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/
- Christian Daniel, Jonathan Taylor, and Sebastian Nowozin, Learning Step Size Controllers for Robust Neural Network Training, AAAI - Association for the Advancement of Artificial Intelligence, 12 February 2016.
- Diane Bouchacourt, Sebastian Nowozin, and M. Pawan Kumar, Entropy-based Latent Structured Output Prediction, IEEE – Institute of Electrical and Electronics Engineers, 1 December 2015.
- Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, and Carsten Rother, A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems, in International Journal of Computer Vision, Springer, 1 November 2015.
- Jan Stühmer, Sebastian Nowozin, Andrew Fitzgibbon, Richard Szeliski, Travis Perry, Sunil Acharya, Daniel Cremers, and Jamie Shotton, Model-Based Tracking at 300Hz using Raw Time-of-Flight Observations, in ICCV 2015 - International Conference on Computer Vision, IEEE – Institute of Electrical and Electronics Engineers, 1 October 2015.
- Varun Jampani, Sebastian Nowozin, Matthew Loper, and Peter V. Gehler, The informed sampler: A discriminative approach to Bayesian inference in generative computer vision models, in Computer Vision and Image Understanding, vol. 136, pp. 32-44, Elsevier, 1 October 2015.
- Kevin Schelten, Sebastian Nowozin, Jeremy Jancsary, Carsten Rother, and Stefan Roth, Interleaved Regression Tree Field Cascades for Blind Image Deconvolution, IEEE – Institute of Electrical and Electronics Engineers, 6 January 2015.
- Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, and Christoph H. Lampert, Advanced Structured Prediction, MIT Press, 1 November 2014.
- Daniel Khashabi, Sebastian Nowozin, Jeremy Jancsary, and Andrew Fitzgibbon, Joint Demosaicing and Denoising via Learned Non-parametric Random Fields, in Transactions on Image Processing, IEEE – Institute of Electrical and Electronics Engineers, 1 October 2014.
- 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.