Keyword: machine learning

2019 (2)
Applications of machine learning in real-life digital health interventions: Review of the literature. Triantafyllidis, A., K. & Tsanas, A. Journal of Medical Internet Research, 21(4):1-9, 2019.
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Block Stability for MAP Inference. Lang, H., Sontag, D., & Vijayaraghavan, A. In Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AI-STATS) (To appear), 2019.
Block Stability for MAP Inference [link]Paper  abstract   bibtex   
2018 (14)
Multiobjective Optimization for Stiffness and Position Control in a Soft Robot Arm Module. Ansari, Y., Manti, M., Falotico, E., Cianchetti, M., & Laschi, C. IEEE Robotics and Automation Letters, 3(1):108–115, January, 2018.
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Block Stability for MAP Inference. Lang, H., Sontag, D., & Vijayaraghavan, A. ArXiv e-prints arXiv:1810.05305, 2018.
Block Stability for MAP Inference [link]Paper  abstract   bibtex   
Gaze and the Control of Foot Placement When Walking in Natural Terrain. Matthis, J. S., Yates, J. L., & Hayhoe, M. M. Current Biology, April, 2018.
Gaze and the Control of Foot Placement When Walking in Natural Terrain [link]Paper  doi  bibtex   
Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques. Seixas, A., A., Henclewood, D., A., Williams, S., K., Jagannathan, R., Ramos, A., Zizi, F., & Jean-Louis, G. Frontiers in Neurology, 9:534, Frontiers, 7, 2018.
Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques [link]Website  abstract   bibtex   
Super Resolution Network Analysis Defines the Molecular Architecture of Caveolae and Caveolin-1 Scaffolds. Khater, I. M., Meng, F., Wong, T. H., Nabi, I. R., & Hamarneh, G. Nature - Scientific reports, 8(9009):1-15, 2018.
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seq-ImmuCC: Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data. Chen, Z., Quan, L., Huang, A., Zhao, Q., Yuan, Y., Yuan, X., Shen, Q., Shang, J., Ben, Y., Qin, F. X., & Wu, A. Frontiers in Immunology, 9:1286, 2018.
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Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression. Carrillo, F., Sigman, M., Fernández Slezak, D., Ashton, P., Fitzgerald, L., Stroud, J., Nutt, D., J., & Carhart-Harris, R., L. Journal of Affective Disorders, 230:84-86, Elsevier, 4, 2018.
Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression [link]Website  abstract   bibtex   
Noninvasive Determination of Gene Mutations in Clear Cell Renal Cell Carcinoma using Multiple Instance Decisions Aggregated CNN. Hussain, A., Hamarneh, G., & Abugharbieh, R. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 11071, pages 657-665, 2018.
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Generative Adversarial Networks to Segment Skin Lesions. Izadi, S., Mirikharaji, Z., Kawahara, J., & Hamarneh, G. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 881-884, 2018.
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Stroke-Associated Hemiparesis Detection Using Body Joints and Support Vector Machines. Ramesh, V., Agrawal, K., Meyer, B., Cauwenberghs, G., & Weibel, N. In Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare, of PervasiveHealth '18, pages 55–58, New York, NY, USA, 2018. ACM.
Stroke-Associated Hemiparesis Detection Using Body Joints and Support Vector Machines [link]Paper  doi  abstract   bibtex   
Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation. Mirikharaji, Z. & Hamarneh, G. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 11073, pages 737-745, 2018.
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Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review. Yang, L., MacEachren, A., Mitra, P., & Onorati, T. ISPRS International Journal of Geo-Information, 7(2):65, Multidisciplinary Digital Publishing Institute, 2, 2018.
Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review [pdf]Paper  Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review [link]Website  abstract   bibtex   
Predicting Cancer with a Recurrent Visual Attention Model for Histopathology Images. BenTaieb, A. & Hamarneh, G. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 11071, pages 129-137, 2018.
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Smart City Visualization Tool for the Open Data Georeferenced Analysis Utilizing Machine Learning. Estrada, E., Maciel, R., Ochoa, A., Bernabe-Loranca, B., Oliva, D., & Larios, V. International Journal of Combinatorial Optimization Problems & Informatics, 9(2):25–40, May, 2018.
Smart City Visualization Tool for the Open Data Georeferenced Analysis Utilizing Machine Learning [link]Paper  abstract   bibtex   
2017 (14)
BrainNetCNN: Artificial Convolutional Neural Networks for Connectomes. Kawahara, J., Brown, C. J., Miller, S., Booth, B. G., Chau, V., Grunau, R., Zwicker, J., & Hamarneh, G. In 2nd Annual Health Technology Symposium, Vancouver, Canada, pages 1, 2017.
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Lesion volume Estimation from PET without Requiring Segmentation. Taghanaki, S. A., Duggan, N., Ma, H., Celler, A., Benard, F., & Hamarneh, G. In Quantitative Imaging Network (QIN) Annual Meeting, 2017.
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Learning to Run Heuristics in Tree Search. Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E. B., & Turaga, D. In 26th International Joint Conference on Artificial Intelligence, 2017.
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DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks. Rajchl, M., Lee, M. C. H., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M. A., Hajnal, J. V., Kainz, B., & Rueckert, D. IEEE Trans. Med. Imaging, 36(2):674–683, 2017.
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks [link]Paper  doi  bibtex   
Learning to jump in granular media: Unifying optimal control synthesis with Gaussian process-based regression. Chang, A. H, Hubicki, C. M, Aguilar, J. J, Goldman, D. I, Ames, A. D, & Vela, P. A In Robotics and Automation (ICRA), 2017 IEEE International Conference on, pages 2154–2160, 2017. IEEE.
Learning to jump in granular media: Unifying optimal control synthesis with Gaussian process-based regression [pdf]Paper  bibtex   1 download  
Fully Convolutional Networks to Detect Clinical Dermoscopic Features. Kawahara, J. & Hamarneh, G. Technical Report arxiv:1703.04559, 3, 2017.
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A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling. Arabnejad, H., Pahl, C., Jamshidi, P., & Estrada, G. In Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, Madrid, Spain, May 14-17, 2017, pages 64–73, 2017.
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling [link]Paper  doi  bibtex   
Exploring Stroke-associated Hemiparesis Assessment with Support Vector Machines. Ramesh, V., Agrawal, K., Meyer, B., Cauwenberghs, G., & Weibel, N. In Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, of PervasiveHealth '17, pages 464–467, New York, NY, USA, 2017. ACM. Poster
Exploring Stroke-associated Hemiparesis Assessment with Support Vector Machines [link]Paper  doi  abstract   bibtex   
Machine-learning techniques in economics : New Tools for Predicting Economic Growth. Basuchoudhary, A., Bang, J., T., & Sen, T. Springer, Cham, 2017.
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A Practical Guide To Using Face Technology (Part I). Lee, F. November, 2017.
A Practical Guide To Using Face Technology (Part I) [link]Paper  abstract   bibtex   
Learning Feature Engineering for Classification. Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E. B., & Turaga, D. In 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017.
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Poverty from Space: Using High-Resolution Satellite Imagery for Estimating Economic Well-Being. Engstrom, R., Hersh, J., & Newhouse, D. 12 2017.
Poverty from Space: Using High-Resolution Satellite Imagery for Estimating Economic Well-Being [link]Website  abstract   bibtex   
Molecular Level Quantification of Cav1 Clusters in Super-Resolution Imaging Data. Khater, I. M., Meng, F., Nabi, I. R., & Hamarneh, G. In Frontiers in Biophysics, Vancouver, Canada, pages 1, 2017.
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Counting Apples and Oranges With Deep Learning: A Data-Driven Approach. Chen, S. W., Shivakumar, S. S., Dcunha, S., Das, J., Okon, E., Qu, C., Taylor, C. J., & Kumar, V. IEEE Robotics and Automation Letters, 2(2):781–788, April, 2017.
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2016 (17)
S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning. Schrider, D. R. & Kern, A. D. PLOS Genetics, 12(3):e1005928, March, 2016.
S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning [link]Paper  doi  abstract   bibtex   
Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks. Geitgey, A. June, 2016.
Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks [link]Paper  abstract   bibtex   
Discovering Biosignatures of Cav1 Domains: Computational Methods for Super-resolution Microscopy. Khater, I. M., Meng, F., Nabi, I. R., & Hamarneh, G. In LSI Imaging Super-resolution Mini-symposium, Vancouver, Canada, pages 1, 2016.
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Clinically-Inspired Automatic Classification of Ovarian Carcinoma Subtypes. BenTaieb, A., Nosrati, M., Li-Chang, H., Huntsman, D., & Hamarneh, G. Journal of Pathology Informatics, 7(1):1-28, 2016.
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Automated detection and classification of feeding strikes by larval fish from continuous high-speed digital video: a novel method to extract quantitative data from fast, sparse kinematic events. Zilka, M., Eyal Shamur, E., Hassner, T., China, V., Liberzon, A., & Holzman, R. Journal of Experimental Biology, 2016.
Automated detection and classification of feeding strikes by larval fish from continuous high-speed digital video: a novel method to extract quantitative data from fast, sparse kinematic events [pdf]Paper  abstract   bibtex   
Learning to Branch in Mixed Integer Programming. Khalil, E. B., Le Bodic, P., Song, L., Nemhauser, G. L, & Dilkina, B. N In AAAI, pages 724–731, 2016.
Learning to Branch in Mixed Integer Programming. [pdf]Paper  bibtex   
Backcasting and a new way of command in computational design. Koenig, R. & Schmitt, G. In CAADence in Architecture, pages 15–25, 2016.
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Tumour Lesion Segmentation from 3D PET using a Machine Learning driven Active Surface. Ahmadvand, P., Duggan, N., Benard, F., & Hamarneh, G. In Medical Image Computing and Computer-Assisted Intervention Workshop on Machine Learning in Medical Imaging (MICCAI MLMI), volume 10019, pages 271-278, 2016.
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A Data-Driven Demand Response Recommender System. Behl, M. & Mangharam, R. Journal of Applied Energy, 2016. [Under Review]
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Predictive Subnetwork Extraction with Structural Priors for Infant Connectomes. Brown, C. J., Miller, S., Booth, B. G., Zwicker, J., Grunau, R., Synnes, A., Chau, V., & Hamarneh, G. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 9900, pages 175-183, 2016.
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Learning Time-Varying Forecast Combinations. Mandel, A. & Sani, A. Working Paper Centre d'Economie de la Sorbonne 2016.36, 2016.
Learning Time-Varying Forecast Combinations [link]Paper  abstract   bibtex   
Comparing of feature selection and classification methods on report-based subhealth data. Li Huang, Shixing Yan, Jiamin Yuan, Zhiya Zuo, Fuping Xu, Yanzhao Lin, Mary Qu Yang, Zhimin Yang, & Li, G. In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 1356-1358, 12, 2016. IEEE.
Comparing of feature selection and classification methods on report-based subhealth data [link]Website  abstract   bibtex   
On the Application of Rough Sets to Skeletal Maturation Classification. Garza-Morales, R., López-Irarragori, F., & Sanchez, R. Artif. Intell. Rev., 45(4):489--508, Kluwer Academic Publishers, Norwell, MA, USA, April, 2016.
On the Application of Rough Sets to Skeletal Maturation Classification [link]Paper  doi  abstract   bibtex   
Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures. Jamshidi, P., Sharifloo, A. M., Pahl, C., Arabnejad, H., Metzger, A., & Estrada, G. In 12th International ACM SIGSOFT Conference on Quality of Software Architectures, QoSA 2016, Venice, Italy, April 5-8, 2016, pages 70–79, 2016. $\bigstar$
Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures [pdf]Paper  Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures [link]Slides  Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures [link]Paper  doi  bibtex   
Variational Autoencoders Explained. August, 2016.
Variational Autoencoders Explained [link]Paper  abstract   bibtex   
Segmentation-Free Estimation of Kidney Volumes in CT with Dual Regression Forests. Hussain, A., Hamarneh, G., O'Connell, T., Mohammed, M., & Abugharbieh, R. In Medical Image Computing and Computer-Assisted Intervention Workshop on Machine Learning in Medical Imaging (MICCAI MLMI), volume 10019, pages 156-163, 2016.
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Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical. Kop, R., Hoogendoorn, M., ten Teije, A., Büchner, Frederike L Slottje, P., Moons, L. M., & Numans, M. E. Computers in Biology and Medicine, 76:30–38, 2016.
Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical [link]Paper  doi  abstract   bibtex   
2015 (7)
Probabilistic event calculus for event recognition. Skarlatidis, A., Paliouras, G., Artikis, A., & Vouros, G. A. ACM Trans. Comput. Logic, 16(2):11:1--11:37, ACM, New York, NY, USA, feb, 2015.
Probabilistic event calculus for event recognition [link]Paper  doi  bibtex   
Grand Challenge Veterinary Imaging: Technology, Science, and Communication. McEvoy, F., J. Frontiers in veterinary science, 2:38, 9, 2015.
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Metadata Dependent Mondrian Processes. Wang, Y., Li, B., Wang, Y., & Chen, F. In Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37, of ICML'15, pages 1339--1347, 2015. JMLR.org.
Metadata Dependent Mondrian Processes [link]Paper  bibtex   
Anchored Discrete Factor Analysis. Halpern, Y., Horng, S., & Sontag, D. In arXiv:1511.03299, 2015.
Anchored Discrete Factor Analysis [pdf]Paper  abstract   bibtex   
Accurate Data Cleansing through Model Checking and Machine Learning Techniques. Boselli, R., Cesarini, M., Mercorio, F., & Mezzanzanica, M. In Helfert, M., Holzinger, A., Belo, O., & Francalanci, C., editors, Data Management Technologies and Applications, volume 178, of Communications in Computer and Information Science, pages 62-80. Springer International Publishing, 2015.
Accurate Data Cleansing through Model Checking and Machine Learning Techniques [link]Paper  doi  bibtex   
Fine-grained OD estimation with automated zoning and sparsity regularisation. Menon, A. K., Cai, C., Wang, W., Wen, T., & Chen, F. Transportation Research Part B: Methodological, 80:150--172, Elsevier BV, oct, 2015.
Fine-grained OD estimation with automated zoning and sparsity regularisation [link]Paper  doi  bibtex   
A computer vision tracking system for pigmented skin lesions. Mirzaalian, H., Lee, T., & Hamarneh, G. In World Congress of Dermatology (WCD), 2015.
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2014 (9)
ARTgrid: A Two-Level Learning Architecture Based on Adaptive Resonance Theory. Švaco, M. & Jerbić, B. Advances in Artificial Neural Systems, 2014:1–9, 2014.
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Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues. Kukurba, K. R., Zhang, R., Li, X., Smith, K. S., Knowles, D. A., How Tan, M., Piskol, R., Lek, M., Snyder, M., MacArthur, D. G., Li, J. B., & Montgomery, S. B. PLoS Genetics, 10(5):e1004304, Public Library of Science, 2014.
Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues [link]Paper  doi  abstract   bibtex   
Software Bug Localization with Markov Logic. Zhang, S. 2014.
Software Bug Localization with Markov Logic [pdf]Paper  bibtex   
Transcriptome sequencing of a large human family identifies the impact of rare noncoding variants. Li, X., Battle, A., Karczewski, K. J., Zappala, Z., Knowles, D. A., Smith, K. S., Kukurba, K. R., Wu, E., Simon, N., & Montgomery, S. B. American Journal of Human Genetics, 95(3):245–56, Elsevier, 2014.
Transcriptome sequencing of a large human family identifies the impact of rare noncoding variants. [link]Paper  doi  abstract   bibtex   
A machine learning method for high-frequency data forecasting. Allende, H., López, E., & Allende-Cid, H. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 8827, pages 621-628, 2014.
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Ventricular fibrillation and tachycardia classification using a machine learning approach. Li, Q., Rajagopalan, C., & Clifford, G., D. IEEE Transactions on Biomedical Engineering, 61(6):1607-1613, IEEE Computer Society, 2014.
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Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference. Badawi, O., Brennan, T., Celi, L. A., Feng, M., Ghassemi, M., Ippolito, A., Johnson, A., Mark, R. G, Mayaud, L., Moody, G., Moses, C., Naumann, T., Nikore, V., Pimentel, M., Pollard, T. J, Santos, M., Stone, D. J, Zimolzak, A., & MIT Critical Data Conference 2014 Organizing Committee JMIR Medical Informatics, 2(2):e22, August, 2014.
Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference [link]Paper  doi  bibtex   
Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology. Peters, D. P. C., Havstad, K. M., Cushing, J., Tweedie, C., Fuentes, O., & Vilanueva-Rosales, N. Ecosphere, 5(6):67. http://dx.doi.org/10.1890/ES13–00359.1, 2014.
Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology [link]Paper  abstract   bibtex   
A methodology for the characterization and diagnosis of cognitive impairments-Application to specific language impairment. Oliva, J., Serrano, J., I., del Castillo, M., D., & Iglesias, Á. Artificial Intelligence in Medicine, 61(2):89-96, Elsevier B.V., 2014.
A methodology for the characterization and diagnosis of cognitive impairments-Application to specific language impairment [pdf]Paper  A methodology for the characterization and diagnosis of cognitive impairments-Application to specific language impairment [link]Website  abstract   bibtex   
2013 (5)
Fixed-form variational posterior approximation through stochastic linear regression. Salimans, T. & Knowles, D. A. Bayesian Analysis, 8(4):837–882, International Society for Bayesian Analysis, 2013. Winner of the Lindley Prize!
Fixed-form variational posterior approximation through stochastic linear regression [link]Paper  doi  abstract   bibtex   
Decision Forests with Spatio-temporal Features for Graph-based Tumour Segmentation in 4D Lung CT. Mirzaei, H., Tang, L. Y. W., Werner, R., & Hamarneh, G. In Medical Image Computing and Computer-Assisted Intervention Workshop on Machine Learning in Medical Imaging (MICCAI MLMI), volume 8184, pages 179-186, 2013.
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Detecting inappropriate access to electronic health records using collaborative filtering. Menon, A. K., Jiang, X., Kim, J., Vaidya, J., & Ohno-Machado, L. Machine Learning, 95(1):87--101, Springer Nature, jun, 2013.
Detecting inappropriate access to electronic health records using collaborative filtering [link]Paper  doi  bibtex   
Parallel globally optimal structure learning of Bayesian networks. Nikolova, O., Zola, J., & Aluru, S. Journal of Parallel and Distributed Computing, 73(8):1039-1048, 8, 2013.
Parallel globally optimal structure learning of Bayesian networks [link]Website  abstract   bibtex   
Online ridge regression method using sliding windows. Arce, P. & Salinas, L. C. In pages 87-90, 2013.
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2012 (8)
Locally-Adaptive Similarity Metric for Deformable Medical Image Registration. Tang, L. Y. W., Hero, A. O, & Hamarneh, G. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 728-731, 2012.
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Transfer learning for cross-company software defect prediction. Ma, Y., Luo, G., Zeng, X., & Chen, A. Information and Software Technology, 54(3):248--256, 2012.
Transfer learning for cross-company software defect prediction [link]Paper  doi  abstract   bibtex   
Triaging incoming change requests: Bug or commit history, or code authorship?. Linares-Vásquez, M., Hossen, K., Dang, H., Kagdi, H., Gethers, M., & Poshyvanyk, D. In Software Maintenance (ICSM), 2012 28th IEEE International Conference on, pages 451–460, September, 2012.
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Learning and Inference in Probabilistic Classifier Chains with Beam Search. Kumar, A., Vembu, S., Menon, A. K., & Elkan, C. In Machine Learning and Knowledge Discovery in Databases, pages 665--680. Springer Berlin Heidelberg, 2012.
Learning and Inference in Probabilistic Classifier Chains with Beam Search [link]Paper  doi  bibtex   
Learning Features for Streak Detection in Dermoscopic Color Images using Localized Radial Flux of Principal Intensity Curvature. Mirzaalian, H., Lee, T., & Hamarneh, G. In IEEE workshop on Mathematical Methods for Biomedical Image Analysis (IEEE MMBIA), pages 97-101, 2012.
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An Infinite Latent Attribute Model for Network Data. Palla, K., Knowles, D. A., & Ghahramani, Z. In 29th International Conference on Machine Learning (ICML 2012), pages 1607–1614, 2012.
An Infinite Latent Attribute Model for Network Data [pdf]Paper  abstract   bibtex   
Intelligent system for predicting wireless sensor network performance in on-demand deployments. Otero, C., Kostanic, I., Peter, A., Ejnioui, A., & Daniel Otero, L. In 2012 IEEE Conference on Open Systems, ICOS 2012, 2012.
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Predicting Metal-Binding Sites from Protein Sequence. Passerini, A., Lippi, M., & Frasconi, P. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 9:203–213, IEEE Computer Society Press, Los Alamitos, CA, USA, January, 2012.
Predicting Metal-Binding Sites from Protein Sequence [pdf]Paper  doi  bibtex   
2011 (4)
Response prediction using collaborative filtering with hierarchies and side-information. Menon, A. K., Chitrapura, K., Garg, S., Agarwal, D., & Kota, N. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD \textquotesingle11, 2011. ACM Press.
Response prediction using collaborative filtering with hierarchies and side-information [link]Paper  doi  bibtex   
Fast Algorithms for Approximating the Singular Value Decomposition. Menon, A. K. & Elkan, C. ACM Transactions on Knowledge Discovery from Data, 5(2):1--36, Association for Computing Machinery (ACM), feb, 2011.
Fast Algorithms for Approximating the Singular Value Decomposition [link]Paper  doi  bibtex   
Link Prediction via Matrix Factorization. Menon, A. K. & Elkan, C. In Machine Learning and Knowledge Discovery in Databases, pages 437--452. Springer Berlin Heidelberg, 2011.
Link Prediction via Matrix Factorization [link]Paper  doi  bibtex   
3D shape analysis of thigh muscles: people with Chronic Obstructive Pulmonary Disease versus healthy older adults. HajGhanbari, B., Hamarneh, G., Changizi, N., Ward, A., & Reid, W. D. In Canadian Physiotherapy Association Congress, pages 1, 2011.
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2009 (5)
Improving propensity score weighting using machine learning. Lee, B., K., Lessler, J., & Stuart, E., A. Statistics in Medicine, 29(3):n/a-n/a, Wiley-Blackwell, 2009.
Improving propensity score weighting using machine learning [pdf]Paper  Improving propensity score weighting using machine learning [link]Website  abstract   bibtex   
ViridiScope: Design and Implementation of a Fine Grained Power Monitoring System for Homes. Kim, Y., Schmid, T., Charbiwala, Z. M., & Srivastava, M. B. In Proceedings of the 11th International Conference on Ubiquitous Computing, of Ubicomp '09, pages 245--254, New York, NY, USA, 2009. ACM.
ViridiScope: Design and Implementation of a Fine Grained Power Monitoring System for Homes [link]Paper  doi  abstract   bibtex   
AG-ART: An adaptive approach to evolving ART architectures. Kaylani, A., Georgiopoulos, M., Mollaghasemi, M., & Anagnostopoulos, G. C. Neurocomputing, 72(10–12):2079 - 2092, June, 2009. Lattice Computing and Natural Computing (JCIS 2007) / Neural Networks in Intelligent Systems Designn (ISDA 2007)
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Reforestation planning using Bayesian networks. Ordóñez Galán, C., Matías, J., Rivas, T., & Bastante, F. Environmental Modelling & Software, 24(11):1285-1292, 11, 2009.
Reforestation planning using Bayesian networks [link]Website  abstract   bibtex   
The elements of statistical learning: data mining, inference, and prediction. Hastie, T., Tibshirani, R., & Friedman, J. H. Springer, New York, NY, 2nd ed edition, 2009. largely / videos
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2008 (1)
3D Bicipital Groove Shape Analysis and Relationship to Tendopathy. Ward, A., Hamarneh, G., & Schweitzer, M. Journal of Digital Imaging, 21(2):219-234, 2008.
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2007 (3)
Genetic Optimization of Art Neural Network Architectures. Kaylani, A., Georgiopoulos, M., Mollaghasemi, M., & Anagnostopoulos, G. C. In Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing, of ASC '07, pages 225–230, Anaheim, CA, USA, 2007. ACTA Press.
Genetic Optimization of Art Neural Network Architectures [link]Paper  bibtex   
Anatomical Shape Analysis: Exploring the Relationship between Shape and Pathology. Ward, A., Hamarneh, G., & Schweitzer, M. In CIHR National Research Poster Competition, Canadian Student Health Research Forum (CSHRF), Winnipeg, June 6-7, 2007.
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Infinite Sparse Factor Analysis and Infinite Independent Components Analysis. Knowles, D. A. & Ghahramani, Z. In 7th International Conference on Independent Component Analysis and Signal Separation (ICA), 2007.
Infinite Sparse Factor Analysis and Infinite Independent Components Analysis [link]Paper  Infinite Sparse Factor Analysis and Infinite Independent Components Analysis [pdf]Pdf  doi  abstract   bibtex   
2006 (2)
3D Shape Analysis of the Supraspinatus Muscle. Ward, A., Hamarneh, G., Ashry, R., & Schweitzer, M. In Medical Image Computing and Computer-Assisted Intervention Joint Diseases Workshop (MICCAI JD), pages 96-103, 2006.
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3D Shape Description of the Bicipital Groove: Correlation to Pathology. Ward, A., Hamarneh, G., & Schweitzer, M. In Medical Image Computing and Computer-Assisted Intervention Joint Diseases Workshop (MICCAI JD), pages 80-87, 2006.
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2005 (1)
A Tutorial on the Cross-Entropy Method. Boer, P., D., E. 2005.
A Tutorial on the Cross-Entropy Method [pdf]Paper  bibtex   
2004 (1)
A Combined Transmembrane Topology and Signal Peptide Prediction Method. Käll, L., Krogh, A., & Sonnhammer, E. L. L Journal of Molecular Biology, 338(5):1027–1036, May, 2004.
A Combined Transmembrane Topology and Signal Peptide Prediction Method [link]Paper  doi  abstract   bibtex   
2002 (1)
Research abstract for semantic anomaly detection in dynamic data feeds with incomplete specifications. Raz, O. In Proceedings of the 24rd International Conference on Software Engineering, 2002. ICSE 2002, pages 733--734, May, 2002.
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2000 (1)
Feature Subset Selection by Bayesian network-based optimization. Inza, I., Larrañaga, P., Etxeberria, R., & Sierra, B. Artificial Intelligence, 123(1-2):157-184, 10, 2000.
Feature Subset Selection by Bayesian network-based optimization [link]Website  abstract   bibtex   
1999 (2)
An overview of statistical learning theory. Vapnik, V. N. IEEE Trans Neural Netw, 10(5):988–999, 1999.
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Weka: Practical machine learning tools and techniques with Java implementations. Witten, I., Frank, E., Trigg, L., Hall, M., Holmes, G., & Cunningham, S. In Proceedings of the ICONIP/ANZIIS/ANNES’99 Workshop Future directions for intelligent systems and information sciences, 1999.
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1994 (2)
On a learnability question associated to neural networks with continuous activations (extended abstract). DasGupta, B., Siegelmann, H. T., & Sontag, E. In COLT '94: Proceedings of the seventh annual conference on Computational learning theory, pages 47–56, New York, NY, USA, 1994. ACM Press.
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On the Intractability of Loading Neural Networks. DasGupta, B., Siegelmann, H., & Sontag, E. In Roychowdhury, V. P., Y., S. K., & A., O., editors, Theoretical Advances in Neural Computation and Learning, pages 357–389. Kluwer Academic Publishers, 1994.
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1991 (1)
Adaptive case-based reasoning. Callan, J. & Fawcett, T. In Proceedings of the Third DARPA Case-Based Reasoning, pages 179-190, 1991. Morgan Kaufmann.
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