Publications related to Lower Bounds on Active Learning for Graphical Model Selection

Semi-Markov model for simulating MOOC students

Pierre Dillenbourg, Lukasz Kidzinski, Louis Pierre Faucon

Large-scale experiments are often expensive and time consuming. Although Massive Online Open Courses (MOOCs) provide a solid and consistent framework for learning analytics, MOOC practitioners are still reluctant to risk resources in experiments. In this s ...

2016

Active Learning of Self-concordant like Multi-index Functions

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic

We study the problem of actively learning a multi-index function of the form f (x) = g_0 (A_0 x) from its point evaluations, where A_0 ∈ R_{k×d} with k ≪ d. We build on the assumptions and techniques of an existing approach based on low-rank matrix recover ...

2015

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its perform ...

2015

Automatic Hand Phantom Map Detection Methods

Christian Enz, Claudio Bruschini, Huaiqi Huang, Tao Li, Jörn Justiz

Many amputees have maps of referred sensation from their missing hand on their residual limb (phantom maps). This skin area can serve as a target for providing amputees with tactile sensory feedback. Providing tactile feedback on the phantom map can improv ...

2015

Introducing Geometry in Active Learning for Image Segmentation

Pascal Fua, Raphael Sznitman, Ksenia Konyushkova

We propose an Active Learning approach to training a segmentation classifier that exploits geometric priors to streamline the annotation process in 3D image volumes. To this end, we use these priors not only to select voxels most in need of annotation but ...

2015

SVM Active Learning Approach for Image Classification Using Spatial Information

Devis Tuia

In the last few years, active learning has been gaining growing interest in the remote sensing community in optimizing the process of training sample collection for supervised image classification. Current strategies formulate the active learning problem i ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Approximations for Stochastic Graph Rewriting

Sandro Stucki

In this note we present a method to compute approximate descriptions of a class of stochastic systems. For the method to apply, the system must be presented as a Markov chain on a state space consisting in graphs or graph-like objects, and jumps must be de ...

Springer International Publishing2014

Temporally Consistent Snow Cover Estimation from Noisy, Irregularly Sampled Measurements

Sabine Süsstrunk, Matthew Brown, Dominic Rüfenacht

We propose a method for accurate and temporally consistent surface classification in the presence of noisy, irregularly sampled measurements, and apply it to the estimation of snow coverage over time. The input imagery is extremely challenging, with large ...

2014

Active learning for monitoring network optimization

Devis Tuia, Alexei Pozdnoukhov

This book provides an introduction to spatio-temporal design that contains a description of one or two basic settings (e.g., migration and biodiversity) that includes real data sets, data-generating mechanisms, and possible simulation scenarios. Furthermor ...

John Wiley & Sons2013

Privacy and Dynamics of Social Networks

Pedram Pedarsani

Over the past decade, investigations in different fields have focused on studying and understanding real networks, ranging from biological to social to technological. These networks, called complex networks, exhibit common topological features, such as a h ...

EPFL2013

Lower Bounds on Active Learning for Graphical Model Selection

Graph Chatbot

Chat with Graph Search

Semi-Markov model for simulating MOOC students

Active Learning of Self-concordant like Multi-index Functions

Random sampling of bandlimited signals on graphs

Automatic Hand Phantom Map Detection Methods

Introducing Geometry in Active Learning for Image Segmentation

SVM Active Learning Approach for Image Classification Using Spatial Information

Approximations for Stochastic Graph Rewriting

Temporally Consistent Snow Cover Estimation from Noisy, Irregularly Sampled Measurements

Active learning for monitoring network optimization

Privacy and Dynamics of Social Networks

Approximations for Stochastic Graph Rewriting

Automatic Hand Phantom Map Detection Methods

Random sampling of bandlimited signals on graphs

Active learning for monitoring network optimization

Privacy and Dynamics of Social Networks

Temporally Consistent Snow Cover Estimation from Noisy, Irregularly Sampled Measurements

Semi-Markov model for simulating MOOC students

Introducing Geometry in Active Learning for Image Segmentation

SVM Active Learning Approach for Image Classification Using Spatial Information

Active Learning of Self-concordant like Multi-index Functions