Summer Schools

TUTORIAL - Spectral Methods for Dimensionality Reduction (Part 1)

TUTORIAL - High dimensional geometry: concentration and curvature (Part 1)

TUTORIAL - Graphical models: parametric and nonparametric perspectives (Part 1)

Learning Graphical Models from Data

Multiscale geometric clustering of data sets

TUTORIAL - Spectral Methods for Dimensionality Reduction (Part 2)

TUTORIAL - High dimensional geometry: concentration and curvature (Part 2)

TUTORIAL - Graphical models: parametric and nonparametric perspectives (Part 2)

Fast kernel classifiers through online and active learning

Diffusion Geometry and High-Dimensional Data

TUTORIAL - Spectral Methods for Dimensionality Reduction (Part 3)

TUTORIAL - High dimensional geometry: concentration and curvature (Part 3)

TUTORIAL - Graphical models: parametric and nonparametric perspectives (Part 3)

Biological network analysis

Fast Multipole Method

TUTORIAL - Spectral Methods for Dimensionality Reduction (Part 4)

TUTORIAL - Reproducing Kernel Hilbert Spaces and why they are important.

TUTORIAL - The Multicategory Support Vector Machine (of Lee, Lin and Wahba)

TUTORIAL - Regularized Kernel Estimation (RKE) From Dissimilarity Data

Some theoretical aspects of support vector machines and related kernel-based learning methods

Relationship Detection in Semantic Graphs

TUTORIAL - Probabilistic Models of Relational Data (Part 1)

TUTORIAL - Probabilistic Models of Relational Data (Part 2)

Diffusion geometries as organisational tools for information and data analysis

Rodeo: Sparse nonparametric regression in high dimensions

TUTORIAL - Geometry in high dimensional data and image processing (Part 1)

TUTORIAL - Geometry in high dimensional data and image processing (Part 2)

TUTORIAL - Geometry in high dimensional data and image processing (Part 3)

Dimension Reduction of Hyperspectral Image (HSI) Data

Adventures in High Dimensional Data Analysis: Hyperspectral Gaseous Plume Detection

TUTORIAL - Energy-Based Models (Part 1)

TUTORIAL - Rational Kernels: A Unifying Kernel Framework for the Analysis of Text, Speech, and Biological Sequences (Part 1)

TUTORIAL - Metrics and Regularizations in Image Analysis (Part 1)

Detecting Nuclear Materials from Cosmic-Ray Muon Scattering Data

Proton and X-ray tomography: Image analysis successes and challenges

TUTORIAL - Energy-Based Models (Part 2)

TUTORIAL - Rational Kernels: A Unifying Kernel Framework for the Analysis of Text, Speech, and Biological Sequences (Part 2)

TUTORIAL -Metrics and Regularizations in Image Analysis (Part 2)

Unsupervised and weakly supervised extraction of information from image and video data

TUTORIAL - Learning Invariant Representations in High Dimension:Application to object detection and recognition in images.

TUTORIAL - Introduction to numerical geometry of images

TUTORIAL - Metrics and Regularizations in Image Analysis (Part 3)

A contrario detection of clusters, and application to shape recognition

Natural image statistics and biological vision

TUTORIAL - Learnable Metrics and Trainable Graph Machines.

TUTORIAL - Matching isometric manifolds by flat embedding

Inverse scale space for image restoration

Face recognition

Visibility Optimizations

TUTORIAL - Massive Graph Mining (Part 1)

TUTORIAL - Web classification and clustering using links

Proximity Graphs for Instance-Based Learning

Scan statistics on graphs

Near(est) neighbor problem in high dimensions

TUTORIAL - Massive Graph Mining (Part 2)

TUTORIAL - Segmentation problems

Random Dot Product Graphs

Data structures and algorithms for tractable statistical mining of very high dimensional data.

Fast Monte Carlo Algorithms for Matrix Operations and Massive Data Set Analysis

TUTORIAL: Analysis of Biological Networks (Part 1)

TUTORIAL - Vector space retrieval: extension, efficiency and evaluation

Mathematics and algorithmics of process detection

Research Challenges in the BioEncyclopedia Project

Dynamic Generalized Linear Models for Graphs

TUTORIAL: Analysis of Biological Networks (Part 2)

TUTORIAL - Variable Latent Semantic Indexing

Finding patterns in large, real networks

Conditional random fields

Content and the Scan Statistic for Enron

TUTORIAL: Analysis of Biological Networks (Part 3)

Statistical Inference in Sensor Networks

Information to Insight

Cycles, Communities, Ergodicity and Communications in Network Topologies