{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T01:51:10Z","timestamp":1772934670285,"version":"3.50.1"},"reference-count":45,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,8]]},"DOI":"10.1109\/bigdata66926.2025.11400965","type":"proceedings-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:57:57Z","timestamp":1772830677000},"page":"3960-3968","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Interpretability in Self-Training with Tsetlin Machines for Mitigating Noisy Pseudo-Labels"],"prefix":"10.1109","author":[{"given":"Jiechao","family":"Gao","sequence":"first","affiliation":[{"name":"Stanford University,Center for SDGC,Stanford,United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohan Kumar","family":"Yadav","sequence":"additional","affiliation":[{"name":"Independent Researcher,Oslo,Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyuan","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Toronto,Department of Computer Science,Toronto,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"Stanford University,Center for SDGC,Stanford,United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","article-title":"Virtual adversarial training: A regularization method for supervised and semi-supervised learning","volume":"41","author":"Miyato","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"1908","DOI":"10.1109\/ICIP.2016.7532690","article-title":"Mutual exclusivity loss for semi-supervised deep learning","volume-title":"2016 IEEE International Conference on Image Processing (ICIP)","author":"Sajjadi","year":"2016"},{"key":"ref3","volume-title":"Temporal ensembling for semi-supervised learning","volume":"abs\/1610.02242","author":"Laine","year":"2016"},{"key":"ref4","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Neural Information Processing Systems","author":"Tarvainen","year":"2017"},{"key":"ref5","article-title":"Unsupervised data augmentation for consistency training","volume-title":"Learning","author":"Xie","year":"2019"},{"key":"ref6","doi-asserted-by":"crossref","DOI":"10.3115\/v1\/D14-1181","article-title":"Convolutional neural networks for sentence classification","volume-title":"Conference on Empirical Methods in Natural Language Processing","author":"Kim","year":"2014"},{"key":"ref7","article-title":"Character-level convolutional networks for text classification","volume-title":"Neural Information Processing Systems","author":"Zhang","year":"2015"},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D15-1167","article-title":"Document modeling with gated recurrent neural network for sentiment classification","volume-title":"Conference on Empirical Methods in Natural Language Processing","author":"Tang","year":"2015"},{"key":"ref9","volume-title":"Mixmatch: A holistic approach to semi-supervised learning","volume":"abs\/1905.02249","author":"Berthelot","year":"2019"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neunet.2021.10.008","article-title":"Interpolation consistency training for semi-supervised learning","volume":"145","author":"Verma","year":"2019","journal-title":"Neural networks: the official journal of the International Neural Network Society"},{"key":"ref11","article-title":"Semisupervised learning with normalizing flows","volume-title":"International Conference on Machine Learning","author":"Izmailov","year":"2019"},{"key":"ref12","first-page":"10684","article-title":"Selftraining with noisy student improves imagenet classification","volume-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Xie","year":"2019"},{"key":"ref13","article-title":"Peer loss functions: Learning from noisy labels without knowing noise rates","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Liu","year":"2020"},{"key":"ref14","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","author":"Lee","year":"2013","journal-title":"ICML 2013 Workshop: Challenges in Representation Learning (WREPL)"},{"key":"ref15","volume-title":"Understanding deep learning requires rethinking generalization","volume":"abs\/1611.03530","author":"Zhang","year":"2016"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2018","journal-title":"Nature Machine Intelligence"},{"key":"ref17","article-title":"Attention is not explanation","volume-title":"North American Chapter of the Association for Computational Linguistics","author":"Jain","year":"2019"},{"key":"ref18","volume-title":"The tsetlin machine - a game theoretic bandit driven approach to optimal pattern recognition with propositional logic","volume":"abs\/1804.01508","author":"Granmo","year":"2018"},{"key":"ref19","article-title":"Massively parallel and asynchronous tsetlin machine architecture supporting almost constant-time scaling","volume-title":"International Conference on Machine Learning","author":"Abeyrathna","year":"2020"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"6072","DOI":"10.1109\/TPAMI.2022.3203150","article-title":"On the convergence of tsetlin machines for the xor operator","volume":"45","author":"Jiao","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref21","article-title":"Humanlevel interpretable learning for aspect-based sentiment analysis","volume-title":"AAAI Conference on Artificial Intelligence","author":"Yadav","year":"2021"},{"key":"ref22","volume-title":"Variational pretraining for semi-supervised text classification","volume":"abs\/1906.02242","author":"Gururangan","year":"2019"},{"key":"ref23","doi-asserted-by":"crossref","DOI":"10.3115\/v1\/P15-1161","article-title":"Weakly supervised role identification in teamwork interactions","volume-title":"Annual Meeting of the Association for Computational Linguistics","author":"Yang","year":"2015"},{"key":"ref24","volume-title":"Mixtext: Linguisticallyinformed interpolation of hidden space for semi-supervised text classification","volume":"abs\/2004.12239","author":"Chen","year":"2020"},{"key":"ref25","article-title":"Adversarial training methods for semi-supervised text classification","volume-title":"Machine Learning","author":"Miyato","year":"2016"},{"key":"ref26","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/N19-1364","article-title":"Let\u2019s make your request more persuasive: Modeling persuasive strategies via semi-supervised neural nets on crowdfunding platforms","volume-title":"North American Chapter of the Association for Computational Linguistics","author":"Yang","year":"2019"},{"key":"ref27","volume-title":"Semi-supervised sequence modeling with cross-view training","volume":"abs\/1809.08370","author":"Clark","year":"2018"},{"key":"ref28","volume-title":"Uncertaintyaware self-training for text classification with few labels","volume":"abs\/2006.15315","author":"Mukherjee","year":"2020"},{"key":"ref29","volume-title":"A multi-stage semi-supervised improved deep embedded clustering (ms-ssidec) method for bearing fault diagnosis under the situation of insufficient labeled samples","volume":"abs\/2109.13521","author":"Sun","year":"2021"},{"key":"ref30","article-title":"Selftuning for data-efficient deep learning","volume-title":"International Conference on Machine Learning","author":"Wang","year":"2021"},{"key":"ref31","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v36i10.21391","article-title":"Contrast-enhanced semi-supervised text classification with few labels","volume-title":"AAAI Conference on Artificial Intelligence","author":"Tsai","year":"2022"},{"key":"ref32","volume-title":"Robustness to adversarial perturbations in learning from incomplete data","volume":"abs\/1905.13021","author":"Najafi","year":"2019"},{"key":"ref33","volume-title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","volume":"abs\/2001.07685","author":"Sohn","year":"2020"},{"key":"ref34","article-title":"The new york times annotated corpus","author":"Sandhaus","year":"2008","journal-title":"Linguistic Data Consortium"},{"key":"ref35","article-title":"Discriminative topic mining via category-name guided text embedding","volume-title":"Proceedings of The Web Conference 2020","author":"Meng","year":"2019"},{"key":"ref36","first-page":"167","article-title":"Dbpedia - a large-scale, multilingual knowledge base extracted from wikipedia","volume":"6","author":"Lehmann","year":"2015","journal-title":"Semantic Web"},{"key":"ref37","article-title":"Learning word vectors for sentiment analysis","volume-title":"Annual Meeting of the Association for Computational Linguistics","author":"Maas","year":"2011"},{"key":"ref38","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","volume-title":"North American Chapter of the Association for Computational Linguistics","author":"Devlin","year":"2019"},{"issue":"1","key":"ref39","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/ACVMOT.2005.107","article-title":"Semi-supervised self-training of object detection models","author":"Rosenberg","year":"2005","journal-title":"2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV\/MOTION\u201905) - Volume 1"},{"key":"ref40","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2023.acl-long.904","article-title":"Prototype-guided pseudo labeling for semi-supervised text classification","volume-title":"Annual Meeting of the Association for Computational Linguistics","author":"Yang","year":"2023"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26260"},{"key":"ref42","volume-title":"Tsetlin machine embedding: Representing words using logical expressions","volume":"abs\/2301.00709","author":"Bhattarai","year":"2023"},{"key":"ref43","volume-title":"Explainable tsetlin machine framework for fake news detection with credibility score assessment","volume":"abs\/2105.09114","author":"Bhattarai","year":"2021"},{"key":"ref44","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2021.blackboxnlp-1.19","article-title":"Enhancing interpretable clauses semantically using pretrained word representation","volume-title":"BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP","author":"Yadav","year":"2021"},{"key":"ref45","article-title":"Robustness to spurious correlations in text classification via automatically generated counterfactuals","volume-title":"AAAI Conference on Artificial Intelligence","author":"Wang","year":"2020"}],"event":{"name":"2025 IEEE International Conference on Big Data (BigData)","location":"Macau, China","start":{"date-parts":[[2025,12,8]]},"end":{"date-parts":[[2025,12,11]]}},"container-title":["2025 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11400704\/11400712\/11400965.pdf?arnumber=11400965","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T07:14:17Z","timestamp":1772867657000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11400965\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/bigdata66926.2025.11400965","relation":{},"subject":[],"published":{"date-parts":[[2025,12,8]]}}}