, Zhang Y, Zhou J (2013) Maximizing expected model change for active learning in regression. Proceedings - IEEE International Conference on Data Mining, ICDM pp 51–60. 10.1109/ICDM.2013.104
2013
Cited alongside, same era.
Duchi JC, Jordan MI, Wainwright MJ (2013) Local privacy and statistical minimax rates. 2013 IEEE 54th Annual Symposium on Foundations of Computer Science pp 429–438. 10.1109/FOCS.2013.53
2013
Cited alongside, same era.
Fiez T, Jain L, Jamieson K, et al (2019) Sequential experimental design for transductive linear bandits. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) URL https://proceedings.neurips.cc/paper_files/paper/2019/file/8ba6c657b03fc7c8dd4dff8e45defcd2-Paper.pdf
2019
Later among the works it cites.
Ge D, Zeng XJ (2020) Learning data streams online — an evolving fuzzy system approach with self-learning/adaptive thresholds. Information Sciences 507:172–184. 10.1016/j.ins.2019.08.036
2019
Later among the works it cites.
Gouk H, Pfahringer B, Frank E (2019) Stochastic gradient trees. URL http://proceedings.mlr.press/v101/gouk19a/gouk19a.pdf
2019
Later among the works it cites.
Mohamad S, Sayed-Mouchaweh M, Bouchachia A (2020) Online active learning for human activity recognition from sensory data streams. Neurocomputing 390:341–358. 10.1016/j.neucom.2019.08.092
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ebbinghaus H (2013) Memory: A contribution to experimental psychology. Annals of Neurosciences 20. 10.5214/ans.0972.7531.200408
2013
Cited alongside, same era.
Ferdowsi Z, Ghani R, Settimi R (2013) Online active learning with imbalanced classes. 2013 IEEE 13th International Conference on Data Mining pp 1043–1048. 10.1109/ICDM.2013.12
2013
Cited alongside, same era.
Feuz KD, Cook DJ (2013) Real-time annotation tool (rat). In: Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence
2013
Cited alongside, same era.
Fu Y, Zhu X, Li B (2013) A survey on instance selection for active learning. Knowledge and Information Systems 35:249–283. 10.1007/s10115-012-0507-8
2013
Cited alongside, same era.
Gama J, Sebastiao R, Rodrigues PP (2013) On evaluating stream learning algorithms. Machine learning 90:317–346
2013
Cited alongside, same era.
Hoi SCH, Jin R, Zhao P, et al (2013) Online multiple kernel classification. Machine Learning 90:289–316. 10.1007/s10994-012-5319-2
2013
Cited alongside, same era.
Ienco D, Bifet A, Zliobaite, et al (2013) Clustering based active learning for evolving data streams. 16th International Conference on Discovery Science 10.1007/978-3-642-40897-7_6
2013
Cited alongside, same era.
2013
Cited alongside, same era.
Rudovic O, Zhang M, Schuller B, et al (2019) Multi-modal active learning from human data: A deep reinforcement learning approach. 2019 International Conference on Multimodal Interaction pp 6–15. 10.1145/3340555.3353742
2019
Later among the works it cites.
Vahdat A, Belbahri M, Nia VP (2019) Active learning for high-dimensional binary features. 15th International Conference on Network and Service Management (CNSM) URL https://www.computer.org/csdl/proceedings-article/cnsm/2019/09012676/1hQr3hscsJG
2019
Later among the works it cites.
Wassermann S, Cuvelier T, Casas P (2019) Ral-improving stream-based active learning by reinforcement learning. URL https://hal.archives-ouvertes.fr/hal-02265426
2019
Later among the works it cites.
Wu Y, Chen Y, Wang L, et al (2019) Large scale incremental learning. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 10.1109/CVPR.2019.00046
2019
Later among the works it cites.
Gemaque RN, Costa AFJ, Giusti R, et al (2020) An overview of unsupervised drift detection methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10. 10.1002/widm.1381
2020
Later among the works it cites.
Haussmann E, Fenzi M, Chitta K, et al (2020) Scalable active learning for object detection. Proceedings 31st IEEE Intelligent Vehicles Symposium (IV) https://doi.org/10.1109/IV47402.2020.9304793
2020
Later among the works it cites.
Jedra Y, Proutiere A (2020) Optimal best-arm identification in linear bandits. 34th Conference on Neural Information Processing Systems (NeurIPS 2020) URL https://proceedings.neurips.cc/paper/2020/file/7212a6567c8a6c513f33b858d868ff80-Paper.pdf
2020
Later among the works it cites.
Kumar P, Gupta A (2020) Active learning query strategies for classification, regression, and clustering: A survey. Journal of Computer Science and Technology 35:913–945. 10.1007/s11390-020-9487-4
2020
Later among the works it cites.
Min F, Zhang SM, Ciucci D, et al (2020) Three-way active learning through clustering selection. International Journal of Machine Learning and Cybernetics 11:1033–1046. 10.1007/s13042-020-01099-2
2020
Later among the works it cites.
Prabhu V, Chandrasekaran A, Saenko K, et al (2020) Active domain adaptation via clustering uncertainty-weighted embeddings. URL https://github.com/virajprabhu/CLUE
2020
Later among the works it cites.
Réda C, Kaufmann E, Delahaye-Duriez A (2020) Machine learning applications in drug development. Computational and structural biotechnology journal 18:241–252
2020
Later among the works it cites.
Schmidt S, Rao Q, Tatsch J, et al (2020) Advanced active learning strategies for object detection. 2020 IEEE Intelligent Vehicles Symposium (IV) pp 871–876. 10.1109/IV47402.2020.9304565
2020
Later among the works it cites.
Shah K, Manwani N (2020) Online active learning of reject option classifiers. Proceedings of the AAAI Conference on Artificial Intelligence 34:5652–5659. 10.1609/aaai.v34i04.6019
2020
Later among the works it cites.
Yuan M, Lin HT, Boyd-Graber J (2020) Cold-start active learning through self-supervised language modeling. roceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) 10.18653/v1/2020.emnlp-main.637
2020
Later among the works it cites.
Zhang H, Liu W, Sun L, et al (2020a) Analyzing network traffic for protocol identification: An ensemble online active learning method. Proceedings - 2020 6th International Conference on Big Data and Information Analytics, BigDIA 2020 pp 167–172. 10.1109/BigDIA51454.2020.00035
2020
Later among the works it cites.
Zhang H, Ravi SS, Davidson I (2020b) A graph-based approach for active learning in regression. Proceedings of the 2020 SIAM International Conference on Data Mining (SDM) https://doi.org/10.1137/1.9781611976236.32
2020
Later among the works it cites.
Zhang H, Liu W, Liu Q (2022) Reinforcement online active learning ensemble for drifting imbalanced data streams. IEEE Transactions on Knowledge and Data Engineering 34:3971–3983. 10.1109/TKDE.2020.3026196
2020
Later among the works it cites.
Zwanka RJ, Buff C (2021) Covid-19 generation: A conceptual framework of the consumer behavioral shifts to be caused by the covid-19 pandemic. Journal of International Consumer Marketing 33:58–67. 10.1080/08961530.2020.1771646
2020
Later among the works it cites.
Zyblewski P, Ksieniewicz P, Woźniak M (2020) Combination of active and random labeling strategy in the non-stationary data stream classification. In: International Conference on Artificial Intelligence and Soft Computing, Springer, pp 576–585
2020
Later among the works it cites.
Avadhanula V, Colini Baldeschi R, Leonardi S, et al (2021) Stochastic bandits for multi-platform budget optimization in online advertising. In: Proceedings of the Web Conference 2021, pp 2805–2817
2021
Later among the works it cites.
Cacciarelli D, Boresta M (2021) What drives a donor? a machine learning‐based approach for predicting responses of nonprofit direct marketing campaigns. International Journal of Nonprofit and Voluntary Sector Marketing 10.1002/nvsm.1724
2021
Later among the works it cites.
2021
Later among the works it cites.
Chae J, Hong S (2021) Stream-based active learning with multiple kernels. 2021 International Conference on Information Networking (ICOIN) pp 718–722. 10.1109/ICOIN50884.2021.9333940
2021
Later among the works it cites.
2021
Later among the works it cites.
Desalvo G, Gentile C, Thune TS (2021) Online active learning with surrogate loss functions. Advances in Neural Information Processing Systems 34 (NeurIPS 2021) URL https://proceedings.neurips.cc/paper/2021/hash/c1619d2ad66f7629c12c87fe21d32a58-Abstract.html
2021
Later among the works it cites.
Fontaine X, Perrault P, Valko M, et al (2021) Online a-optimal design and active linear regression. URL http://proceedings.mlr.press/v139/fontaine21a/fontaine21a.pdf
2021
Later among the works it cites.
Hanneke S, Yang L (2021) Toward a general theory of online selective sampling: Trading off mistakes and queries. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics URL https://proceedings.mlr.press/v130/hanneke21a.html
2021
Later among the works it cites.
Hoang TN, Hong S, Xiao C, et al (2021) Aid: Active distillation machine to leverage pre-trained black-box models in private data settings. Proceedings of the Web Conference 2021 pp 3569–3581. 10.1145/3442381.3449944
2021
Later among the works it cites.
Hoi SC, Sahoo D, Lu J, et al (2021) Online learning: A comprehensive survey. Neurocomputing 459:249–289. 10.1016/j.neucom.2021.04.112
2021
Later among the works it cites.
Li A, Boyd A, Smyth P, et al (2021) Detecting and adapting to irregular distribution shifts in bayesian online learning. 35th Conference on Neural Information Processing Systems (NeurIPS 2021) URL https://papers.nips.cc/paper/2021/file/362387494f6be6613daea643a7706a42-Paper.pdf
2021
Later among the works it cites.
Liu S, Xue S, Wu J, et al (2021) Online active learning for drifting data streams. IEEE Transactions on Neural Networks and Learning Systems 10.1109/TNNLS.2021.3091681
2021
Later among the works it cites.
Menard P, Domingues OD, Jonsson A, et al (2021) Fast active learning for pure exploration in reinforcement learning. Proceedings of the 38th International Conference on Machine Learning URL http://proceedings.mlr.press/v139/menard21a/menard21a-supp.pdf
2021
Later among the works it cites.
Nixon C, Sedky M, Hassan M (2021) Reviews in online data stream and active learning for cyber intrusion detection-a systematic literature review. In: 2021 Sixth International Conference on Fog and Mobile Edge Computing (FMEC), IEEE, pp 1–6
2021
Later among the works it cites.
Qin J, Wang C, Zou Q, et al (2021) Active learning with extreme learning machine for online imbalanced multiclass classification. Knowledge-Based Systems 231:107385. 10.1016/j.knosys.2021.107385
2021
Later among the works it cites.
Ruan Y, Yang J, Zhou Y (2020) Linear bandits with limited adaptivity and learning distributional optimal design. STOC 2021: Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing https://doi.org/10.1145/3406325.3451004
2021
Later among the works it cites.
Suzuki K, Sunagawa T, Sasaki T, et al (2021) Annotation cost reduction of stream-based active learning by automated weak labeling using a robot arm. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp 9000–9007. 10.1109/IROS51168.2021.9636355
2021
Later among the works it cites.
Suárez-Cetrulo AL, Kumar A, Miralles-Pechuán L (2021) Modelling the covid-19 virus evolution with incremental machine learning. 29th Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2021 URL https://ceur-ws.org/Vol-3105/paper1.pdf
2021
Later among the works it cites.
Wu R, Guo C, Su Y, et al (2021) Online adaptation to label distribution shift. 35th Conference on Neural Information Processing Systems (NeurIPS 2021) URL https://www.kaggle.com/Cornell-University/arxiv
2021
Later among the works it cites.
Zhou C, Ma X, Michel P, et al (2021) Examining and combating spurious features under distribution shift. Proceedings of the 38 th International Conference on Machine Learning URL https://github.com/violet-zct/
2021
Later among the works it cites.
Azizi MJ, Kveton B, Ghavamzadeh M (2022) Fixed-budget best-arm identification in structured bandits. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22) URL https://www.ijcai.org/proceedings/2022/0388.pdf
2022
Later among the works it cites.
2022
Later among the works it cites.
Cacciarelli D, Kulahci M (2022) A novel fault detection and diagnosis approach based on orthogonal autoencoders. Computers & Chemical Engineering 163:107853. 10.1016/j.compchemeng.2022.107853
2022
Later among the works it cites.
2022
Later among the works it cites.
Cacciarelli D, Kulahci M, Tyssedal JS (2022b) Stream-based active learning with linear models. Knowledge-Based Systems 254:109664. 10.1016/j.knosys.2022.109664
2022
Later among the works it cites.
2022
Later among the works it cites.
Gu X, Han J, Shen Q, et al (2022) Autonomous learning for fuzzy systems: a review. Artificial Intelligence Review 10.1007/s10462-022-10355-6
2022
Later among the works it cites.
Huang B, Salgia S, Zhao Q (2022) Disagreement-based active learning in online settings. IEEE Transactions on Signal Processing 70:1947–1958. 10.1109/TSP.2022.3159388
2022
Later among the works it cites.
Jin Q, Yuan M, Li S, et al (2022) Cold-start active learning for image classification. Information Sciences 616:16–36. 10.1016/j.ins.2022.10.066
2022
Later among the works it cites.
2022
Later among the works it cites.
Lima M, Neto M, Filho TS, et al (2022) Learning under concept drift for regression—a systematic literature review. IEEE Access 10:45410–45429. 10.1109/ACCESS.2022.3169785
2022
Later among the works it cites.
Ren P, Xiao Y, Chang X, et al (2022) A survey of deep active learning. ACM Computing Surveys 54:1–40. 10.1145/3472291
2022
Later among the works it cites.
Riis C, Antunes F, Hüttel FB, et al (2022) Bayesian active learning with fully bayesian gaussian processes. In Proceedings of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) URL https://proceedings.neurips.cc/paper_files/paper/2022/file/4f1fba885f266d87653900fd3045e8af-Paper-Conference.pdf
2022
Later among the works it cites.
Rožanec JM, Trajkova E, Dam P, et al (2022) Streaming machine learning and online active learning for automated visual inspection. IFAC-PapersOnLine 55:277–282. 10.1016/j.ifacol.2022.04.206
2022
Later among the works it cites.
Suárez-Cetrulo AL, Quintana D, Cervantes A (2023) A survey on machine learning for recurring concept drifting data streams. Expert Systems with Applications 213:118934. 10.1016/j.eswa.2022.118934
2022
Later among the works it cites.
Thompson J, Walters WP, Feng JA, et al (2022) Optimizing active learning for free energy calculations. Artificial Intelligence in the Life Sciences 2:100050. 10.1016/j.ailsci.2022.100050
2022
Later among the works it cites.
Tieppo E, dos Santos RR, Barddal JP, et al (2022) Hierarchical classification of data streams: a systematic literature review. Artificial Intelligence Review 55:3243–3282. 10.1007/s10462-021-10087-z
2022
Later among the works it cites.
Wu J, Chen J, Huang D (2022) Entropy-based active learning for object detection with progressive diversity constraint. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 10.1109/CVPR52688.2022.00918
2022
Later among the works it cites.
Aguiar G, Krawczyk B, Cano A (2023) A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework. Machine Learning pp 1–79
2023
Closest in time.
2023
Closest in time.
Beck N, Kothawade S, Shenoy P, et al (2023) Streamline: Streaming active learning for realistic multi-distributional settings. arXiv preprint arXiv:230510643
2023
Closest in time.
Cacciarelli D, Kulahci M (2023) Hidden dimensions of the data: Pca vs autoencoders. Quality Engineering pp 1–10
2023
Closest in time.
Cacciarelli D, Kulahci M, Tyssedal JS (2023) Robust online active learning. Quality and Reliability Engineering International https://doi.org/10.1002/qre.3392 , URL https://onlinelibrary.wiley.com/doi/abs/10.1002/qre.3392 , https://onlinelibrary.wiley.com/doi/pdf/10.1002/qre.3392
2023
Closest in time.
Cheng J, Zheng Z, Guo Y, et al (2023) Active broad learning with multi-objective evolution for data stream classification. Complex & Intelligent Systems pp 1–18
2023
Closest in time.
Fowler K, Kokilepersaud K, Prabhushankar M, et al (2023) Clinical trial active learning. In: The 14th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB)
2023
Closest in time.
Ghiasi S, Pazzi G, Del Grosso C, et al (2023) Combining thermodynamics-based model of the centrifugal compressors and active machine learning for enhanced industrial design optimization. In: 1st Workshop on the Synergy of Scientific and Machine Learning Modeling@ ICML2023
2023
Closest in time.
Gu X, Han J, Shen Q, et al (2023) Autonomous learning for fuzzy systems: a review. Artificial Intelligence Review 56(8):7549–7595
2023
Closest in time.
Halder B, Hasan KA, Amagasa T, et al (2023) Autonomic active learning strategy using cluster-based ensemble classifier for concept drifts in imbalanced data stream. Expert Systems with Applications p 120578
2023
Closest in time.
Lughofer E, Škrjanc I (2023) Online active learning for evolving error feedback fuzzy models within a multi-innovation context. IEEE Transactions on Fuzzy Systems
2023
Closest in time.
Manjah D, Cacciarelli D, Standaert B, et al (2023) Stream-based active distillation for scalable model deployment. Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition (CVPR) Workshops
2023
Closest in time.
Martins VE, Cano A, Junior SB (2023) Meta-learning for dynamic tuning of active learning on stream classification. Pattern Recognition 138:109359
2023
Closest in time.
Saran A, Yousefi S, Krishnamurthy A, et al (2023) Streaming active learning with deep neural networks. In: Krause A, Brunskill E, Cho K, et al (eds) Proceedings of the 40th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol 202. PMLR, pp 30005–30021, URL https://proceedings.mlr.press/v202/saran23a.html
2023
Closest in time.
Woźniak M, Zyblewski P, Ksieniewicz P (2023) Active weighted aging ensemble for drifted data stream classification. Information Sciences 630:286–304
2023
Closest in time.
Yan X, Sarkar M, Lartey B, et al (2023) An online learning framework for sensor fault diagnosis analysis in autonomous cars. IEEE Transactions on Intelligent Transportation Systems
2023
Closest in time.
Yin C, Chen S, Yin Z (2023) Clustering-based active learning classification towards data stream. ACM Transactions on Intelligent Systems and Technology 14(2):1–18
2023
Closest in time.
Zhang K, Liu S, Chen Y (2023) Online active learning framework for data stream classification with density-peaks recognition. IEEE Access 11:27853–27864
2023
Closest in time.
Cerqueira V, Torgo L, Mozetič I (2020) Evaluating time series forecasting models: an empirical study on performance estimation methods. Machine Learning 109:1997–2028. 10.1007/s10994-020-05910-7
2028
Closest in time.
Pham T, Kottke D, Krempl G, et al (2022) Stream-based active learning for sliding windows under the influence of verification latency. Machine Learning 111:2011–2036. 10.1007/s10994-021-06099-z
2036
Closest in time.