Domain Adaptation for Statistical Classifiers

Maximum Likelihood from Incomplete Data Via the EM Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935

Multitask Learning

Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636

Hierarchical Mixtures of Experts and the EM Algorithm

Jordan, M. I., & Jacobs, R. A. (1994) · Neural Computation · doi:10.1162/neco.1994.6.2.181 · Cited by 2,786

An extensive empirical study of feature selection metrics for text classification

Forman, G. (2003) · doi:10.5555/944919.944974 · Cited by 2,400

Nonparametric Statistical Inference

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Maximum Entropy Markov Models for Information Extraction and Segmentation

McCallum, A., Freitag, D., & Pereira, F. C. N. (2000) · Cited by 1,321

Inducing features of random fields

Pietra, S. D., Pietra, V. D., & Lafferty, J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.588021 · Cited by 1,034

Multitask Learning: A Knowledge-Based Source of Inductive Bias

Caruana, R. (1993) · Elsevier eBooks · doi:10.1016/b978-1-55860-307-3.50012-5 · Cited by 753

A Model of Inductive Bias Learning

Baxter, J. (2000) · Cited by 730

One System, Many Domains: Open-Domain Statistical Machine Translation via Feature Augmentation

Clark, J. H., Lavie, A., & Dyer, C. (2018) · Figshare · doi:10.1184/r1/6473591 · Cited by 23

Learning Unbiased Representations via Mutual Information Backpropagation

Ragonesi, R., Volpi, R., Cavazza, J., & Murino, V. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00307 · Cited by 24

A Sensor-Driven Visit Detection System in Older Adults’ Homes: Towards Digital Late-Life Depression Marker Extraction

Schütz, N., Botros, A., Hassen, S. B., Saner, H., Buluschek, P., Urwyler, P., Pais, B., Santschi, V., Gatica-Perez, D., Muri, R. M., et al. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3114595 · Cited by 23

Document sublanguage clustering to detect medical specialty in cross-institutional clinical texts

Doing-Harris, K. M., Patterson, O. V., Igo, S., & Hurdle, J. F. (2013) · doi:10.1145/2512089.2512101 · Cited by 22

Incentive Compatible Privacy-Preserving Data Analysis

Kantarcıoğlu, M., & Jiang, W. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.61 · Cited by 22

Self-taught learning

Ng, A. Y., & Raina, R. (2009) · Cited by 23

Dual Exclusive Attentive Transfer for Unsupervised Deep Convolutional Domain Adaptation in Speech Emotion Recognition

Ocquaye, E. N. N., Mao, Q., Song, H., Xu, G., & Xue, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2924597 · Cited by 22

Unsupervised Part-of-Speech Tagging in the Large

Biemann, C. (2009) · Research on Language and Computation · doi:10.1007/s11168-010-9067-9 · Cited by 22

Relevant knowledge helps in choosing right teacher

Cai, P., Gao, W. G., Zhou, A., & Wong, K. (2011) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2009916.2009935 · Cited by 21

On combining active and transfer learning for medical data classification

Xinyao, T., Du, B., Huang, J., Wang, Z., & Zhang, L. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2017.0524 · Cited by 21

Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation

Panfilov, E., Tiulpin, A., Klein, S., Nieminen, M. T., & Saarakkala, S. S. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00057 · Cited by 21

Word sense disambiguation using OntoNotes

Zhong, Z., Ng, H. T., & Chan, Y. S. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613845 · Cited by 21

NES-TL: Network Embedding Similarity-Based Transfer Learning

Fu, C., Zheng, Y., Liu, Y., Xuan, Q., & Chen, G. (2019) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2019.2942341 · Cited by 21

Feature Selection by Transfer Learning with Linear Regularized Models

Helleputte, T., & Dupont, P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04180-8_52 · Cited by 22

Transportability from Multiple Environments with Limited Experiments

Bareinboim, E., Lee, S., Honavar, V., & Pearl, J. (2013) · eScholarship (California Digital Library) · Cited by 21

Predicting Intentions from Motion: The Subject-Adversarial Adaptation Approach

Zunino, A., Cavazza, J., Volpi, R., Morerio, P., Cavallo, A., Becchio, C., & Murino, V. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01234-9 · Cited by 20

FUZZY BRIDGED REFINEMENT DOMAIN ADAPTATION: LONG-TERM BANK FAILURE PREDICTION

Behbood, V., Lü, J., & Zhang, G. (2013) · International Journal of Computational Intelligence and Applications · doi:10.1142/s146902681350003x · Cited by 20

Knowledge transfer with low-quality data: A feature extraction issue

Quanz, B., Huan, J., & Mishra, M. (2011) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2011.5767917 · Cited by 20

Revisiting Multi-Domain Machine Translation

Pham, M., Crego, J., & Yvon, F. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00351 · Cited by 26

Learning and Domain Adaptation

Mansour, Y. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04414-4_4 · Cited by 20

Domain Adaptation by Constraining Inter-Domain Variability of Latent Feature Representation

Titov, I. (2011) · Cited by 20

Joint discriminative subspace and distribution adaptation for unsupervised domain adaptation

Gholenji, E., & Tahmoresnezhad, J. (2020) · Applied Intelligence · doi:10.1007/s10489-019-01610-5 · Cited by 18

Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model

Xiao, M., & Guo, Y. (2013) · Cited by 19

Recommending ticket resolution using feature adaptation

Zhou, W., Li, T., Shwartz, L., & Grabarnik, G. Y. (2015) · International Conference on Network and Service Management (CNSM) · doi:10.1109/cnsm.2015.7367333 · Cited by 19

Incorporating domain and sentiment supervision in representation learning for domain adaptation

Liu, B., Huang, M., Sun, J., & Zhu, X. (2015) · International Conference on Artificial Intelligence · Cited by 20

Distance metric learning under covariate shift

Cao, B., Ni, X., Sun, J.-T., Wang, G., & Yang, Q. (2011) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · doi:10.5591/978-1-57735-516-8/ijcai11-205 · Cited by 20

DialBERT: A Hierarchical Pre-Trained Model for Conversation DisentanglementPreprint

Li, T., Gu, J.-C., Zhu, X., Liu, Q., Ling, Z.-H., Su, Z., & Wei, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.03760 · Cited by 28

Disentangling Style and Speaker Attributes for TTS Style Transfer

An, X., Soong, F. K., & Xie, L. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3145297 · Cited by 18

Domain Adaptation for Resume Classification Using Convolutional Neural Networks

Sayfullina, L., Malmi, E., Liao, Y.-P., & Jung, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-73013-4_8 · Cited by 18

Domain Adaptation with Twin Support Vector Machines

Xie, X., Sun, S., Chen, H., & Qian, J. (2017) · Neural Processing Letters · doi:10.1007/s11063-017-9775-3 · Cited by 18

Stationary Subspace Analysis as a Generalized Eigenvalue Problem

Hara, S., Kawahara, Y., Washio, T., & von Bünau, P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17537-4_52 · Cited by 18

Adapting Component Analysis

Dorri, F., & Ghodsi, A. (2012) · Proceedings · doi:10.1109/icdm.2012.85 · Cited by 18

Refining Language Models with Compositional ExplanationsPreprint

Yao, H., Chen, Y., Ye, Q., Jin, X., & Ren, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.10415 · Cited by 20

Dependency Parsing with Compression Rules

Gamallo, P. (2015) · doi:10.18653/v1/w15-2214 · Cited by 17

A Sensor-Driven Visit Detection System in Older Adults Homes: Towards Digital Late-Life Depression Marker Extraction

Schütz, N., Botros, A., Ben Hassen, S., Saner, H. E., Buluschek, P., Urwyler, P., Pais, B., Santschi, V., Gática-Pérez, D., Müri, R. M., et al. (2021) · Bern Open Repository and Information System (University of Bern) · Cited by 17

Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics

Pavlović, M., Hajj, G. S. A., Kanduri, C., Pensar, J., Wood, M. E., Sollid, L. M., Greiff, V., & Sandve, G. K. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-023-00781-8 · Cited by 29

Weighted SCL model for adaptation of sentiment classification

Tan, S., & Wang, Y. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.02.106 · Cited by 17

Unsupervised Domain Adaptation with Regularized Domain Instance Denoising

Csurka, G., Chidlovskii, B., Clinchant, S., & Michel, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_37 · Cited by 17

An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis

Geethapriya, A., & Valli, S. (2021) · Information Systems Frontiers · doi:10.1007/s10796-020-10094-5 · Cited by 18

Analogical Proportions: Why They Are Useful in AI

Prade, H. M., & Richard, G. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/621 · Cited by 17

Semantic role labeling for news tweets

Liu, X., Li, K., Han, B., Zhou, M., Jiang, L., Xiong, Z., & Huang, C. (2012) · Cited by 17

A Comparative Study of Cross-Lingual Sentiment Classification

Wan, X. (2012) · Web Intelligence · doi:10.5555/2457524.2457659 · Cited by 17

Deep Transfer Learning Ensemble for Classification

Kandaswamy, C., Silva, L. M., Alexandre, L. A., & Santos, J. M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19258-1_29 · Cited by 18

Domain Adaptation to Summarize Human Conversations

Sandu, O. A., Carenini, G., Murray, G., & Ng, R. T. (2010) · Cited by 17

Long Time Series High-Quality and High-Consistency Land Cover Mapping Based on Machine Learning Method at Heihe River Basin

Zhong, B., Yang, A., Jue, K., & Wu, J. (2021) · Remote Sensing · doi:10.3390/rs13081596 · Cited by 16

Development and Application of Predictive Models to Distinguish Seepage Slicks from Oil Spills on Sea Surfaces Employing SAR Sensors and Artificial Intelligence: Geometric Patterns Recognition under a Transfer Learning Approach

Genovez, P. C., Ponte, F. F. D. A., Matias, Í. D. O., Torres, S. B., Beisl, C. H., Mano, M. F., Silva, G. M. A., & de Miranda, F. P. (2023) · Remote Sensing · doi:10.3390/rs15061496 · Cited by 16

A Prior Model of Structural SVMs for Domain Adaptation

Lee, C. (2011) · ETRI Journal · doi:10.4218/etrij.11.0110.0571 · Cited by 16

Data-Driven Approaches for Paraphrasing across Language Variations.

Xu, W. H. (2014) · ProQuest LLC eBooks · Cited by 16

Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications

Cartas, A., Radeva, P. I., & Dimiccoli, M. (2020) · IEEE Access · doi:10.1109/access.2020.2990333 · Cited by 18

Profile Decomposition Based Hybrid Transfer Learning for Cold-Start Data Anomaly Detection

Li, Z., Yan, H., Tsung, F., & Zhang, K. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3530990 · Cited by 16

Language Models as Representations for Weakly-Supervised NLP Tasks

Huang, F., Yates, E., Ahuja, A., & Downey, D. (2012) · Cited by 16

Analyzing Urdu Social Media for Sentiments using Transfer Learning with Controlled Translations

Mukund, S., & Srihari, R. K. (2012) · Cited by 16

Relevance Ranking for Vertical Search Engines

(2014) · Elsevier eBooks · doi:10.1016/c2012-0-02862-1 · Cited by 17

Semisupervised Multitask Learning With Gaussian Processes

Skolidis, G., & Sanguinetti, G. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2272403 · Cited by 16

Improving SCL model for sentiment-transfer learning

Tan, S., & Cheng, X. (2009) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1620853.1620903 · Cited by 16

Dangers of Bayesian Model Averaging under Covariate ShiftPreprint

Izmailov, P., Nicholson, P., Lotfi, S., & Wilson, A. G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.11905 · Cited by 22

Unsupervised Multitarget Domain Adaptation With Dictionary-Bridged Knowledge Exploitation

Tian, Q., Ma, C., Cao, M., Wan, J., Lei, Z., & Chen, S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3193289 · Cited by 15

Domain Adaptive Classification

Mirrashed, F., & Rastegari, M. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.324 · Cited by 15

Bilingual recursive neural network based data selection for statistical machine translation

Wong, D. F., Lu, Y., & Chao, L. S. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.05.003 · Cited by 15

Libri-Adapt: a New Speech Dataset for Unsupervised Domain Adaptation

Mathur, A., Kawsar, F., Bianchi‐Berthouze, N., & Lane, N. D. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053074 · Cited by 15

Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching

Mezza, A. I., Habets, E. A. P., Müller, M., & Sarti, A. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287533 · Cited by 15

Penalized eigendecompositions: motivations from domain adaptation for calibration transfer

Andries, E. (2016) · Journal of Chemometrics · doi:10.1002/cem.2818 · Cited by 15

Enabling Rapid Classification of Social Media Communications During Crises

Imran, M., Mitra, P., & Srivastava, J. (2016) · International Journal of Information Systems for Crisis Response and Management · doi:10.4018/ijiscram.2016070101 · Cited by 15

Risk assessment using transfer learning for grassland fires

Liu, X., Zhang, G., Lü, J., & Zhang, J. (2019) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2019.01.011 · Cited by 16

Iterative parameter mixing for distributed large-margin training of structured predictors for natural language processing

Coppola, G. (2015) · ERA · Cited by 16

Domain Adaptation from User-level Facebook Models to County-level Twitter Predictions

Rieman, D., Jaidka, K., Schwartz, H. A., & Ungar, L. (2017) · International Joint Conference on Natural Language Processing · Cited by 14

Introducing Temporal Correlation in Rainfall and Wind Prediction From Underwater Noise

Trucco, A., Barla, A., Bozzano, R., Pensieri, S., Verri, A., & Solarna, D. (2023) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2022.3223406 · Cited by 16

SentiRank: Cross-Domain Graph Ranking for Sentiment Classification

Wu, Q., Tan, S., Zhai, H., Zhang, G., Duan, M., & Cheng, X. (2009) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2009.55 · Cited by 14

Improving Chinese Word Segmentation on Micro-blog Using Rich Punctuations

Zhang, L., Li, L., He, Z., Wang, H., & Sun, N. (2013) · Cited by 14

Fredholm Multiple Kernel Learning for Semi-Supervised Domain Adaptation

Wang, W., Wang, H., Zhang, C., & Gao, Y. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10818 · Cited by 14

A concise peephole model based transfer learning method for small sample temporal feature-based data-driven quality analysis

Luo, W., Zhang, J., Feng, P., Yu, D., & Wu, Z. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105665 · Cited by 14

Knowledge transfer based on feature representation mapping for text classification

Meng, J., Lin, H., & Li, Y. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.02.085 · Cited by 14

Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer

Wu, T., Luo, J., Zhou, Y., Wang, C., Xi, J., & Fang, J. (2020) · Remote Sensing · doi:10.3390/rs12010174 · Cited by 14

ICANN/PASCAL2 Challenge: MEG Mind Reading — Overview and Results

Arto Klami, A., Ramkumar, P., Virtanen, S., Parkkonen, L. T., Hari, R., & Samuel Kaski, A. (2012) · Cited by 14

Transfer Learning via Multi-View Principal Component Analysis

Ji, Y., Chen, J., Niu, G., Shang, L., & Dai, X. (2011) · Journal of Computer Science and Technology · doi:10.1007/s11390-011-9417-6 · Cited by 14

Democracy is good for ranking

Gao, W. G., & Yang, P. (2014) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2556195.2556267 · Cited by 13

Holographic Feature Learning of Egocentric-Exocentric Videos for Multi-Domain Action Recognition

Huang, Y., Yang, X., Gao, J., & Xu, C. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3078882 · Cited by 13

Transferable discriminant linear regression for cross-corpus speech emotion recognition

Li, S., Song, P., & Zhang, W. (2022) · Applied Acoustics · doi:10.1016/j.apacoust.2022.108919 · Cited by 13

Dictionary transfer for image denoising via domain adaptation

Chen, G., Xiong, C., & Corso, J. J. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467078 · Cited by 13

Discriminative importance weighting of augmented training data for acoustic model training

Sivasankaran, S., Emmanuel Vincent, A., & Illina, I. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953085 · Cited by 12

Reference Signal-Based Method to Remove Respiration Noise in Electrodermal Activity (EDA) Collected from the Field

Lee, G., Choi, B., Jebelli, H., Ahn, C. R., & Lee, S. H. (2019) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784482438.003 · Cited by 13

FlexAdapt: Flexible Cycle-Consistent Adversarial Domain Adaptation

Mathur, A., Isopoussu, A., Kawsar, F., Bianchi‐Berthouze, N., & Lane, N. D. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00155 · Cited by 13

Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion

Zhao, C., Su, Y., Pauls, A., & Platanios, E. A. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.381 · Cited by 16

Domain adaptive object detection

Mirrashed, F., Morariu, V. I., Siddiquie, B., Feris, R. S., & Davis, L. S. (2013) · Proceedings · doi:10.1109/wacv.2013.6475036 · Cited by 14

Predicting the Effectiveness of Self-Training: Application to Sentiment ClassificationPreprint

Asch, V. V., & Daelemans, W. M. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.03288 · Cited by 13

Kernelized Heterogeneous Risk MinimizationPreprint

Liu, J., Hu, Z., Cui, P., Li, B., & Shen, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.12425 · Cited by 13

An Efficient Domain-Adaptation Method using GAN for Fraud Detection

Hwang, J., & Kim, K. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0111113 · Cited by 13

Estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment

Albuquerque, I., Monteiro, J., Rosanne, O., & Falk, T. H. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.992732 · Cited by 12

Distributionally Robust Learning With Stable Adversarial Training

Liu, J., Shen, Z., Cui, P., Zhou, L., Kuang, K., & Li, B. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3224056 · Cited by 15

A geometric framework for transfer learning using manifold alignment

Mahadevan, S., & Wang, C. (2010) · Cited by 12

Active Annotation Translation

Branson, S., Hjörleifsson, K. E., & Perona, P. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.473 · Cited by 12

Comparative analysis of machine learning algorithms on social media test

Ragupathy, R., & Maguluri, L. P. (2018) · International Journal of Engineering & Technology · doi:10.14419/ijet.v7i2.8.10425 · Cited by 12

Dependency parsing with finite state transducers and compression rules

Gamallo, P., & García, M. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.05.003 · Cited by 12

Adversarial balancing-based representation learning for causal effect inference with observational data

Du, X., Сун, Л., Duivesteijn, W., Nikolaev, A. G., & Pechenizkiy, M. (2019) · TU/e Research Portal · Cited by 12

Cross-domain sentiment classification using a two-stage method

Liu, K., & Zhao, J. (2009) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1645953.1646212 · Cited by 12

Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment

Albuquerque, I., Monteiro, J., Rosanne, O., Tiwari, A., Gagnon, J.-F., & Falk, T. H. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914469 · Cited by 12

Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks

Yang, H., Zhuang, T., & Zong, C. (2015) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00138 · Cited by 12

Domain invariant speech features using a new divergence measure

Wisler, A. A., Berisha, V., Liss, J., & Spanias, A. (2014) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2014.7078553 · Cited by 11

Instance Transfer Learning with Multisource Dynamic TrAdaBoost

Zhang, Q., Li, H., Zhang, Y., & Li, M. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/282747 · Cited by 11

Feature Relevance Network-Based Transfer Learning for Indoor Location Estimation

Seok, H.-S., Hwang, K., & Zhang, B. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2076277 · Cited by 11

Domain Adaptation--Can Quantity compensate for Quality?.

Ben-David, S., Shalev‐Shwartz, S., & Urner, R. (2012) · Cited by 9

A survey on domain adaptation theory: learning bounds and theoretical guaranteesPreprint

Redko, I., Morvant, E., Habrard, A., Sebban, M., & Bennani, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.11829 · Cited by 154

A two-stage approach to domain adaptation for statistical classifiers

Jiang, J., & Zhai, C. (2007) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1321440.1321498 · Cited by 119

Domain Adaptation: A Small Sample Statistical Approach

Salakhutdinov, R., Kakade, S. M., & Foster, D. P. (2012) · International Conference on Artificial Intelligence and Statistics

Advances in Domain Adaptation Theory

Redko, I., Morvant, E., Habrard, A., Sebban, M., & Bennani, Y. (2019) · HAL · Cited by 65