While I mentioned that SPARS13 was starting today, it so happens that ROKS 2013 is also happening in old Europe starting today as well. The list of extended abstracts is listed below. In about two weeks, SAHD 2013, Duke's Workshop on Sensing and Analysis of High-Dimensional Data will also take place, this time in the U.S. The program is listed below with a link to the abstracts. I note that one of the many interesting speakers include none other than Tomaso Poggio, one of the author of the feedforward model for the visual cortex and mentioned here back in 2007 ( Compressed Sensing in the Primary Visual Cortex ?) and in Sunday's Morning Insight: Faster Than a Blink of an Eye. Lots of good things are happening this summer.
From the ROKS2013 program:
Monday July 8
| 12:00-13:00 | Registration and welcome coffee in Salons Arenberg castle |
| 13:00-13:10 | Welcome by Johan Suykens |
| 13:10-14:00 | Deep-er Kernels John Shawe-Taylor (University College London) [abstract] |
| 14:00-14:50 | Connections between the Lasso and Support Vector Machines Martin Jaggi (Ecole Polytechnique Paris) [abstract] |
| 14:50-15:10 | Coffee break |
| 15:10-16:40 | Oral session 1 (3 x 30 min): Feature selection and sparsity |
| 16:40-17:30 | Kernel Mean Embeddings applied to Fourier Optics Bernhard Schoelkopf (Max Planck Institute Tuebingen) [abstract] |
| 17:30-19:00 | Reception in Salons Arenberg Castle |
Tuesday July 9
| 09:00-09:50 | Large-scale Convex Optimization for Machine Learning Francis Bach (INRIA) [abstract] |
| 09:50-10:40 | Domain-Specific Languages for Large-Scale Convex Optimization Stephen Boyd (Stanford University) [abstract] |
| 10:40-11:00 | Coffee break |
| 11:00-12:00 | Oral session 2 (2 x 30 min): Optimization algorithms |
| 12:00-12:20 | Spotlight presentations Poster session 1 (2 min/poster) |
| 12:20-14:30 | Group picture, Lunch in De Moete, Poster session 1 in Rooms S |
| 14:30-15:20 | Dynamic L1 Reconstruction Justin Romberg (Georgia Tech) [abstract] |
| 15:20-16:10 | Multi-task Learning Massimiliano Pontil (University College London) [abstract] |
| 16:10-16:30 | Coffee break |
| 16:30-18:30 | Oral session 3 (4 x 30 min): Kernel methods and support vector machines |
| 19:00 | Dinner in Faculty Club |
Wednesday July 10
| 09:00-09:50 | Subgradient methods for huge-scale optimization problems Yurii Nesterov (Catholic University of Louvain) [abstract] |
| 09:50-10:40 | Living on the edge: A geometric theory of phase transitions in convex optimization Joel Tropp (California Institute of Technology) [abstract] |
| 10:40-11:00 | Coffee break |
| 11:00-12:30 | Oral session 4 (3 x 30 min): Structured low-rank approximation |
| 12:30-12:50 | Spotlight presentations Poster session 2 (2 min/poster) |
| 12:50-14:30 | Lunch in De Moete, Poster session 2 in Rooms S |
| 14:30-15:20 | Minimum error entropy principle for learning Ding-Xuan Zhou (City University of Hong Kong) [abstract] |
| 15:20-16:10 | Learning from Weakly Labeled Data James Kwok (Hong Kong University of Science and Technology) [abstract] |
| 16:10-16:30 | Coffee break |
| 16:30-18:00 | Oral session 5 (3 x 30 min): Robustness |
| 18:00 | Closing |
Oral session 1: Feature selection and sparsity
(July 8, 15:10-16:40)
- The graph-guided group lasso for genome-wide association studies
Zi Wang and Giovanni Montana
Mathematics Department, Imperial College London
[abstract] - Feature Selection via Detecting Ineffective Features
Kris De Brabanter (1) and Laszlo Gyorfi (2)
(1) KU Leuven ESAT-SCD
(2) Dep. Comp. Sc. & Inf. Theory, Budapest Univ. of Techn. and Econ.
[abstract] - Sparse network-based models for patient classification using fMRI
Maria J. Rosa, Liana Portugal, John Shawe-Taylor and Janaina Mourao-Miranda
Computer Science Department, University College London
[abstract]
Oral session 2: Optimization algorithms
(July 9, 11:00-12:00)
- Incremental Forward Stagewise Regression: Computational Complexity and Connections to LASSO
Robert Freund (1), Paul Grigas (2) and Rahul Mazumder (2)
(1) MIT Sloan School of Management
(2) MIT Operations Research Center
[abstract] - Fixed-Size Pegasos for Large Scale Pinball Loss SVM
Vilen Jumutc, Xiaolin Huang and Johan A.K. Suykens
KU Leuven ESAT-SCD
[abstract]
Oral session 3: Kernel methods and support vector machines
(July 9, 16:30-18:30)
- Output Kernel Learning Methods
Francesco Dinuzzo (1), Cheng Soon Ong (2) and Kenji Fukumizu (3)
(1) MPI for Intelligent Systems Tuebingen
(2) NICTA, Melbourne
(3) Institute of Statistical Mathematics, Tokyo
[abstract] - Deep Support Vector Machines for Regression Problems
M.A. Wiering, M. Schutten, A. Millea, A. Meijster and L.R.B. Schomaker
Institute of Artif. Intell. and Cognitive Eng., Univ. of Groningen
[abstract] - Subspace Learning and Empirical Operator Estimation
Alessandro Rudi (1), Guille D. Canas (2) and Lorenzo Rosasco (3)
(1) Istituto Italiano di Tecnologia
(2) MIT-IIT
(3) Universita di Genova
[abstract] - Kernel based identification of systems with multiple outputs using nuclear norm regularization
Tillmann Falck, Bart De Moor and Johan A.K. Suykens
KU Leuven, ESAT-SCD and iMinds Future Health Department
[abstract]
Oral session 4: Structured low-rank approximation
(July 10, 11:00-12:30)
- First-order methods for low-rank matrix factorization applied to informed source separation
Augustin Lefevre (1) and Francois Glineur (1,2)
(1) ICTEAM Institute and (2) CORE Institute, Universite catholique de Louvain
[abstract] - Structured low-rank approximation as optimization on a Grassmann manifold
Konstantin Usevich and Ivan Markovsky
Dep. ELEC, Vrije Universiteit Brussel
[abstract] - Scalable Structured Low Rank Matrix Optimization Problems
Marco Signoretto (1), Volkan Cevher (2) and Johan A.K. Suykens (1)
(1) KU Leuven, ESAT-SCD
(2) LIONS, EPFL Lausanne
[abstract]
Oral session 5: Robustness
(July 10, 16:30-18:00)
- Learning with Marginalized Corrupted Features
Laurens van der Maaten (1), Minmin Chen (2), Stephen Tyree (2) and Kilian Weinberger (2)
(1) Delft University of Technology
(2) Washington University in St. Louis
[abstract] - Robust regularized M-estimators of regression parameters and covariance matrix
Esa Ollila, Hyon-Jung Kim and Visa Koivunen
Department of Signal Processing and Acoustics, Aalto University
[abstract] - Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization
Nicolas Gillis (1) and Robert Luce (2)
(1) ICTEAM Institute, Univ. catholique de Louvain
(2) Technische Univ. Berlin
[abstract]
Poster session 1
(July 9, 13:15-14:30)
- Data-Driven and Problem-Oriented Multiple-Kernel Learning
Valeriya Naumova and Sergei V. Pereverzyev
Affiliations: Johann Radon Institute for Computational and Applied Mathematics (RICAM) Austrian Academy of Sciences
[abstract] - Support Vector Machine with spatial regularization for pixel classification
Remi Flamary (1) and Alain Rakotomamonjy (2)
(1) Lagrange Lab., CNRS, Universite de Nice Sophia-Antipolis
(2) LITIS Lab., Universite de Rouen
[abstract] - Regularized structured low-rank approximation
Mariya Ishteva and Konstantin Usevich and Ivan Markovsky
Dept. ELEC, Vrije Universiteit Brussel
[abstract] - A Heuristic Approach to Model Selection for Online Support Vector Machines
Davide Anguita, Alessandro Ghio, Isah A. Lawal and Luca Oneto
DITEN, University of Genoa
[abstract] - Lasso and Adaptive Lasso with Convex Loss Functions
Wojciech Rejchel
Nicolaus Copernicus University, Torun, Poland
[abstract] - Conditional Gaussian Graphical Models for Multi-output Regression of Neuroimaging Data
Andre F Marquand (1), Maria Joao Rosa (2) and Orla Doyle (1)
(1) King`s College London
(2) University college London
[abstract] - High-dimensional convex optimization problems via optimal affine subgradient algorithms
Masoud Ahookhosh and Arnold Neumaier
Faculty of Mathematics, University of Vienna
[abstract] - Joint Estimation of Modular Gaussian Graphical Models
Jose Sanchez and Rebecka Jornsten
Mathematical Sciences, Chalmers Univ, of Technology and University of Gothenburg
[abstract] - Learning Rates of l1-regularized Kernel Regression
Lei Shi, Xiaolin Huang and Johan A.K. Suykens
KU Leuven, ESAT-SCD
[abstract] - Reduced Fixed-Size LSSVM for Large Scale Data
Raghvendra Mall and Johan A.K. Suykens
KU Leuven, ESAT-SCD
[abstract]
Poster session 2
(July 10, 13:15-14:30)
- Pattern Recognition for Neuroimaging Toolbox
Jessica Schrouff (1), Maria J. Rosa (2), Jane Rondina (2), Andre Marquand (3), Carlton Chu (4), John Ashburner (5), Jonas Richiardi (6), Christophe Phillips (1) and Janaina Mourao-Miranda (2)
(1) Cyclotron Research Centre, University of Liege
(2) Computer Science Dep., University College London
(3) Institute of Psychology, King`s College, London
(4) NIMH, NIH, Bethesda
(5) Wellcome Trust Centre for Neuroimaging, University College London
(6) Stanford University
[abstract] - Stable LASSO for High-Dimensional Feature Selection through Proximal Optimization
Roman Zakharov and Pierre Dupont
ICTEAM Institute, Universite catholique de Louvain
[abstract] - Regularization in topology optimization
Atsushi Kawamoto, Tadayoshi Matsumori, Daisuke Murai and Tsuguo Kondoh
Toyota Central R&D Labs., Inc., Nagakute
[abstract] - Classification of MCI and AD patients combining PET data and psychological scores
Fermin Segovia, Christine Bastin, Eric Salmon and Christophe Phillips
Cyclotron Research Centre, University of Liege
[abstract] - Kernels design for Internet traffic classification
Emmanuel Herbert (1), Stephane Senecal (1) and Stephane Canu (2)
(1) Orange Labs, Issy-les-Moulineux
(2) LITIS/INSA, Rouen
[abstract] - Kernel Adaptive Filtering: Which Technique to Choose in Practice
Steven Van Vaerenbergh and Ignacio Santamaria
Department of Communications Engineering, University of Cantabria, Spain
[abstract] - Structured Machine Learning for Mapping Natural Language to Spatial ontologies
Parisa Kordjamshidi and Marie-Francine Moens
Dep. of Computer Science, Katholieke Universiteit Leuven
[abstract] - Windowing strategies for on-line multiple kernel regression
Manuel Herrera and Rajan Filomeno Coelho
BATir Dep., Universite libre de Bruxelles
[abstract] - Non-parallel semi-supervised classification
Siamak Mehrkanoon and Johan A.K. Suykens
KU Leuven, ESAT-SCD
[abstract] - Visualisation of neural networks for model reduction
Tamas Kenesei and Janos Abonyi
University of Pannonia, Department of Process Engineering
[abstract] - Convergence analysis of stochastic gradient descent on strongly convex objective functions
Cheng Tang and Claire Monteleoni
Dep. of Computer Science, The George Washington University
[abstract]
Tuesday, July 23
9-11am: Oral Session T1
- Ron DeVore - Recovering a high dimensional manifold from snapshots
- Raphy Coifman - Harmonic Analysis on Data arrays
- Thomas Strohmer - On Eigenvector Localization
- Sinan Gunturk - Quantization Alternatives for Frames and Compressive Systems
11-12:30: Whiteboard Session T2
- Anna Gilbert - Modal analysis with compressive measurements
- Martin Strauss - Sparse Recovery Against Bounded Adversaries
- Karl Rohe - The blessing of transitivity in sparse and stochastic networks
- Justin Romberg - Blind Deconvolution using Convex Programming
- Joshua Vogelstein - Two-Sample Testing for Graph-Valued Observations
- Radu Balan - Stability of Informational Complete Sets of Vectors
- Daniel Sussman - A limit theorem for scaled eigenvectors of random dot product graphs
- Quentin Berthet - Computational Lower Bounds for Sparse PCA
- Volkan Cevher - Group-Sparse Model Selection: Hardness and Relaxations
12:30-1:30: Box lunch (provided)
1:30-3:30: Oral Session T3
- Amit Singer - Global registration of multiple point clouds using semidefinite programming
- Rachel Ward - Sparse recovery without incoherence
- Andrea Bertozzi - Geometric methods in image processing, networks, and machine learning
3:30-5: Poster Session T4 (Last names starting P-Z)
- Reeves, Galen - Achieving Bayes MMSE Performance in the Sparse Signal + Gaussian White Noise Model when the Noise Level is Unknown
- Renna, Francesco - Compressive Sensing of Gaussian Mixtures
- Shi, Jianing - Video Compressive Sensing for Dynamic MRI
- Soni, Akshay and Swayambhoo Jain - Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach
- Sprechmann, Pablo - Sparse similarity-preserving hashing
- Tepper, Mariano - A Bi-clustering Formulation of Multiple Model Estimation
- Wang, Liming - Generalized Bregman Divergence and Gradient of Mutual Information: Unifying Poisson and Gaussian Channels
- Watanabe, Takanori - Machine Learning for Connectomics
- Yang, Jianbo and Yuan, Xin - Robust Principle Component Analysis based Real-Time Leave-Bag-Behind Detection
- Yang, Yi - Robust 1-bit compressive sensing using adaptive outlier pursuit
- Zhang, Yuqian - Towards Guaranteed Illumination Models for Non-Convex Objects
- Balzano, Laura - GROUSE local convergence and relationship to ISVD
- others TBD -- sign up during registration!
Wednesday, July 24
9-11am: Oral Session W1
- Robert Nowak - Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis
- Piotr Indyk
- Vladimir Koltchinskii - Low Rank Estimation of Smooth Kernels on Weighted Graphs
- Michael Gehm - Gigapixel!
11-12:30: Whiteboard Session W2
- Tony Jebara
- Stan Osher - The magic of l1 regularization.
- Ryan Adams - Towards Scalable Bayesian Inference with Parallelized Markov Chain Monte Carlo
- Deanna Needell - Iterative methods for super-resolution
- Maryam Fazel - Recovery and denoising of signals with simultaneous structure
- Garvesh Raskutti - Learning directed graphs based on sparsest permutations
- Hervé Rouault - Inference with sparse measurement matrices and localization microscopy
- Yue Lu - Adaptive Parameter Estimation with Finite Memory
12:30-1:30: Box lunch (provided)
1:30-4:00: Oral Session W3
- Karl Rohe - Preconditioning for sparse inference
- Joel Tropp - Living on the edge: A geometric theory of phase transitions in convex optimization
- Rene Vidal - See All by Looking at A Few: Sparse Modeling for Finding Data Exemplars
- Venkat Chandrasekaran - Computational and Statistical Tradeoffs via Convex Relaxation
4-5: Poster Session W4 (last names starting I-Q)
- Jiang, Xin - Minimax optimal rates for photon-limited compressed sensing with Poisson noise
- Keller, Yosi - Sensor localization by diffusion based regression
- Kim, Jinyoung - Subthalamic nucleus prediction using statistical shape relationships for deep brain stimulation surgery
- Li, Jiang - Adaptive Graph Construction Approach for Large-Scale Manifold Learning
- Lawlor, David - Regression in High Dimensions via Geometric Multiresolution Analysis
- Lyzinski, Vincent - Seeded Graph Graph Matching
- Ma, Yanting - Two-Part Reconstruction in Noisy Compressed Sensing
- Minsker, Stas - A new look at the sample mean: Robust estimation in Hilbert spaces
- Mu, Cun - Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
- Nokleby, Matt - Information-Theoretic Limits on the Classification of Gaussian Mixtures: Classification on the Grassmann Manifold
- Osting, Braxton - Enhanced statistical rankings via targeted data collection
- Pourkamali Anaraki, Farhad - Kernel Compressive Sensing
- Qiu, Qiang - Recognition and Clustering using Learned Low-rank Transformation
- others TBD -- sign up during registration!
Evening: Workshop banquet with dinner speaker Ingrid Daubechies (provided)
Thursday, July 25
9-11am: Oral Session H1
- Howard Bondell - Consistent high-dimensional Bayesian variable selection via penalized credible regions
- Qi, Alan - Bayesian approaches for correlated variable selection and online learning
- Natesh Pillai - Constructing shrinkage prior distributions in high dimensions
- Tomaso Poggio - The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work)
11-12:30: Poster Session H2 (last names starting A-H)
- Al-Aifari, Reema - Spectral and asymptotic analysis for the truncated Hilbert transform arising in limited data tomography
- Basu, Sumanta - High Dimensional Vector Autoregression
- Quentin Berthet - Computational Lower Bounds for Sparse PCA
- Boyd-Graber, Jordan - Online Latent Dirichlet Allocation with Infinite Vocabulary
- Bruna, Joan - Scattering Representations and Structured Convolutional Networks
- Cevher, Volkan - Composite self-concordant minimization
- Crosskey, Miles - Learning Dynamical systems: Applications to Molecular Dynamics
- Duarte, Marco - Spectral Compressive Sensing via Polar Interpolation
- Engelhardt, Barbara - A joint model of sparse and dense structure in a latent factor model
- Guan, Peng - Online Markov Decision Processes with Kullback-Leibler Control Cost
- Guhaniyogi, Rajarshi - Conditional density filtering for Bayesian streaming
- Harms, Andrew - The Knowledge Enhanced Random Demodulator
- Hashemi, Jordan - Estimation of head motion from tracked facial features
- Haupt, Jarvis - Compressive Saliency Sensing
- Henao, Ricardo - A novel statistical model for feature detection with application in high accuracy mass spectrometry data.
- Hu, Huiyi - Graphical Data Clustering and Community Detection using the MBO scheme
- Hughes, Shannon - An Efficient Method for Finding Intersections of Many Manifolds with Application to Patch-Based Image Processing
- others TBD -- sign up during registration!
12:30-1:30: Box lunch (provided)
1:30-4:00: Oral Session H3
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