| Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management. A Artikis, M Weidlich, F Schnitzler, I Boutsis, T Liebig, N Piatkowski, ... EDBT 14, 712-723, 2014 | 92 | 2014 |
| Traveling time prediction in scheduled transportation with journey segments A Gal, A Mandelbaum, F Schnitzler, A Senderovich, M Weidlich Information Systems 64, 266-280, 2017 | 67 | 2017 |
| Heterogeneous Stream Processing for Disaster Detection and Alarming F Schnitzler, T Liebig, S Mannor, G Souto, S Bothe, H Stange IEEE International Conference on Big Data, 914-923, 2014 | 21 | 2014 |
| Combining a Gauss-Markov model and Gaussian process for traffic prediction in Dublin city center. F Schnitzler, T Liebig, S Mannor, K Morik EDBT/ICDT Workshops, 373-374, 2014 | 19 | 2014 |
| L1-based compression of random forest models A Joly, F Schnitzler, P Geurts, L Wehenkel 20th European Symposium on Artificial Neural Networks, 2012 | 17 | 2012 |
| Off-policy model-based learning under unknown factored dynamics A Hallak, F Schnitzler, T Mann, S Mannor International Conference on Machine Learning, 711-719, 2015 | 16 | 2015 |
| Power optimization for wireless autonomous transducer solutions V Pop, J van de Molengraft, F Schnitzler, J Penders, R van Schaijk, ... Proceedings of the PowerMEMS and MicroEMS Workshop, Sendai, Japan 9, 12, 2008 | 15 | 2008 |
| Intelligent synthesis and real-time response using massive streaming of heterogeneous data (insight) and its anticipated effect on intelligent transport systems (its) in dublin … D Kinane, F Schnitzler, S Mannor, T Liebig, K Morik, J Marecek, B Gorman, ... ITS, Dresden, Germany, 2014 | 13 | 2014 |
| Heterogeneous stream processing and crowdsourcing for traffic monitoring: Highlights F Schnitzler, A Artikis, M Weidlich, I Boutsis, T Liebig, N Piatkowski, ... Joint European Conference on Machine Learning and Knowledge Discovery in …, 2014 | 13 | 2014 |
| Sub-quadratic Markov tree mixture learning based on randomizations of the Chow-Liu algorithm S Ammar, P Leray, F Schnitzler, L Wehenkel | 11 | 2010 |
| Sensor selection for crowdsensing dynamical systems F Schnitzler, JY Yu, S Mannor Artificial Intelligence and Statistics, 829-837, 2015 | 10 | 2015 |
| Optimal sample selection for batch-mode reinforcement learning E Rachelson, F Schnitzler, L Wehenkel, D Ernst | 9 | 2011 |
| Time-aware user identification with topic models C Lesaege, F Schnitzler, A Lambert, JR Vigouroux 2016 IEEE 16th International Conference on Data Mining (ICDM), 997-1002, 2016 | 6 | 2016 |
| Towards sub-quadratic learning of probability density models in the form of mixtures of trees F Schnitzler, P Leray, L Wehenkel | 6 | 2010 |
| Efficiently approximating Markov tree bagging for high-dimensional density estimation F Schnitzler, S Ammar, P Leray, P Geurts, L Wehenkel Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011 | 5 | 2011 |
| Insight: Dynamic traffic management using heterogeneous urban data N Panagiotou, N Zygouras, I Katakis, D Gunopulos, N Zacheilas, I Boutsis, ... Joint European Conference on Machine Learning and Knowledge Discovery in …, 2016 | 4 | 2016 |
| Crowd sourcing, with a few answers: Recommending commuters for traffic updates E Daly, M Berlingerio, F Schnitzler Proceedings of the 9th ACM Conference on Recommender Systems, 253-256, 2015 | 3 | 2015 |
| On predicting traveling times in scheduled transportation A Gal, A Mandelbaum, F Schnitzler, A Senderovich, M Weidlich Proceedings of the 2nd International Conference on Mining Urban Data-Volume …, 2015 | 3 | 2015 |
| Insight-driven Crisis Information-Preparing for the Unexpected using Big Data. H Stange, S Steenhoek, S Bothe, F Schnitzler ISCRAM, 2015 | 2 | 2015 |
| Compressible Reparametrization of Time-Variant Linear Dynamical Systems N Piatkowski, F Schnitzler Solving Large Scale Learning Tasks. Challenges and Algorithms, 234-250, 2016 | 1 | 2016 |
Shie MannorProfessor of Electrical Engineering, TechnionVerified email at technion.ac.il
