stoch-appl.mit.edu
MIT Stochastics and Statistics Seminar
http://stoch-appl.mit.edu/index.html
MIT Stochastics and Statistics Seminar. For organizational matters, or to be added to the mailing list, please contact Lizzie Raymer. UC Berkeley) 32-141, 11-12. Efficient Optimal Strategies for Universal Prediction. Harvard University) 32-141, 11-12. Next Generation Missing Data Methodology. Harvard University) 32-141, 11-12. Minimax Estimation of Nonlinear Functionals with Higher Order Influence Functions: Results and Applications. Harvard University) 32-141, 11-12. University of Chicago) 32-141, 11-12.
machinelearning.wisc.edu
News | Machine Learning
https://machinelearning.wisc.edu/news
A modern learning experience. Dane Morgan is featured in a June 2016, College of Engineering story about Informatics Skunkworks. This entry was posted in Web Article. And tagged College of Engineering. August 5, 2016. Computer-generated database of diffusion values is shared online. Materials Science and Engineering. Led by Dane Morgan, Harvey D. Spangler Professor in Materials Science and Engineering. At UW Madison, the researchers published details of their advance July 19 in the journal Scientific Data.
machinelearning.wisc.edu
People | Machine Learning
https://machinelearning.wisc.edu/people
Mathematical Foundations of Machine Learning. Biostatistics & Medical Informatics. Electrical & Computer Engineeering. Electrical & Computer Engineeering. Electrical & Computer Engineering. Biostatistics & Medical Informatics. Electrical & Computer Engineering. Mechanical Engineering Grainger Institute. Biostatistics & Medical Informatics. Electrical & Computer Engineering. Applications of Machine Learning. Biostatistics & Medical Informatics. Biostatistics & Medical Informatics.
nextml.org
Team
http://nextml.org/team
Lalit Jain is a graduate student in mathematics at the University of Wisconsin, Madison. His research interests revolve around algebraic geometry, topology and number theory. He has also been exploring applications of geometry to machine learning. He is advised by Jordan Ellenberg and Robert Nowak. As well as being affiliated with the departments of Computer Sciences. At the University of Wisconsin. He is also a Fellow of the IEEE and the Wisconsin Institute for Discovery. Current and Past Team Members.
spars2013.epfl.ch
Organizing Committee - SPARS 2013
http://spars2013.epfl.ch/index.php/Organizing_Committee
Signal Processing with Adaptive Sparse Structured Representations. July 8-11, 2013. EPFL, Lausanne. Ecole Polytechnique Fédérale de Lausanne, Switzerland. Ecole Polytechnique Fédérale de Lausanne, Switzerland. Ecole Polytechnique Fédérale de Lausanne, Switzerland. INSA de Rouen, France. Université Paris Diderot, France. University of Edinburgh, UK. Centre de Recherche INRIA Rennes, France. Queen Mary University of London, UK. UCD CASL and University College Dublin, Ireland. University of Oxford, UK.
spars2013.epfl.ch
Plenary Talks - SPARS 2013
http://spars2013.epfl.ch/index.php/Plenary_Talks
Signal Processing with Adaptive Sparse Structured Representations. July 8-11, 2013. EPFL, Lausanne. Special Lecture by Ronald DeVore. An abbreviated and very personal history of nonlinear approximation. Classification with Sparse Deep Scattering Networks. Assigning statistical significance in high-dimensional problems. Wavelet for Graphs and its Deployment to Image Processing. Learning Near-Isometric Linear Embeddings. Adaptive Sensing of Sparse Signals. Lecture by Ronald DeVore.