Welcome! My name is Sebastian Nowozin and I am a machine learning researcher, working at DeepMind. Until July 2022 I was working at Microsoft Research Cambridge (UK), where I led the Machine Intelligence research theme. Before joining Microsoft in June 2020 I was at the Brain team at Google Berlin, and before that I was leading the Machine Intelligence and Perception group at Microsoft Research Cambridge, UK. On this page you find publications and software related to my work.

I am always looking for interesting research collaborations and ideas, please get in touch and send me an email.

I develop novel algorithms and models for artificial intelligence and machine learning applications.

My current research interest is in the following areas: 1. the application of AI to advance science; 2. deep learning algorithms for uncertainty quantification; 3. the consequence of model misspecification on estimation and on decision problems.

In terms of methodology I am interested in structured prediction, deep learning, Monte Carlo methods, and decision theory.

My work has found industrial applications in computer vision, cloud-based machine learning, gesture recognition, statistical image processing, numerical optimization, and in time-of-flight imaging.

• September 2022. I joined DeepMind in London.
• July 2020. I have been interviewed in Sean Welleck's Thesis Review Podcast. Find the podcast recording here.

Former PhD Students

• Diane Bouchacourt, jointly supervised with Pawan Kumar, now at Facebook AI Research (Paris)
• Andreas Lehrmann, jointly supervised with Peter Gehler, now at Disney Research
• Lars Mescheder, jointly supervised with Andreas Geiger, now at Amazon
• Sergey Prokudin, jointly supervised with Peter Gehler, now at ETH Zurich
• John Bronskill, jointly supervised with Richard Turner
• Kevin Roth, jointly supervised with Aurelien Lucchi and Thomas Hofmann

recent publications

• please find my most recent publications at Google Scholar

2021

• Lorenzo Noci, Kevin Roth, Gregor Bachmann, Sebastian Nowozin, and Thomas Hofmann, "Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect", (arXiv preprint, PDF, reviews), 34th Annual Conference on Neural Information Processing Systems (NeurIPS 2021).
• Lorenzo Noci, Gregor Bachmann, Kevin Roth, Sebastian Nowozin, and Thomas Hofmann, "Precise characterization of the prior predictive distribution of deep ReLU networks", (arXiv preprint, PDF, reviews), 34th Annual Conference on Neural Information Processing Systems (NeurIPS 2021).
• John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, and Richard E. Turner, "Memory Efficient Meta-Learning with Large Images", (arXiv preprint, PDF, reviews, code for LITE, code for ORBIT), 34th Annual Conference on Neural Information Processing Systems (NeurIPS 2021).

2020

• Florian Wenzel (first author), Kevin Roth (first author), Bastiaan S. Veeling (first author), Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, and Sebastian Nowozin, "How Good is the Bayes Posterior in Deep Neural Networks Really?", (arXiv, PDF, supplementary materials, slides, video, and code) 37th International Conference on Machine Learning (ICML 2020).
• Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, and Sebastian Nowozin, "The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks", (arXiv preprint, PDF soon, presentation link soon), 37th International Conference on Machine Learning (ICML 2020).
• John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, and Richard E. Turner, "TaskNorm: Rethinking Batch Normalization for Meta-Learning", (arXiv preprint, PDF soon, presentation link soon), 37th International Conference on Machine Learning (ICML 2020).
• Jan Stühmer, Richard E. Turner, and Sebastian Nowozin, "Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations", (arXiv, PDF, and supplementary), International Conference on Artificial Intelligence and Statistics (AISTATS 2020).

2019

• Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua Dillon, Jie Ren, and Zachary Nado, "Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift", (PDF soon, arXiv) 32th Annual Conference on Neural Information Processing Systems (NeurIPS 2019).
• James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, and Richard E. Turner, "Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes", (PDF soon, arXiv) 32th Annual Conference on Neural Information Processing Systems (NeurIPS 2019).
• Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, and Cheng Zhang, "Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model", (PDF soon, arXiv) 32th Annual Conference on Neural Information Processing Systems (NeurIPS 2019).
• Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger, "Occupancy Networks: Learning 3D Reconstruction in Function Space", (PDF, supplementary, video, code, arXiv preprint), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2019).
• Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, and Cheng Zhang, "EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE", (PDF, arXiv) 36th International Conference on Machine Learning (ICML 2019).
• Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt, "Deterministic Variational Inference for Bayesian Neural Network", (PDF, reviews and discussions, arXiv preprint), Seventh International Conference on Learning Representations (ICLR 2019).
• Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner, "Meta-Learning Probabilistic Inference for Prediction", (PDF, reviews and discussions, arXiv preprint), Seventh International Conference on Learning Representations (ICLR 2019).

2018

• Daniel Coelho de Castro and Sebastian Nowozin, "Contextual Face Recognition with a Nested-Hierarchical Nonparametric Identity Model", (PDF, arXiv preprint), All of Bayesian Nonparametrics Workshop (BNP @ NeurIPS 2018).
• Sergey Prokudin, Peter Gehler, and Sebastian Nowozin, "Deep Directional Statistics: Pose Estimation with Uncertainty Quantification", (PDF, arXiv preprint), European Conference on Computer Vision 2018 (ECCV 2018).
• Daniel Coelho de Castro and Sebastian Nowozin, "From Face Recognition to Models of Identity: A Bayesian Approach to Learning about Unknown Identities from Unsupervised Data ", (PDF, arXiv preprint), European Conference on Computer Vision 2018 (ECCV 2018).
• Lars Mescheder, Andreas Geiger, and Sebastian Nowozin, "Which Training Methods for GANs do actually Converge?", (PDF, supplementary, code, arXiv preprint), Thirty-fifth International Conference on Machine Learning (ICML 2018).
• Sebastian Nowozin, "Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference", (PDF, reviews, code), Sixth International Conference on Learning Representations.
• Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman, "PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples", (PDF, reviews), Sixth International Conference on Learning Representations.
• Diane Bouchacourt, Ryota Tomioka, and Sebastian Nowozin, "Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations", (PDF, arXiv preprint), 32nd AAAI Conference on Artificial Intelligence (AAAI 2018).

2017

• Sergey Tulyakov, Andrew W. Fitzgibbon, Sebastian Nowozin, and "Hybrid VAE: Improving Deep Generative Models using Partial Observations", (PDF, arXiv preprint), Bayesian Deep Learning Workshop at NIPS 2017.
• Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann, "Stabilizing Training of Generative Adversarial Networks through Regularization", (PDF, supplementary (ZIP), reviews, code, arXiv preprint), 31th Annual Conference on Neural Information Processing Systems (NIPS 2017).
• Lars Mescheder, Sebastian Nowozin, and Andreas Geiger, "The Numerics of GANs", (PDF, supplementary, code, arXiv preprint), 31th Annual Conference on Neural Information Processing Systems (NIPS 2017).
• Sergey Prokudin, Daniel Kappler, Sebastian Nowozin, and Peter Gehler, "Learning to Filter Object Detections", (PDF), 39th German Conference on Pattern Recognition (GCPR 2017).
• Lars Mescheder, Sebastian Nowozin, and Andreas Geiger, "Adversarial Variational Bayes", (PDF, supplementary, code), 34th International Conference on Machine Learning (ICML 2017).
• Michael Schober, Amit Adam, Omer Yair, Shai Mazor, and Sebastian Nowozin, "Dynamic Time-of-Flight", (PDF, supplementary, video), Computer Vision and Pattern Recognition (CVPR 2017).
• Eric Brachmann, Alex Krull, Sebastian Nowozin, and Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother, "DSAC - Differentiable RANSAC for Camera Localization", (PDF, supplementary), Computer Vision and Pattern Recognition (CVPR 2017).
• Alex Krull, Eric Brachmann, Sebastian Nowozin, and Frank Michel, Jamie Shotton, Carsten Rother, "PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning", (PDF) Computer Vision and Pattern Recognition (CVPR 2017).
• Matej Balog, Alex Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Danny Tarlow, "DeepCoder: Learning to Write Programs", (preprint PDF, media coverage), accepted to International Conference on Learning Representations (ICLR 2017).

2016

• Christoph Dann, Katja Hofmann, and Sebastian Nowozin, "Memory Lens: How Much Memory Does an Agent Use?", (PDF), The 13th European Workshop on Reinforcement Learning (EWRL 2016).
• Sebastian Nowozin, Botond Cseke, and Ryota Tomioka, "f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization", (PDF, supplementary, arXiv preprint, MSR link), 30th Annual Conference on Neural Information Processing Systems (NIPS 2016).
• Diane Bouchacourt, M. Pawan Kumar, and Sebastian Nowozin, "DISCO Nets : DISsimilarity COefficients Networks", (PDF, supplementary materials, code, local copy, arXiv preprint), 30th Annual Conference on Neural Information Processing Systems (NIPS 2016).
• Olya Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, Manuel Costa, "Oblivious Multi-Party Machine Learning on Trusted Processors", (PDF), 25th USENIX Security Symposium.
• Amit Adam, Christoph Dann, Omer Yair, Shai Mazor, Sebastian Nowozin, "Bayesian Time-of-Flight for Realtime Shape, Illumination, and Albedo", (PDF, supplementary, video, publisher link, MSR link, arXiv preprint), IEEE Transactions on Pattern Analysis and Machine Intelligence.
• Christian Daniel, Jonathan Taylor, Sebastian Nowozin, "Learning Step Size Controllers for Robust Neural Network Training", (PDF), Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16).

2015

• Uwe Schmidt, Jeremy Jancsary, Sebastian Nowozin, Stefan Roth, Carsten Rother, "Cascades of Regression Tree Fields for Image Restoration", (PDF, publisher link, code (local copy), MSR link), IEEE Transactions on Pattern Analysis and Machine Intelligence.
• Diane Bouchacourt, Sebastian Nowozin, and M. Pawan Kumar, "Entropy-based Latent Structured Output Prediction", (PDF, supplementary), International Conference on Computer Vision (ICCV 2015).
• Jan Stühmer, Sebastian Nowozin, Andrew W. Fitzgibbon, Rick Szeliski, Travis Perry, Sunil Acharya, Daniel Cremers, and Jamie Shotton, "Model-Based Tracking at 300Hz using Raw Time-of-Flight Observations", (PDF, video), International Conference on Computer Vision (ICCV 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, Carsten Rother, "A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems", (PDF, supplementary 1, supplementary 2, project, publisher link), International Journal of Computer Vision (IJCV).
• Varun Jampani, Sebastian Nowozin, Matthew Loper, and Peter V. Gehler, "The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models", (PDF, arXiv preprint), Special Issue on Generative Models in Computer Vision, Computer Vision and Image Understanding.
• Kevin Schelten, Sebastian Nowozin, Jeremy Jancsary, Carsten Rother, and Stefan Roth, "Interleaved Regression Tree Field Cascades for Blind Image Deconvolution", (PDF, code), IEEE Winter Conference on Applications of Computer Vision (WACV 2015).

2014

• Yuting Wu, Daniel J. Holland, Mick D. Mantle, Andrew G. Wilson, Sebastian Nowozin, Andrew Blake, and Lynn F. Gladden, "A Bayesian Method to Quantifying Chemical Composition using NMR: Application to Porous Media Systems", (PDF), 22nd European Signal Processing Conference.
• Daniel Khashabi, Sebastian Nowozin, Jeremy Jancsary, and Andrew W. Fitzgibbon, "Joint Demosaicing and Denoising via Learned Non-parametric Random Fields", (PDF, new benchmark data (local copy), publisher link), IEEE Transactions on Image Processing, Vol. 23, Issue 12.
• Sebastian Nowozin, Peter V. Gehler, Jeremy Jancsary, and Christoph H. Lampert (Editors), "Advanced Structured Prediction", (publisher link), edited volume to be released November 2014, Neural Information Processing series, MIT Press.
• Sébastien Bratières, Novi Quadrianto, Sebastian Nowozin, Zoubin Ghahramani, "Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications", (PDF, supplementary, code), International Conference on Machine Learning (ICML 2014).
• Sebastian Nowozin, "Optimal Decisions from Probabilistic Models: the Intersection-over-Union Case", (PDF, supp. mat., code, MSR link), 2014 Conference on Computer Vision and Pattern Recognition (CVPR 2014).
• Andreas Lehrmann, Peter V. Gehler, Sebastian Nowozin, "Efficient Nonlinear Markov Models for Human Motion", (PDF), 2014 Conference on Computer Vision and Pattern Recognition (CVPR 2014).
• Sungwoong Kim, Chang D. Yoo, Sebastian Nowozin, Pushmeet Kohli, "Image Segmentation Using Higher-Order Correlation Clustering", (PDF, publisher link), IEEE Transactions on Pattern Analysis and Machine Intelligence.

2013

• Andreas Lehrmann, Peter V. Gehler, Sebastian Nowozin, "A Non-parametric Bayesian Network Prior of Human Pose", (PDF, poster, project, code, code local copy), 14th International Conference on Computer Vision (ICCV 2013).
• Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, Antonio Criminisi, "Decision Jungles: Compact and Rich Models for Classification", (PDF, supplementary, project), 27th Annual Conference on Neural Information Processing Systems (NIPS 2013).
• Po-Ling Loh, Sebastian Nowozin, "Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates", (PDF), 24th International Conference on Algorithmic Learning Theory (ALT 2013).
• Sebastian Nowozin, "Constructing Composite Likelihoods in General Random Fields", (PDF, openreview), ICML 2013 Workshop on Inferning: Interactions between Inference and Learning.
• Jeremy Jancsary, Sebastian Nowozin, Carsten Rother, "Learning Convex QP Relaxations for Structured Prediction", (PDF, code), 2013 International Conference on Machine Learning (ICML 2013).
• Uwe Schmidt, Carsten Rother, Sebastian Nowozin, Jeremy Jancsary, Stefan Roth, "Discriminative Non-blind Deblurring", (PDF, talk slides, poster, code (local copy)), 2013 Conference on Computer Vision and Pattern Recognition (CVPR 2013). Awarded the "best student paper" award.
• Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Jan Lellmann, Nikos Komodakis, Carsten Rother, "A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems", (PDF, project), 2013 Conference on Computer Vision and Pattern Recognition (CVPR 2013).

2012

• Jeremy Jancsary, Sebastian Nowozin, Carsten Rother, "Non-parametric CRFs for Image Labeling", (PDF, code), Modern Nonparametric Methods in Machine Learning, NIPS workshop paper.
• Sungwoong Kim, Sebastian Nowozin, Pushmeet Kohli, Chang D. Yoo, "Task-Specific Image Partitioning", (PDF), IEEE Transactions on Image Processing.
• Jeremy Jancsary, Sebastian Nowozin, Carsten Rother, "Loss-Specific Training of Non-Parametric Image Restoration Models: A New State of the Art", (PDF, supplementary, project, dataset, dataset (local copy), code), 12th European Conference on Computer Vision.
• Sebastian Nowozin, Jamie Shotton, "Action Points: A Representation for Low-latency Online Human Action Recognition", (PDF, MSR link), technical report.
• Andreas Müller, Sebastian Nowozin, Christoph H. Lampert, "Information Theoretic Clustering using Minimum Spanning Trees", (PDF), DAGM-OAGM 2012.
• Christoph Dann, Peter V. Gehler, Stefan Roth, Sebastian Nowozin, "Pottics - The Potts Topic Model for Semantic Segmentation", (PDF), DAGM-OAGM 2012.
• Sebastian Nowozin, "Improved Information Gain Estimates for Decision Tree Induction", (PDF, arXiv, video recording), 29th International Conference on Machine Learning (ICML 2012).
• Jeremy Jancsary, Sebastian Nowozin, Toby Sharp, Carsten Rother, "Regression Tree Fields -- An Efficient, Non-parametric Approach to Image Labeling Problems", (PDF, supplementary materials, project, code), 2012 Conference on Computer Vision and Pattern Recognition (CVPR 2012).
• Simon Fothergill, Helena Mentis, Pushmeet Kohli, Sebastian Nowozin, "Instructing People for Training Gestural Interactive Systems", (PDF), ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2012).

2011

• Sungwoong Kim, Sebastian Nowozin, Pushmeet Kohli, Chang D. Yoo, "Higher-Order Correlation Clustering for Image Segmentation", (PDF), 25th Annual Conference on Neural Information Processing Systems (NIPS 2011).
• Sebastian Nowozin, Carsten Rother, Shai Bagon, Toby Sharp, Bangpeng Yao, Pushmeet Kohli, "Decision Tree Fields", (PDF, supplementary materials, talk slides, poster, code, hard energy minimization instances (142MB)), 13th International Conference on Computer Vision (ICCV 2011).
• Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright (Editors), "Optimization for Machine Learning", (publisher link), edited volume published November 2011, Neural Information Processing series, MIT Press.
• Patrick Pletscher, Sebastian Nowozin, Pushmeet Kohli, Carsten Rother, "Putting MAP back on the map", (PDF, supplementary), 33rd Annual Symposium of the German Association for Pattern Recognition (DAGM 2011).
• Tutorial: Sebastian Nowozin and Christoph H. Lampert, "Structured Learning and Prediction in Computer Vision", (PDF, updated May 2011), Foundations and Trends in Computer Graphics and Vision series of now publishers. Tutorial slides online.
• Dhruv Batra, Sebastian Nowozin, Pushmeet Kohli, "Tighter Relaxations for MAP-MRF Inference: A Local Primal-Dual Gap based Separation Algorithm", (PDF), International Conference on Artificial Intelligence and Statistics (AISTATS 2011).
• Taesup Kim, Sebastian Nowozin, Pushmeet Kohli, Chang D. Yoo, "Variable Grouping for Energy Minimization", (PDF), IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011).
• Hiroto Saigo, Andre Altmann, Jasmina Bogojeska, Fabian Müller, Sebastian Nowozin, Thomas Lengauer, "Learning from Past Treatments and Their Outcome Improves Prediction of In Vivo Response to Anti-HIV Therapy", (PDF), Statistical Applications in Genetics and Molecular Biology, Vol. 10 (2011), Issue 1.

2010

• Sebastian Nowozin, Peter V. Gehler, and Christoph H. Lampert, "On Parameter Learning in CRF-based Approaches to Object Class Image Segmentation", (PDF, supplementary), 11th European Conference on Computer Vision (ECCV 2010).
• Tutorial: Carsten Rother and Sebastian Nowozin, "Higher-order Models in Computer Vision", (homepage, PDF slides), IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010).
• Sebastian Nowozin and Christoph H. Lampert, "Global Interactions in Random Field Models: A Potential Function Ensuring Connectedness", (PDF, publisher link), SIAM Journal on Imaging Sciences (SIIMS), Vol. 3, Issue 4, 2010.

2009

• Sebastian Nowozin, "Learning with Structured Data: Applications to Computer Vision", (PDF) PhD dissertation at the Technical University of Berlin.
  
• Peter V. Gehler and Sebastian Nowozin, "On Feature Combination Methods for Multiclass Object Classification", (PDF, talk slides, project), IEEE International Conference on Computer Vision (ICCV 2009).
• Paramveer S. Dhillon, Sebastian Nowozin, and Christoph H. Lampert, "Combining Appearance and Motion for Human Action Classification in Videos", (PDF), 1st International Workshop on Visual Scene Understanding (ViSU 09).
• Sebastian Nowozin and Stefanie Jegelka, "Solution Stability in Linear Programming Relaxations: Graph Partitioning and Unsupervised Learning", (PDF), International Conference on Machine Learning (ICML 2009).
• Sebastian Nowozin and Christoph H. Lampert, "Global Connectivity Potentials for Random Field Models", (PDF, additional material, project), IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009).
• Peter V. Gehler and Sebastian Nowozin, "Let the Kernel Figure it Out; Principled Learning of Pre-processing for Kernel Classifiers", (PDF, project) IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009).

2008

• Sebastian Nowozin and Koji Tsuda, "Frequent Subgraph Retrieval in Geometric Graph Databases", (PDF, project) IEEE International Conference on Data Mining (ICDM 2008).
• Peter V. Gehler and Sebastian Nowozin, "Infinite Kernel Learning", (PDF, project), Max Planck Institute for Biological Cybernetics Techreport TR-178.
• Hiroto Saigo, Sebastian Nowozin, Tadashi Kadowaki, Taku Kudo and Koji Tsuda, "gBoost: A Mathematical Programming Approach to Graph Classification and Regression", (PDF, project), Machine Learning Journal, Springer, Vol 75, Number 1.
• Sebastian Nowozin and Gökhan BakIr, "A Decoupled Approach to Exemplar-based Unsupervised Learning", (PDF, project), 25th International Conference on Machine Learning (ICML 2008).
• Paramveer S. Dhillon, Sebastian Nowozin, and Christoph H. Lampert, "Combining Appearance and Motion for Human Action Classification in Videos", (PDF), Max Planck Institute for Biological Cybernetics Techreport TR-174.
• Sebastian Nowozin and Koji Tsuda, "Frequent Subgraph Retrieval in Geometric Graph Databases", (PDF, project), Max Planck Institute for Biological Cybernetics Techreport TR-180, extended version of ICDM 2008 paper.

2007

• Sebastian Nowozin, Gökhan BakIr, and Koji Tsuda, "Discriminative Subsequence Mining for Action Classification", (PDF, project), IEEE International Conference on Computer Vision (ICCV 2007).
• Sebastian Nowozin, Koji Tsuda, Takeaki Uno, Taku Kudo, and Gökhan BakIr, "Weighted Substructure Mining for Image Analysis", (PDF, additional material, project), IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007).

• Area chair: NIPS/NeurIPS (2012, 2014, 2015, 2017, 2018, 2019, 2020), ICML (2015, 2017, 2019, 2020), UAI (2020), AISTATS (2018), CVPR (2018), ECCV (2016, 2018)
• Editorial activities: JMLR action editor (2013-), IJCV associate editor (2014-2018), TPAMI associate editor (2014-2017)
• Reviewer, machine learning conferences: NIPS (2008-2011, 2013, 2016, best reviewer award 2008), ICML (2009-2014, 2016), GCPR (2013-2016), AISTATS (2010), UAI (2009), ECML/PKDD (2010)
• Reviewer, computer vision conferences: CVPR (2010-2017), ECCV (2010, 2012, 2014), ICCV (2011, 2013, 2015)
• Reviewer, machine learning journals: JMLR, TNN, ADAC
• Reviewer, computer vision journals: TPAMI, IJCV
• PC member, workshops: Machine Learning Open Source Software (at ICML 2010), Structured Models in Computer Vision (at CVPR 2010), 1st International Workshop on Parts and Attributes (at ECCV 2010), HiPot: Workshop on Higher-Order Models and Global Constraints in Computer Vision (at ECCV 2012).
• Workshop chair: Data Learning and Inference (DALI 2018)
• Workshop organizer: Uncertainty and Robustness in Deep Visual Learning (at CVPR 2019), Theory of Generative Adversarial Networks (at DALI 2017), Optimization for Machine Learning (OPT 2008-2011 at NIPS), Structured Prediction: Tractability, Learning, and Inference (at CVPR 2013), International Workshop on Graphical Models in Computer Vision (GMCV 2014) (at ECCV 2014). Machine Learning for Intelligent Image and Video Processing (at ICCV 2015).

• MSR Demosaicing dataset (local copy).
• RTF Non-blind Deblurring code, (local copy).
• Intersection-over-Union optimization code.
• MSRC-12 Kinect gesture data set. Details are described in our CHI 2012 paper.
• Decision Tree Fields - C++ implementation of the ICCV 2011 model
• Regression Tree Fields - C++ implementation
• grante - C++ discrete factor graph estimation and inference library
• Tuwo - C++ computer vision library
• gboost - graph mining and classification
• pboost - sequence mining and classification
• freqgeo - geometric subgraph mining
• infex - exemplar-based models for unsupervised learning
• lpstability - multicut LP relaxation code

• TU-Berlin university homepage (from 2001-2006)

You can reach me by email at nowozin@gmail.com