My main research interest is in developing optimization and machine learning techniques suitable for solving high-level computer vision tasks, such as image classification and object recognition.
High-level computer vision tasks are a unique source of hard machine learning problems for three reasons. First, in contrast to physics-based processes we do not know the correct model (model uncertainty). Second, humans excel at all high-level vision tasks and therefore can provide data and assess model performance (ground truth oracle). Third, image and video data is available for free at an enormous scale (data availability). These properties make computer vision a particularly attractive area for machine learning research.
I am particularly interested in using mathematical optimization as a tool to solve computer vision machine learning tasks.
My personal homepage is here: http://www.nowozin.net/sebastian/
- Sebastian Nowozin, Optimal Decisions from Probabilistic Models: the Intersection-over-Union Case, in Computer Vision and Pattern Recognition (CVPR 2014), IEEE Computer Society, 1 June 2014
- Po-Ling Loh and Sebastian Nowozin, Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates, in 24th International Conference on Algorithmic Learning Theory (ALT 2013), , 1 September 2013
- Sebastian Nowozin, Constructing Composite Likelihoods in General Random Fields, in ICML 2013 Workshop on Inferning: Interactions between Inference and Learning, , 1 July 2013
- Joerg Kappes, Bjoern Andres, Fred Hamprecht, Christoph Schnoerr, Sebastian Nowozin, Dhurv Batra, Sungwoong Kim, Bernhard Kausler, Jan Lellmann, Nikos Komodakis, and Carsten Rother, A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems, in CVPR, June 2013
- Jeremy Jancsary, Sebastian Nowozin, and Carsten Rother, Learning Convex QP Relaxations for Structured Prediction, in 30th International Conference on Machine Learning (ICML), June 2013
- Uwe Schmidt, Carsten Rother, Sebastian Nowozin, Jeremy Jancsary, and Stefan Roth, Discriminative Non-blind Deblurring, in 2013 Conference on Computer Vision and Pattern Recognition (CVPR 2013), IEEE Computer Society, 20 April 2013
- Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, and Antonio Criminisi, Decision Jungles: Compact and Rich Models for Classification, in Proc. NIPS, 2013
- S. Nowozin, C. Rother, S. Bagon, T. Sharp, B. Yao, and P. Kohli, Decision Tree Fields: An Efficient Non-parametric Random Field Model for Image Labeling, in Decision Forests for Computer Vision and Medical Image Analysis, Springer, 2013
- Jeremy Jancsary, Sebastian Nowozin, and Carsten Rother, Non-parametric CRFs for Image Labeling, in NIPS Workshop on Modern Nonparametric Methods in Machine Learning, December 2012
- Jeremy Jancsary, Sebastian Nowozin, and Carsten Rother, Loss-Specific Training of Non-Parametric Image Restoration Models: A New State of the Art, in 12th European Conference on Computer Vision, Springer, 2 August 2012