 We pursue research on automated reasoning, adaptation, and the theories and applications of decision making and learning. Our research goals include learning from data and data mining. By building software that automatically learns from data, we design applications that have new functions and flexibility. Our research focuses on using statistical methods for the development of more advanced, intelligent computer systems. |
- Spoken Language UnderstandingSpoken language understanding (SLU) is an emerging field in between the areas of speech processing and natural language processing. The term spoken language understanding has largely been coined for targeted understanding of human speech directed at machines. This project covers our research on SLU tasks such as domain detection, intent determination, and slot filling, using data-driven methods.
- Social Analytics: From Demographics to PsychometricsWe investigate how people's behaviour online can be characterized in terms of psychometric measurements such as the Big-5 personality traits openness, conscientiousness, extraversion, agreeableness, and neuroticism as well as general intelligence and satisfaction-with-life. We investigate patterns of Facebook usage, website preferences, query logs, and Facebook Likes and look for interesting correlations which can be used to predict users behaviours, preferences or characteristics.
- Distribution Modeller: Environmental Modelling at the Speed of ThoughtDistribution Modeller (temporary name only!) is CEES' end-to-end browser tool that lets the researcher to rapidly import data, supplement that data with environmental info from FetchClimate, specify an arbitrary model by point and click or in code, parameterize the model against the data using Filzbach, make and visualize predictions with a full propagation of parameter uncertainty – then package and share everytihng, in a way that is inspectable, repeatable, and modifiable.
- Amplifying Learning through Electronic TextbooksWith the emergence of abundant online content, cloud computing, and electronic reading devices, textbooks are poised for transformative changes. Taking into account the vast amount of existing textbooks designed for traditional printed medium and the potential for enabling new kinds of functionalities through the medium of electronic textbooks, we present the results of our research into algorithmically diagnosing and enhancing the quality of textbooks.
Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon, Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images, in Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, June 2013
Tiezheng Ge, Kaiming He, Qifa Ke, and Jian Sun, Optimized Product Quantization for Approximate Nearest Neighbor Search, in CVPR 2013, IEEE Computer Society, June 2013
Shipra Agrawal and Navin Goyal, Thompson Sampling for contextual bandits with linear payoffs, in 30th International Conference on Machine Learning (ICML), June 2013
P. Kontschieder, P. Kohli, J. Shotton, and A. Criminisi, GeoF: Geodesic Forests for Learning Coupled Predictors, in Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, June 2013
Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig, Linguistic Regularities in Continuous SpaceWord Representations, in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-2013), Association for Computational Linguistics, 27 May 2013
