The Software: Home : Overview : API and Architecture : Components : Toolkit
The Group: Team : Outcomes : Publications
Main Achievements
Some theoretical and more applied outcomes of our recent research are listed here.
- Kernel Tests of Independence: The technology referred to as "Hilbert space embeddings of distributions" uses a statistic called "maximum mean discrepency" (MMD) to test if two probability distributions are different. This is a general automatic technique for testing when data comes from different sources, and works in the context of complex domains such as bioinformatics or medical imaging where previous statistical methods offered no alternative. This will allow more reliable analysis of such data. See Gretton et al. 2008, below.
- Optimisation Algorithms for Data Modelling: A common data analysis task is model fitting, which means automatically adapting a complex computer program so that it not only better handles the million or so previous records one may have processed, but should also work better on future records. Applications here include medical diagnosis, targetted advertising, and tax or consumer fraud detection. We have developed a suite of algorithms suited to this task. They have the ability to work on large batches of data, or to work on streaming data of the kind available from the web or a manufacturing process. These algorithms are available as software within Elefant, are reported in our NIPS 2008 publications, and are highly scalable.
- Recommender Systems: Search sites such as Google and Yahoo, and consumer recommendation sites such as Amazon and movies sites need ways of ranking the results they return to users. We have developed efficient ranking algorithms that can be adapted affectively from user data about preferences, and user click-through data. The algorithms were the first so developed to scale to the larger sizes required for internet use, so would allow intermediate-sized companies to provide quality search capability. See Le and Smola, 2007, and Weimer et al., 2008.
- Learning for Pattern Recognition: A computerized method for recognizing patterns in digital images was developed which is 10 times faster than the best previous method while enjoying the same accuracy. This has many practical applications, for example detecting people in digital videos for surveillance purposes. In the future, technologies like this would be part of systems that could reliably and automatically search for a specific "visual query" , like "a white man with a black jacked carrying a gun", in a huge collection of video recordings from surveillance cameras. See McAuley et al., 2008.
Software
As well as our academic publications, we have substantial output in the form of software released in the Elefant platform, as described elsewhere.Highlighted Publications
Full publications are available at our publications site. Here we list a few of our more significant publications. Other publications are available from machine learning related conferences such as NIPS, ICML, ECML, AAAI, KDD, ISMB, CVPR, SIGIR, ICCV and CIKM.2008
Ryabko, D. and Hutter, M., Predicting Non-Stationary Processes, Applied Mathematics Letters, 21 (5) , pp.477--482, 2008.
Shen, Hao, Hüper, Knut, and Kleinsteuber, Martin, Local Convergence Analysis of FastICA and Related Algorithms, IEEE Transactions on Neural Networks, 19 (6) , pp.1022--1032, 2008.
McAuley, J., Caetano, T. S., and Barbosa, M. S., Graph rigidity, cyclic belief propagation and point pattern matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008.
Hutter, M., Algorithmic Complexity, Scholarpedia, 3 (1) , pp.2573, 2008.
Vishwanathan, S.V.N., Borgwardt, K. M., Schraudolph, N., and Kondor, I. R., On Graph Kernels, Journal of Machine Learning Research, 2008.
Astashkin, S. and Sunehag, P., Real method of interpolation on subcouples of codimension one, Studia Mathematica, 185 (2) , pp.151-168, 2008.
Hofmann, T., Schölkopf, B., and Smola, A. J., Kernel methods in machine learning, Annals of Statistics, 36 (3) , pp.1171-1220, 2008.
Gretton, A., Fukumizu, K., Teo, C. H., Song, L., Schölkopf, B., and Smola, A. J., A Kernel Statistical Test of Independence, Advances in Neural Information Processing Systems 20, J.C. Platt and D. Koller and Y. Singer and S. Roweis (Eds.), MIT Press, Cambridge, MA 2008.
Weimer, M., Karatzoglou, A., Le, Q. V., and Smola, A. J., COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking , Advances in Neural Information Processing Systems 20, J.C. Platt and D. Koller and Y. Singer and S. Roweis (Eds.), MIT Press, Cambridge, MA 2008.
2007
Hutter, M., Exact Bayesian Regression of Piecewise Constant Functions, Bayesian Analysis, 2 (4) , pp.635--664, 2007.
Chernov, Alexey, Hutter, M., and Schmidhuber, J., Algorithmic Complexity Bounds on Future Prediction Errors, Information and Computation, 205 (2) , pp.242--261, 2007.
Zhou, J., Cheng, L., and Bischof, W. F., Online learning with Novelty Detection in Human-guided Road Tracking, IEEE Transactions on Geoscience and Remote Sensing (TGRS), 45 (12) , pp.3967--3977, 2007.
Vishwanathan, S.V.N., Smola, A. J., and Vidal, R., Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes, International Journal of Computer Vision, 73 (1) , Springer-Verlag, Netherlands pp.95--119, 2007.
Helmke, Uwe, Hüper, K., Lee, Pei Yean, and Moore, John, Essential Matrix Estimation Using Gauss-Newton Iterations on a Manifold, International Journal of Computer Vision, 74 (2) , pp. 117--136, 2007.
Le, Q. V. and Smola, A. J., Direct Optimization of Ranking Measures, Journal of Machine Learning Research, 2007.
Hutter, M., Legg, S., and Vitányi, Paul M. B., Algorithmic Probability, Scholarpedia, 2 (8) , pp.2572, 2007.
Hutter, M. and Muchnik, Andrej A., On Semimeasures Predicting Martin-Löf Random Sequences, Theoretical Computer Science, 382 (3) , pp.247--261, 2007.
Song, L., Smola, A. J., Gretton, A., Bedo, J., and Borgwardt, K. M., Feature selection via dependence maximization, Journal of Machine Learning Research, 2007.
Hutter, M., On Universal Prediction and Bayesian Confirmation, Theoretical Computer Science, 384 (1) , pp.33--48, 2007.
Bray, Matthieu, Koller-Meier, Esther, Schraudolph, N., and Van Gool, Luc, Fast Stochastic Optimization for Articulated Structure Tracking, Image and Vision Computing, 25 (3) , pp.352--364, 2007.
McAuley, J., Costa, L. da F., and Caetano, T. S., Rich club phenomenon across complex network hierarchies, Applied Physics Letters, 91 (084103) , 2007.
Machine Learning with Structured Outputs, G. Bakir and B. Taskar and S.V.N. Vishwanathan and T. Hofmann and B. Schölkopf and A. J. Smola (Eds.), MIT Press, 2007.
Hüper, K. and Leite, Fatima Silva, On the Geometry of Rolling and Interpolation Curves on S^n , SO_n , and Grassmann Manifolds, Journal of Dynamical and Control Systems, 13 (4) , pp.467--502, 2007.
Günter, S., Schraudolph, N., and Vishwanathan, S.V.N., Fast Iterative Kernel Principal Component Analysis, Journal of Machine Learning Research, 8 , pp.1893--1918, 2007.