**Ata Kaban - Publications**

**
**

**2017**

- A. Kaban, R.J. Durrant. Structure-aware error bounds for linear classification with the
zero-one loss.
**arXiv:1709.09782** - A. Kaban. On
Compressive Ensemble Induced Regularization: How Close
is the Finite Ensemble Precision Matrix to the Infinite Ensemble? The 28th
International Conference on Algorithmic Learning Theory (
**ALT 2017**), Kyoto University, Japan, 15-17 October 2017. - A. Turl, A. Kaban. Joint Blind
Source Separation and Declipping: A Geometric
Approach for Time Disjoint Sources. The 17-th International Symposium
on Signal Processing and Information Technology (ISSPIT 2017).

**2016**

- M.L.
Sanyang, A. Kaban. REMEDA: Random Embedding EDA for
**PPSN**XIV), 17-21 September, Edinburgh, Scotland, 2016. Nominated for best paper award. - M.L.
Sanyang, R.J. Durrant
and A. Kaban. How effective is
Cauchy-EDA in high dimensions? IEEE Congress on Evolutionary Computation 2016 (
**CEC**-2016), 24-29 July, Vancouver, Canada, 2016. - Qi Xu, M.L. Sanyang,
A.Kaban. Large Scale Continuous
EDA Using Mutual Information. IEEE Congress on Evolutionary
Computation 2016 (
**CEC**-2016), 24-29 July, Vancouver, Canada, 2016. - A. Kaban, J. Bootkrajang, R.J. Durrant. Towards Large Scale Continuous
EDA: A Random Matrix Theory Perspective.
**Evolutionary Computation**24(2): 255-291, 2016, MIT Press. Code - FM Schleif, A Kaban, P Tino. Finding
Small Sets of Random Fourier Features for Shift-Invariant Kernel
Approximation. IAPR Workshop on Artificial Neural Networks in Pattern
Recognition, 42-54.

**2015**

- A. Kaban. Improved Bounds on
the Dot Product under Random Projection and Random Sign Projection. 21
^{st}ACM SIGKDD Conference on Knowledge Discovery and Data Mining (**KDD2015**), 10-13 August, Sydney, pp. 487-496. Slides. - A. Kaban. A New
Look at Nearest Neighbours: Identifying Benign
Input Geometries via Random Projections. The 7th
Asian Conference on Machine Learning (
**ACML 2015**), Hong Kong 20-22 November 2015,**Journal of Machine Learning Research-Proceedings Track**45: 65-80, 2015. Slides. - A. Kaban. Non-asymptotic
Analysis of Compressive Fisher Discriminants in terms of the Effective
Dimension. The 7th Asian Conference on Machine Learning (
**ACML 2015**), Hong Kong 20-22 November 2015,**Journal of Machine Learning Research-Proceedings Track**45: 17-32, 2015. Slides. - M. Sanyang, A. Kaban.
**CEC-**2015.**Runner-Up Student Paper Award**. - R.J. Durrant, A. Kaban. Random
Projections as Regularizers: Learning a Linear
Discriminant from Fewer Observations than Dimensions.
**Machine Learning**99(2), pp. 257-286, 2015. - B.
Frenay, A. Kaban. Editorial:
Special issue on advances in learning with label noise.
**Neurocomputing**, 2015. Link.

**2014
**

- A. Kaban. New
Bounds for Compressive Linear Least Squares Regression. The 17-th
International Conference on Artificial Intelligence and Statistics (
**AISTATS 2014**), 22-25 April 2014, Reykjavik, Iceland,**Journal of Machine Learning Research-Proceedings Track**, 33: 448-456. - J. Bootkrajang, A. Kaban. Learning Kernel
Logistic Regression in the Presence of Class Label Noise.
**Pattern Recognition**. Vol. 47, Issue 11, November 2014, pp. 3641-3655. - B.
Frenay, A. Kaban. A Comprehensive
Introduction to Label Noise. ESANN 2014 proceedings.
- H.
Al-Baity,S. Meshoul, A. Kaban, L. AlSafadi. Quantum Behaved
Particle Swarm Optimisation for Data Clustering with Multiple Objectives.
SoCPaR 2014, pp. 215-220.
- M.L.
Sanyang, A. Kaban. Multivariate Cauchy EDA
Optimisation. IDEAL2014, pp. 449-456.
- M.L.
Sanyang, Hanno Muehlbrandt,
A. Kaban.

**2013**

- A. Kaban and R.J. Durrant. Dimension-Adaptive
Bounds on Compressive FLD Classification. The 24th
International Conference on Algorithmic Learning Theory (
**ALT 2013**), pp. 294-308. Slides. - R.J. Durrant and A. Kaban. Random
Projections as Regularizers: Learning a Linear
Discriminant Ensemble from Fewer Observations than Dimensions. The 5th Asian Conference on Machine Learning (
**ACML 2013**),**Journal of Machine Learning Research****-****Proceedings Track,**29: 17-32.**Best Paper Award**. - J. Bootkrajang and A. Kaban. Boosting in the Presence
of Label Noise. Proceedings of the 29
^{th}Conference on Uncertainty in Artificial Intelligence (**UAI 2013**), pp. 82-90, 2013. - R.J. Durrant and A. Kaban. Sharp
Generalization Error Bounds for Randomly-projected Classifiers. 30th International Conference on Machine Learning (
**ICML 2013**),**Journal of Machine Learning Research****-****Proceedings Track**28(3):693-701, 2013. Slides. Video.

[Erratum: in the printed version Sec. 4.1.2 line 3, tan(theta) should read tan(theta/2).] - A. Kaban, J. Bootkrajang, R.J. Durrant. Towards Large Scale
Continuous EDA: A Random Matrix Theory Perspective. 22
^{nd}International Conference on Genetic Algorithms and 18^{th}Annual Genetic Programming Conference (**GECCO 2013**), Amsterdam, The Netherlands, July 6-10.**Best Paper Award in the GDS/EDA tracks**. Slides.[Erratum: in the printed version, in l.h.s. of eq. (8) sigma^4 should read k sigma^4; also typo in r.h.s. of eq. (7) missed the k-dependent factors. All corrected on the above link.]

- A. Kaban. Fractional-norm Regularisation: Learning with Very Few Relevant
Features.
**IEEE Transactions on Neural Networks and Learning Systems**, Volume 24, Issue 6, 2013, pp. 953-963. - J. Bootkrajang and A. Kaban. Classification of Mislabelled Microarrays using Robust Sparse Logistic
Regression.
**Bioinformatics. 29(7): 870-877, 2013**. Code - J. Bootkrajang and A. Kaban. Learning a Label-noise Robust Logistic Regression:
Analysis and Experiments. Proc. IDEAL 2013, LNCS 8206, 2013. (c)
Springer
- S. Ali-Pitchay and A. Kaban. Estimating the Regularisation Parameter in Huber MRF for Image
Resolution Enhancement. Proc. IDEAL 2013, LNCS 8206, pp. 295-302,
2013. (c) Springer
- S. Ali-Pitchay and A. Kaban. Single-frame
Signal Recovery using a Similarity Prior. Mathematical Methodologies
in Pattern Recognition and Machine Learning, Vol. 30, 2013, pp.83-98. (c)
Springer
- S. Mylavarapu and A. Kaban
*.*Random projections versus random feature selection for classification of high dimensional data. Proceedings of the UK Workshop on Computational Intelligence (UKCI 2013), September 9-11, University of Surrey, Guilford, UK, pp. 305-312. - Z.Ma and A. Kaban.
K-Nearest-Neighbours
with a novel similarity measure for intrusion detection. Proceedings
of the UK Workshop on Computational Intelligence (UKCI 2013), September
9-11, University of Surrey, Guilford, UK, pp. 266-271.
- A. Kaban. A new look at compressed ordinary least squares. ICDM Workshops 2013: 482-488.

**2012**

- J. Bootkrajang and A. Kaban. Label-noise Robust
Logistic Regression and its Applications. Proc
**. ECML-PKDD(**1) 2012, pp. 143-158. Code Data - S. Ali-Pitchay and A. Kaban.
Multi-task
Signal Recovery by Higher Level Hyper-parameter Sharing. Proc. 21
^{st}Int. Conference on Pattern Recognition**(ICPR 2012**). - H. Al-Baity, S. Meshoul and A. Kaban.
On Extending
Quantum-Behaved Particle Swarm Optimization to Multi-Objective Context.
Proc. IEEE Congress on Evolutionary Computation (
**CEC 2012**), 2012, pp. 1-8. - H. Al-Baity, S. Meshoul and A. Kaban. Constrained
multi-objective optimization using a quantum behaved particle swarm. Neural Information Processing, Lecture Notes in
Computer Science, Vol. 7665, 2012, pp. 456-464.
- R.J. Durrant and A. Kaban. Error Bounds for
Kernel Fisher Linear Discriminant in Gaussian Hilbert Space. 15-th
International Conference on Artificial Intelligence and Statistics (
**AiStats****2012), Journal of Machine Learning Research-Proceedings Track**22: 337-345, 2012. - S. Ali-Pitchay and A. Kaban. Single-frame
signal recovery using a similarity prior based on Pearson type VII MRF.
1-st International Conference on Pattern Recognition Applications and
Methods (ICPRAM 2012), Vilamoura, Algarve,
Portugal, 6-8 February 2012.
- R.J. Durrant and A. Kaban.
A tight bound on
the performance of Fisher`s
linear discriminant in randomly projected data spaces
**. Pattern Recognition Letters**, ICPR2010 Awards special issue, 33(7):911-919, 2012. - A. Kaban and S. Ali Pitchay.
Single-frame Image
Recovery using a Pearson type VII MRF
**. Neurocomputing,**MLSP2010 special issue**.**80: 111-119, 2012. - A. Kaban. Non-parametric Detection
of Meaningless Distances in High-Dimensional Data.
**Statistics****and Computing**. 22(1): 375-385. code

**2011**

- A. Kaban. On the Distance
Concentration Awareness of Certain Data Reduction Techniques.
**Pattern Recognition**. Vol. 44, Issue 2, Feb 2011, pp.265-277. - J. Bootkrajang and A. Kaban.
Multiclass learning with
labeling errors. 19-th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN 2011),
23-29 April 2011, Bruges, Belgium.

**2010**

- R.J. Durrant and A. Kaban.
Compressed Fisher
Linear Discriminant Analysis: Classification of Randomly Projected Data.
Proceedings
of the 16
^{th}ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (**KDD 2010**) July 25-28, 2010, Washington, DC, USA; Session track: KDD methodology; pp.1119-1128, 2010. - R.J. Durrant and A. Kaban.
A bound on the
performance of LDA in randomly projected data spaces. Proc. 20
^{th}International Conference on Pattern Recognition (**ICPR 2010**) August 23-26, 2010, Istanbul, Turkey -**IBM Best Student Paper Award in the Pattern Recognition and Machine Learning track.** - J. Sun and A. Kaban. A Fast
Algorithm for Robust Mixtures in the Presence of Measurement Errors.
**IEEE Transactions on Neural Networks**. Vol. 21, Issue 8, pp. 1206-1220, 2010. - J. Sun, A. Kaban, J.M. Garibaldi. Robust Mixture
Modeling Using the Pearson Type VII Distribution.
**Pattern Recognition Letters**. . Vol. 31, Issue 16, Dec 2010, pp. 2447-2454. - A. Kaban and S.Ali-Pitchay. Single-frame Image
Super-resolution using a Pearson type VII MRF. Proc. IEEE
International Workshop on Machine Learning for Signal Processing (
**MLSP 2010**). August 29 - September 1, 2010, Kittila, Finland.

**2009**

- R. J. Durrant and A. Kaban. When is
'Nearest Neighbor' Meaningful: A Converse Theorem and Implications
**Journal of Complexity.**Volume 25, Issue 4, August 2009, pp. 385-397. Nr 3 in Science Direct 25 Hottest Articles for Journal of Complexity Oct 2009-Sept 2010. - E. Bingham, A. Kaban, M. Fortelius. The aspect Bernoulli model:
Multiple causes of presences and absences.
**Pattern Analysis and Applications**Vol. 12, No 1 / Feb, pp. 55-78, 2009. code - D. Peavoy and A. Kaban.
Bayesian
Learning of Genetic Network Structure in the Space of Differential
Equation Models. BioSysBio Conference, Cambridge, 23 - 25
March, 2009. slides

**2008**

- X. Wang and A. Kaban. A dynamic bibliometric
model for identifying online communities.
**Data Mining and Knowledge Discovery**, vol. 16 Nr. 1, pp. 67-107, 2008. - A. Kaban and E. Bingham. Factorisation
and denoising of 0-1 data: A variational
approach.
**Neurocomputing**, Vol 71, Issues 10-12, June 2008, pp. 2291-2308. - A. Kaban and R.J. Durrant.
Learning with Lq<1 vs L1-norm regularization with exponentially
many irrelevant features. Proc. of the 1
^{9th}European Conference on Machine Learning**(ECML08**), 15-19 Sept 2008, Antwerp, Belgium. W. Daelemans et al. (eds.): LNAI 5211, pp. 580-596. (c) Springer. slides code - A. Kaban. A probabilistic neighborhood
translation approach for non-standard text categorization. Proc.
Discovery Science (DS08), 13-16 October 2008, Budapest, Hungary. LNAI (c)
Springer, J.-F Boulicaut, M.R Berthold and T.
Horvath (eds): LNAI
5255, pp. 332-343, 2008. (c) Springer.

**2007 **

- A. Kaban. Predictive Modelling of
Heterogeneous Sequence Collections by Topographic Ordering of Histories.
**Machine Learning Journal**, Vol. 68, Nr 1 / July, 2007, pp.63-95. (c) Springer. code - A. Kaban. On Bayesian
Classification with Laplace Priors.
**Pattern Recognition Letters** - M. Harva and A. Kaban.
Variational Learning for Rectified Factor Analysis.
**Signal Processing**, 87(3), pp.509-527. (c) Elsevier, 2007 - L. Nolan, S. Raychaudhury and A. Kaban. Young
stellar populations in early-type galaxies in the Sloan Digital Sky Survey.
**Mon. Not. of the Royal Astron. Soc****.**, 375 (1), 381-387, Blackwell Publisher, 2007. code - J. Sun, A. Kaban and S. Raychaudhury. Robust
mixtures in the presence of measurement errors. In Proceedings of the
24-th Annual International Conference on Machine Learning 2007 (
**ICML07**), (Ed.) Z. Ghahramani. June 20-24, Oregon State University, Corvallis, OR, USA, pp. 847-854. slides code - J. Sun, A. Kaban and S. Raychaudhury. Robust visual mining of
data with error information. The 11-th European Conference on
Principles and Practice of Knowledge Discovery in Databases (
**PKDD07**). 17-21 September 2007, Warsaw, Poland, pp.573-580.

**2006**

- LA Nolan, M Harva, A Kaban
and S Raychaudhury, A data-driven
Bayesian approach to finding young stellar populations in early-type
galaxies from their UV-optical spectra,
**Mon. Not. of the Royal Astron. Soc.**, Blackwell Publisher. 366(1), pp. 321-338, 2006

- A Kaban and X Wang. Deconvolutive
Clustering of Markov States. The 17th
European Conference on Machine Learning (
**ECML06**), LNAI 4212, pp. 246-257, 2006. (c) Springer; (Eds.) J Fuernkranz, T Scheffer and M Spiliopoulou. slides - A Kaban
and E Bingham. ICA-based Binary Feature
Construction. Proc. of the 6-th International Conference on
Independent Component Analysis and Blind Source Separation (
**ICA06**). Charleston, March 5-8, South Carolina, USA. (c) Springer. Eds: J Rosca, D Erdogmus, J.C Principe, S Haykin. pp. 140-148,2006. LNCS 3889, ISBN 3-540-32630-8. slides

- A Kaban, J Sun, S Raychaudhury,
L Nolan. On class visualisation for high dimensional data: Exploring
scientific data sets. Proc. of the 9-th
International Conference on Discovery Science (
**DS06**), October 2006, Barcelona, Spain. LNAI 4265, (c) Springer. slides - X Wang and A Kaban. Model-based Estimation
of Word Saliency in Text. Proc. of the 9-th International Conference on
Discovery Science (
**DS06**), October 2006, Barcelona, Spain. LNAI 4265, (c) Springer. - X Wang and A Kaban. State Aggregation in
Higher-Order Markov Chains for Finding Online Communities. 7-th
International Conference on Intelligent Data Engineering and Automated
Learning (
**IDEAL06**). Sept 2006, Burgos, Spain.

**2005**

- M Girolami
and A Kaban. Sequential Activity
Profiling: Latent Dirichlet Allocation of Markov
Chains.
**Data Mining and Knowledge Discovery**. 10, pp. 175--196, 2005. code - I Nabney,
Y Sun, P Tino and A Kaban: Semisupervised Learning of Hierarchical Latent Trait
Models for Data Visualization.
**IEEE Transactions on Knowledge and Data Engineering**. 17(3), 2005. - A Kaban, L Nolan and S Raychaudhury. Finding Young
Stellar Populations in Elliptical Galaxies from Independent Components of
Optical Spectra. Proc. SIAM International Conference on Data Mining (
**SDM05**), 21--24 April 2005, Newport Beach California, USA, pp. 183--194. - M Harva and A Kaban, A Variational
Bayesian Method for Rectified Factor Analysis. Proc. IEEE
International Joint Conference on Neural Networks (
**IJCNN05**), Montreal, pp. 185-190.

- A Kaban,
A Scalable Generative Topographic Mapping for Sparse
Data Sequences. Proc. IEEE International Conference on Information
Technology: Coding and Computing (
**ITCC05**), Track: Data Coding and Compression, April 4--6, 2005, Las Vegas, NV, USA, pp.51--56. slides

- X Wang and A Kaban,
Finding
Uninformative Features in Binary Data, Proc. of the Sixth
International Conference on Intelligent Data Engineering and Automated
Learning (
**IDEAL05**), 6-8 July, Queensland, Australia, pp. 40-47. (c) Springer

**2004**

- M Girolami
and A Kaban. Simplicial
Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles.
Advances in Neural Information Processing Systems 16 (
**NIPS**), eds Sebastian Thrun, Lawrence Saul and Bernard Scholkopf, The MIT Press 2004, pp. 9-16. - P Tino,
A Kaban and Y Sun. A Generative
Probabilistic Approach to Visualising Sets of Symbolic Sequences.
*Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - (*, August 22-25, Seattle, Washington, USA, (eds.) R. Kohavi, J. Gehrke, W. DuMouchel, J. Ghosh. pp. 701-706, ACM Press, 2004.**KDD04**) - A Kaban,
E Bingham and T Hirsimaki. Learning to Read
Between the Lines: The Aspect Bernoulli Model, Proc. of the SIAM International
Conference on Data Mining (
**SDM04**), April 22-24 2004, Florida, USA, (eds.) Michael W. Berry, Umeshwar Dayal, Chandrika Kamath and David Skillicorn, pp.462-466. code - A Kaban
and X Wang. Context-Based
Identification of User Communities from Internet Chat, Proc. IEEE
International Joint Conference on Neural Networks (
**IJCNN04**), special session On Machine Learning for Text Mining (invited paper). July, Budapest, Hungary, pp 3287--3292.

**2003**

- E Bingham, A
Kaban and M Girolami. Topic
identification in dynamical text by complexity pursuit,
**Neural Processing Letters**17: 1-15, 2003, pp. 69-83. Code: cp.m, test_cp.m

- Kaban,
A and Girolami, M. Fast
Extraction of Semantic Features from a Latent Semantic Indexed Corpus.
**Neural Processing Letters**, vol. 15, issue 1, 2002, pp. 31-43. - M Girolami
and A Kaban. On an
equivalence between PLSI and LDA, 26-th Annual International ACM
Conference on Research and Development in Information Retrieval (
**SIGIR03**), July 28-August 1, 2003, Toronto, Canada, pp 433-434.

**2002**

- Kaban,
A. and Girolami, M: A Dynamic Probabilistic
Model to Visualise Topic Evolution in Text Streams,
**Journal of Intelligent Information Systems**, special issue on Automated Text Categorization, 18:2/3, 107-125, 2002.

- Kaban,
A, Tino, P. and Girolami,
M. A General Framework for a
Principled Hierarchical Visualisation of Multivariate Data, the
Third International Conference on Intelligent Data Engineering and
Automated Learning (
**IDEAL02**), pp. 17-23, Lecture Notes in Computer Science, Springer-Verlag, 2002.

**2001**

- Kaban,
A and Girolami, M. A
Combined Latent Class and Trait Model for the Analysis and Visualisation
of Discrete Data
**IEEE Transactions on Pattern Analysis and Machine Intelligence**23(8), pp. 859-872, 2001. code

- Lorincz,
A, Szatmary, B. and Kaban,
A. Sign-changing
filters similar to cells in primary visual cortex emerge by independent
component analysis of temporally convolved natural image sequences,
**Neurocomputing**38-40, 2001, pp. 1437-1442. - Bingham, E, Kaban,
A. and Girolami, M. Finding topics in
dynamical text: application to chat line discussions.
**WWW10**, May 1--5, 2001, Hong Kong, Poster Proceedings pp. 198--199.

**2000**

- Kaban,
A. and Girolami, M. Initialised
and Guided EM-Clustering of Sparse Binary Data with Applications to Text
Based Documents. Proc. of the 15'th
International Conference on Pattern Recognition
**ICPR00**. Barcelona, Spain, September, 2000, IEEE Computer Press, vol.2, pp. 748--751. - Kaban,
A and Girolami, M. Clustering of
Text Documents by Skewness Maximisation. Proc. of the 2'nd
International Workshop on Independent Component Analysis and Blind Source
Separation,
**ICA00**, June 2000, Helsinki, pp. 435-440. - Kaban,
A. and Girolami, M. Initialised and Guided
EM-Clustering of Binary Coded Text Based Documents Proc.
**EIS00**, Paisley, July 2000. - Girolami,
M, Vinokourov, A and Kaban,
A. The Organisation and Visualisation of Document Corpora:
A Probabilistic Approach. Invited paper. Proc. of the 11?th International Conference and Workshop on Database
and Expert Systems Applications
**DEXA00**. - Andras,
P, Kaban, A, Bossanyi,
R. Neural
Network Approximation of Fuzzy Logic Operators. Annales
Universitatis Occidentalis
Timisiensis, Series Philosophia,
Vol. XII, 2000, ISSN 1224-9688, pp. 147-152.

**Technical
Reports **

- R.J Durrant and A Kaban. A comparison
of the moments of a quadratic form involving orthonormalised
and normalised random projections. Technical Report CSR-11-04, School
of Computer Science, The University of Birmingham.

**Other - Abstracts and workshop contributions**

- From
Random Projections to Learning Theory to Algorithms and Back.
Colloquium at Heidelberg Institute for Theoretical Studies, 18 September
2017.
- On
the Curses and Blessings of High Dimensionality in Data Mining.
Colloquium at Hong Kong Baptist University, 19 November 2015.
- Invited talk at the Turing Institute scoping
workshop on Statistical and Computational Challenges in Large-Scale
Data Analysis. Centre for Mathematical Sciences, Cambridge, 28-30
September 2015.
- Learning from little data, Computational
Astrostatistics, 27-30 January 2014, Lorentz
Center, Leiden.
- Learning with Random
Projections. Seminar talk at UCL, Centre for Computational Statistics
and Machine Learning (CSML), 14 November 2014.
- Some sharp
bounds on compressive learning. Seminar talk at University of
Cambridge, Centre for Mathematical Sciences, 17 January 2014.
- Tutorial with Bob Durrant on Random
Projections for Machine Learning and Data Mining: Theory and Practice
at ECML-PKDD’2012 at Bristol, Sept. 2012. Slides; Handouts
- Learning and Regularisation
with Random Projections. Simple’12, MPI Dresden, Germany, Sept. 2012.
- Distance
concentration and detection of meaningless distances. Schloss Dagstuhl seminar on
Information Visualisation, Visual Data Mining
and Machine Learning, February 2012.
- A Kaban and R.J Durrant.
Subspace-adaptiveness of Compressive FLD. ERCIM 2011 – invited talk for
“High-dimensional statistics, sparsity and applications” by Gerard Biau and Pierre Alquier. 17-19 December 2011, Senate House, University of London, UK.
- R.J Durrant and A
Kaban. Finite sample
effects in compressed Fisher Discriminant Analysis. AISTATS 2010
‘breaking news’ abstract. 13-15 May 2010.
- A statistical test to detect distance concentration from data sets.
AISTATS 2010 ‘breaking news’ abstract. 13-15 May 2010.
- Lq-regularised sparse classifiers: A PAC-Bayes
analysis. Workshop on Sparsity in Machine learning and Statistics,
Cumberland Lodge, 1-3 April, 2009. slides poster
- R.J Durrant and A
Kaban. Sparsity
in the context of learning from high dimensional data. ICArn Workshop, Liverpool, September 2008.
- A Kaban and R.J Durrant.
A norm-concentration
argument for non-convex regularization. ICML/UAI/COLT
Workshop on Sparse Optimization and Variable Selection, 9
^{ }July, 2008, Helsinki, Finland. slides - The concentration of the
L2 distance in latent variable models & some consequences.
Workshop on Future Directions in High-Dimensional Data Analysis, Newton
Institute of Mathematical Sciences, Cambridge, UK, 23-27 June 2008.
- Two applications
of variational inference: Astrophysics and gene
expression analysis. Research Kitchen on Approximate Inference, Bath,
8-9 May 2007.
- L Nolan and S Raychaudhury. Factorisation of Positive Valued Functions for Analysing Galaxy Spectra. ICA
Research Network Workshop (ICArn06),
September 2006, Liverpool. slides
- ICA-rn launch day poster. 15 April, 2005.