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SCH Seminar List

for period 7 January to 27 June 2008

Monday 07 January
10:00-11:00 Donoho, D (Stanford)
  Breakdown point of model selection when the number of variables exceeds the number of observations Sem 1
11:30-12:30 van de Geer, S (Zurich)
  The deterministic lasso Sem 1
14:00-15:00 Wegman, E (George Mason)
  Methods for visualizing high dimensional data Sem 1
15:30-16:30 Young, A (Imperial)
  Bootstrap and parametric inference: successes and challenges Sem 1
Tuesday 08 January
09:00-10:00 Wainwright, M (Berkeley)
  Practical and information-theoretic limitations in high-dimensional inference Sem 1
10:00-11:00 Samworth, R (Cambridge)
  Some thoughts on nonparametric classification: nearest neighbours, bagging and max likelihood estimation of shape-constrained densities Sem 1
11:30-12:30 Cook, RD (Minnesota)
  Model-based sufficient dimension reduction for regression Sem 1
14:00-15:00 Jordan, M (Berkeley)
  Kernel-based contrast functions for sufficient dimension reduction Sem 1
15:30-16:30 Fan, J (Princeton)
  Challenge of dimensionality in model selection and classification Sem 1
16:30-17:30 Bickel, P (Berkeley)
  Regularised estimation of high dimensional covariance matrices Sem 1
Wednesday 09 January
09:00-10:00 Murtagh, F (Royal Holloway)
  The ultrametric topology perspective on analysis of massive, very high dimensional data stores Sem 1
10:00-11:00 Duembgen, L (Bern)
  P-values for computer-intensive classifiers Sem 1
11:30-12:30 Stuetzle, W (Washington)
  Nonparametric cluster analysis: estimating the cluster tree of a density Sem 1
14:00-15:00 West, M (Duke)
  Sparsity modelling in large-scale dynamic models for portfolio analysis Sem 1
15:30-16:30 Candes, E (Caltech)
  Computationally tractable statistical estimation when there are more variables than observations Sem 1
16:30-17:30 Nadler, B (Weizmann)
  Learning in high dimensions, noise, sparsity and treelets Sem 1
Thursday 10 January
09:00-10:00 van der Vaart, AW (Amsterdam)
  Estimating a response parameter in missing data models with high-dimensional covariates Sem 1
10:00-11:00 Wellner, J (Seattle)
  Persistence: alternative proofs of some results of Greenshtein and Ritov Sem 1
11:30-12:30 Cook, D (Iowa State)
  Looking at models in high-dimensional data spaces Sem 1
14:00-15:00 Tanner, J (Edinburgh)
  The surprising structure of Gaussian point clouds and its implications for signal processing Sem 1
15:30-16:30 Lee, A (Carnegie-Mellon)
  Finding low-dimensional structure in high-dimensional data Sem 1
16:30-17:30 Niyogi, P (Chicago)
  A geometric perspective on learning theory and algorithms Sem 1
Friday 11 January
09:00-10:00 Buehlmann, P (Zurich)
  High-dimensional variable selection and graphs: sparsity, faithfulness and stability Sem 1
10:00-11:00 Mammen, E (Mannheim)
  Time series regression with semiparametric factor dynamics Sem 1
11:30-12:30 Yu, B Y (Berkeley)
  Using side information for prediction Sem 1
14:00-15:00 Hoyle, D (Manchester)
  A physicist's approach to high-dimensional inference Sem 1
15:30-16:30 Clarke, B (British Columbia)
  Models, model lists, model spaces and predictive optimality Sem 1
Monday 14 January
11:00-12:00 Jin, J (Purdue)
  Innovative higher criticism for detecting sparse signals in correlated noise Sem 2
Wednesday 16 January
11:00-12:00 Nan, B (Michigan)
  Hierarchically penalised Cox regression for censored data with grouped variables and its oracle property Sem 2
Friday 18 January
11:00-12:00 Pontil, M (UCL)
  A spectral regularisation framework for multi-task structure learning Sem 2
Tuesday 22 January
11:00-12:00 Banks, D (Duke )
  Statistical issues amd metabolomics Sem 2
Thursday 24 January
11:00-12:00 Butucea, C (Paris VI)
  Excess mass estimation Sem 2
15:00-17:00 Cook, D (Minnesota)
  An informal introduction to sufficient dimension reduction Sem 2
Friday 25 January
11:00-12:00 Clarke, J (Miami School of Medicine)
  An ensemble approach to improved prediction from multitype data Sem 2
Tuesday 29 January
11:00-12:30 Zhou, H (Yale)
  Model selection and sharp asymptotic minimaxity Sem 2
Thursday 31 January
09:00-10:00 Kirk, E (Cantab Capital Partners)
  High frequency micro structure in futures markets Sem 1
10:00-10:45 Rogers, C (Cambridge )
  Choosing a portfolio of many assets Sem 1
11:00-12:00 Clarkson, P (BNP Paribas)
  A database of foreign exchange deals Sem 1
Tuesday 05 February
11:00-12:00 Belabbas, A (Harvard)
  Approximation methods in statistical learning theory Sem 2
Thursday 07 February
11:00-12:00 Lawrence, N (Manchester)
  Modelling human motion with Gaussian processes Sem 2
Friday 08 February
11:00-12:00 Caponnetto, A (City University of Hong Kong)
  Properties of regularisation operators in learning theory Sem 2
Tuesday 12 February
11:00-12:00 Kent, J (Leeds)
  Procrustes methods for projective shape Sem 2
Wednesday 13 February
14:00-15:00 Said, YH (George Mason)
  Text mining and high dimensional statistical analysis Sem 2
Friday 15 February
11:00-12:00 Titterington, M (Glasgow)
  An introduction to variational methods for incomplete-data problems Sem 2
Monday 18 February
15:00-15:30 Heller, K (UCL)
  Bayesian hierarchical clustering Sem 1
15:30-16:00 Ghahramani, Z (Cambridge)
  Bayesian nonparametric latent feature models Sem 1
16:00-16:30 Silva, R (Cambridge)
  New models for relational classification Sem 1
16:30-17:00 Snelson, E (Microsoft Cambridge)
  Gaussian process methods for large and high-dimensional data sets Sem 1
Tuesday 19 February
11:00-12:00 Seeger, M (Max-Planck)
  Expectation Propagation -- Experimental Design for the Sparse Linear M Sem 2
Thursday 21 February
11:00-12:00 Cristianini, M (Bristol)
  Some statistical problems from artificial intelligence Sem 2
Friday 22 February
11:00-12:00 Lafferty, J (Carnegie Mellon)
  Functional sparsity Sem 2
Tuesday 26 February
11:00-12:00 Storkey, A (Edinburgh)
  Learning latent activites in large scale dynamical problems Sem 2
Thursday 28 February
11:00-12:00 George, E (Pennsylvania)
  Pre-modelling via BART Sem 1
Friday 29 February
11:00-12:00 Hennig, C (UCL)
  Some thoughts about the design of dissimilarity measures Sem 2
Tuesday 04 March
11:00-12:00 Shi, JQ (Newcastle)
  Gaussian process functional regression model for curve prediction and clustering Sem 2
Thursday 06 March
11:00-12:00 Olhede, S (UCL)
  Non-parametric estimation of HARDI diffusion weighted magnetic resonance imaging data Sem 1
Tuesday 11 March
11:00-12:00 Kovac, A (Bristol)
  Total variation and curves Sem 2
14:00-15:00 House, L (Durham)
  Proteomics data analysis Sem 2
Wednesday 12 March
16:15-17:00 Titterington, M (Glasgow)
  Some issues raised by high dimension in Statistics - a partial overview of the SCH Programme Sem 1
Thursday 13 March
11:00-12:00 Dryden, I (Nottingham)
  Multilevel modelling of proteomic mass-spectrometry data Sem 2
Monday 17 March
17:00-18:00 Donoho, D (Stanford University)
  More unknowns than equations? Not a problem! Use Sparsity! Sem 1
Tuesday 18 March
11:00-12:00 Hancock, E (York)
  Analysis of graphs using diffusion processes and random walks (a random walk through spectral graph theory) Sem 1
Wednesday 19 March
11:00-12:00 Whittaker, J (Lancaster)
  Bootstrapping divergence weighted independence graphs Sem 2
Wednesday 26 March
11:00-12:00 Teh, WH (UCL)
  Improvements to variational Bayesian inference Sem 2
Thursday 27 March
11:00-12:00 Kleijn, B (Free University)
  The semiparametric Bernstein-Von Mises theorem Sem 2
Monday 31 March
10:00-11:00 Birney, E (EBI)
  The evolution of promoter sequence Sem 1
11:30-12:30 Pachter, L (UC, Berkeley)
  Functional genomics and the forest of life Sem 1
14:00-15:00 Brunak, S (Denmark)
  Understanding interactomes by data integration Sem 1
15:30-16:30 McLachlan, GJ (Queensland)
  On mixture models in high-dimensional testing for the detection of differential gene expression Sem 1
16:30-17:30 Margulies, E (National Human Genome Research)
  Statistical challenges in using comparative genomics for the identification of functional sequences Sem 1
Tuesday 01 April
09:00-10:00 Hurles, M (Sanger)
  Structural variation in the human genome Sem 1
10:00-11:00 Benjamini, Y (Tel Aviv)
  Selective inference in complex research problems Sem 1
11:30-12:30 Durbin, R (Sanger)
  Efficient use of population genome sequencing data Sem 1
14:00-15:00 West, M (Duke)
  Sparsity modelling in gene expression pathway studies Sem 1
15:30-16:30 Dermitzakis, M (Sanger)
  Population genomics of human gene expression Sem 1
Wednesday 02 April
09:00-10:00 Enright, A (Sanger)
  Computational analysis and prediction of microRNA binding sites Sem 1
10:00-11:00 Bühlmann, P (ETH Zürich)
  L1-regularisation, motif regression and ChIP-on-chip data analysis Sem 1
11:30-12:30 Huber, W (EBI)
  Extraction and classification of cellular and genetic phenotypes from automated microscopy data Sem 1
14:00-15:00 Beerenwinkel, N (ETH Zürich)
  Ultra-deep sequencing of mixed virus populations Sem 1
Thursday 03 April
09:00-10:00 Segal, E (Weizmann Institute)
  Cracking the regulatory code: predicting expression patterns from DNA sequence Sem 1
10:00-11:00 Bickel, PJ (UC Berkeley)
  Refined nonparametric methods for genomic inference Sem 1
11:30-12:30 Marcotte, EM (Texas at Austin)
  Steps toward directed identification of disease genes: predicting the consequences of genetic perturbations Sem 1
14:00-15:00 Crawford, G (Duke)
  High-resolution identification of active gene regulatory elements Sem 1
15:30-16:30 Bulyk, ML (Harvard Medical School)
  High-resolution binding specificity profiles of transcription factors and cis regulatory codes in DNA Sem 1
Friday 04 April
09:00-10:00 Bertone, P (EBI)
  Functional genomic approaches to stem cell biology Sem 1
10:00-11:00 McVean, G (Oxford)
  Approximate genealogical inference Sem 1
11:30-12:30 Luscombe, N (EBI)
  Genomic principles for feedback regulation of metabolism Sem 1
14:00-15:00 Huang, H (UC Berkeley)
  A bayesian probabilistic approach to transform public microarray repositories into disease diagnosis databases Sem 1
Tuesday 08 April
11:00-12:00 Rubin, D (Berkeley)
  Empirical efficiency maximisation: improved locally efficient covariate adjustment Sem 2
Thursday 10 April
11:00-12:00 Taylor, C (Leeds)
  Boosting kernel estimates Sem 2
Tuesday 15 April
11:00-12:00 Hurley, C (NUI Maynooth)
  Data visualisation via pairwise displays Sem 2
Thursday 17 April
11:00-12:00 Nadler, B (Weizmann Institute)
  Determining the number of factors in a linear mixture model from limited noisy data Sem 2
Monday 21 April
11:00-12:00 Shawe-Taylor, J (University College London)
  Spectra and generalisation Sem 2
Tuesday 22 April
11:00-12:00 Hjort, NL (Oslo)
  Empirical likelihood with a growing number of parameters Sem 2
Thursday 24 April
11:00-12:00 Robert, C (Paris-Dauphine)
  A Bayesian reassessment of nearest-neighbour classification Sem 2
Tuesday 29 April
11:00-12:00 Roweis, S (Toronto)
  Making the sky searchable: large scale astronomical pattern recognition Sem 1
Wednesday 30 April
11:00-12:00 Wellner, J (Washington)
  Testing for sparse normal means: is there a signal? Sem 2
14:00-15:00 Cook, D (Iowa State)
  Looking at data and models in high-dimensional spaces: (1) Tools and tips for making good plots Sem 1
Tuesday 06 May
11:00-12:00 Bunea, F (Florida State)
  Non-asymptotic variable identification via the Lasso and the elastic net Sem 2
Wednesday 07 May
14:00-15:00 Cook, D (Iowa State)
  Looking at data and models in high-dimensional spaces: (2) How, when and why to use interactive and dynamic graphics Sem 1
Thursday 08 May
11:00-12:00 Wegkamp, M (Florida State)
  Lasso type classifiers with a reject option Sem 2
Tuesday 13 May
11:00-12:00 Hero, A (Michigan)
  Entropic graphs for high-dimensional data analysis Sem 1
Wednesday 14 May
11:00-12:00 Marron, S (North Carolina)
  Object oriented data analysis Sem 1
14:00-15:00 Cook, D (Iowa State)
  Looking at data and models in high-dimensional spaces: (3) Determining significance of structure Sem 1
Thursday 15 May
11:00-12:00 Murray, I (Toronto)
  Assessing high-dimensional latent variable models Sem 1
Monday 19 May
16:40-17:10 Hand, D (Imperial)
  Frontiers in applications of data mining Sem 1
17:10-17:40 Bishop, C (Microsoft Research, Cambridge)
  Frontiers in applications of machine learning Sem 1
17:40-18:30 Wallace, D
  Panel discussion Sem 1
Tuesday 20 May
11:00-12:00 Girolami, M (Glasgow)
  On stratified path sampling of the thermodynamic integral: computing Bayes factors for nonlinear ODE models of biochemical pathways Sem 1
Wednesday 21 May
11:00-12:00 Maurer, A
  Slow subspace learning Sem 1
Thursday 22 May
11:00-12:00 Rattray, M (Manchester)
  Latent variable models of transcriptional regulation Sem 1
Tuesday 27 May
15:00-15:30 Gramacy, B (Cambridge)
  On estimating covariances between many assets with histories of highly variable length Sem 1
15:30-16:00 Cule, M (Cambridge)
  Nonparametric estimation of a log-concave density Sem 1
16:00-16:30 Silva, R (Cambridge)
  Factorial mixture of Gaussians and the marginal independence model Sem 1
16:30-17:00 Spiegelhalter, D (Cambridge)
  Understanding uncertainty Sem 1
Wednesday 28 May
11:00-12:00 Mueller, HG (California)
  Functional regression and additive models Sem 1
Thursday 29 May
11:00-12:00 Cristianini, N (Bristol)
  Learning curves: lessons from statistical machine translation Sem 2
Tuesday 03 June
11:00-12:00 Wolfe, PJ (Harvard)
  On the approximation of quadratic forms and sparse matrix products Sem 1
Thursday 05 June
11:00-12:00 Guillas, S (University College London)
  Approximation of functional spatial regression models using bivariate splines Sem 1
Friday 06 June
11:00-12:00 Nychka, DW (National Centre for Atmospheric Research)
  Challenges of regional climate modelling and validation Sem 1
Tuesday 10 June
11:00-12:00 Koltchinskii, V (Georgia Institute of Technology)
  Sparse recovery in convex hulls based on entropy penalisation Sem 1
Thursday 12 June
11:00-12:00 Rohde, A (Weierstrass)
  Confidence sets for the optimal approximating model - bridging a gap between adaptive point estimation and confidence regions Sem 2
Tuesday 17 June
11:00-12:00 van Houwelingen, JC (Leiden University Medical Centre)
  Global testing of association and/or predictability in regression problems with p>>n predictors Sem 1
Wednesday 18 June
14:00-15:00 Godsill, S (Cambridge)
  Sequential inference for dynamically evolving groups of objects Sem 1
15:30-16:10 Cai, Y (Plymouth)
  A Bayesian method for non-Gaussian autoregressive quantile function time series models Sem 1
16:10-16:50 Luo, X (Oxford)
  State estimation in high dimensional systems: the method of the ensemble unscented Kalman filter Sem 1
16:50-17:30 Whiteley, N (Cambridge)
  A modern perspective on auxiliary particle filters Sem 1
Thursday 19 June
09:00-09:40 Reisen, VA (Universidade Federal do Espirito Santo)
  Estimating multiple fractional seasonal long-memory parameter Sem 1
09:40-10:20 Shen, Y (Aston)
  Variational Markov Chain Monte Carlo for inference in partially observed stochastic dynamic systems Sem 1
10:20-11:00 Turner, R (University College London)
  Two problems with variational expectation maximisation for time-series models Sem 1
11:30-12:30 Opper, M (Technische Universität Berlin)
  Approximate Inference for Continuous Time Markov Processes Sem 1
14:00-15:00 Singh, S (Cambridge)
  Recent applications of spatial point processes to multiple-object tracking Sem 1
15:20-16:00 Kondor, R (University College London)
  Multi-object tracking with representations of the symmetric group Sem 1
16:00-17:00 Williams, C (Edinburgh)
  Factorial switching linear dynamical systems for physiological condition monitoring Sem 1
17:00-17:30 Roberts, S (Oxford)
  Bayesian Gaussian process models for multi-sensor time-series prediction Sem 1
Friday 20 June
09:00-09:40 McLachlan, GJ (Queensland)
  Clustering of time course gene-expression data via mixture regression models Sem 1
09:40-10:20 Titsias, MK (Manchester)
  Markov chain Monte Carlo algorithms for Gaussian processes Sem 1
10:20-11:00 Aston, J (Warwick)
  Is that really the pattern we're looking for? Bridging the gap between statistical uncertainty and dynamic programming algorithms Sem 1
11:30-12:30 Moulines, E (CNRS)
  Adaptive Monte Carlo Markov Chains Sem 1
14:00-15:00 Papaspiliopoulos, O (Universitat Pompeu Fabra )
  A methodological framework for Monte Carlo estimation of continuous-time processes Sem 1
15:30-16:10 Sykulski, A, Olhede, SC (Imperial/UCL)
  High frequency variability and microstructure bias Sem 1
16:10-17:10 Ghahramani, Z (Cambridge)
  Nonparametric Bayesian times series models: infinite HMMs and beyond Sem 1
Monday 23 June
10:00-11:00 Hall, PG (Melbourne)
  Variable selection in very high dimensional regression and classification Sem 1
11:30-12:30 Gather, U (Dortmund)
  Dimension reduction Sem 1
14:00-15:00 Meinshausen, N (UC Berkeley)
  Stability - based regularisation Sem 1
15:30-16:30 Cai, T (Pennsylvania )
  Large-scale multiple testing: finding needles in a haystack Sem 1
Tuesday 24 June
09:00-10:00 van Houwelingen, J (Leiden University Medical Center)
  Fitting survival models with P>>n predictors: beyond proportional hazards Sem 1
10:00-11:00 Yuan, MY (Georgia Institute of Technology)
  Model selection and estimation with multiple reproducing Karnel Hilbert spaces Sem 1
11:30-12:30 Tsybakov, A (CREST and Paris 6)
  Sparsity oracle inequalities Sem 1
14:00-14:20 Airoldi, E (Princeton)
  The exchangeable graph model for statistical network analysis Sem 1
14:20-14:40 West, M (Duke)
  Data, models, inference and computation for dynamic cellular networks in systems biology Sem 1
14:40-15:00 Xing, E (Carnegie Mellon)
  Statistical network analysis and inference: methods and applications Sem 1
15:30-16:30 Wit, E (Lancaster)
  High dimensional inference in bioinformatics and genomics Sem 1
16:30-17:30 Li, KC (Adademia Sinica)
  Liquid association for large scale gene expression and network studies Sem 1
Wednesday 25 June
09:00-10:00 Tibshirani, R (Stanford)
  The Lasso: some novel algorithms and applications Sem 1
10:00-11:00 Shawe-Taylor, JS (UC London)
  Sparsity in machine Learning: approaches and analyses Sem 1
11:30-12:30 Owen, A (Stanford)
  Transposably invariant sample reuse: the pigeonhole bootstrap and blockwise cross-validation Sem 1
Thursday 26 June
09:00-10:00 Wang, J-L (UC Davis)
  Covariate adjusted functional principal component analysis for longitudinal data Sem 1
10:00-11:00 Koltchinskii, V (Georgia Institute of Technology)
  Penalized empirical risk minimization and sparse recovery problems Sem 1
11:30-12:30 Wolfe, P (Harvard)
  The Nystrom extension and spectral methods in learning: low-rank approximation of quadratic forms and products Sem 1
14:00-14:20 Pan, G (Eurandom)
  Limiting theorems for large dimensional sample means, sample covariance matrices and Hotelling's T2 statistics Sem 1
14:20-14:40 Shi, JQ (Newcastle)
  Generalised gaussian process functional regression model Sem 1
14:40-15:00 Wang, Y (NSF)
  Estimation of large volatility matrix for high-frequency financial data Sem 1
15:30-16:30 Barber, D (UC London)
  Graph decomposition for community identification and covariance constraints Sem 1
16:30-17:30 Levina, E (Michigan)
  Permutation-invariant covariance regularisation in high dimensions Sem 1
Friday 27 June
09:00-09:20 Helland, IS (Oslo)
  Optimal prediction from relevant components Sem 1
09:20-09:40 Koch, I (New South Wales)
  Dimension selection with independent component analysis and its application to prediction Sem 1
09:40-10:00 Li, L (North Carolina)
  Model free variable selection via sufficient dimension reduction Sem 1
10:00-11:00 Robins, J (Harvard)
  Estimation of nonlinear functionals: recent results and open problems Sem 1
11:30-12:30 Seeger, MW (MPI for Biological Cybernetics)
  Applications of approximate inference and experimental design for sparse (generalised) linear models Sem 1
14:00-15:00 Rice, J (UC Berkeley)
  Statistics in astronomy: the Taiwanese-American occultation survey Sem 1
Other Seminars
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