| Fecha | Aula | Ponente | Tema | Enlace(s) |
|---|---|---|---|---|
| 02/06/2014 | 4.2E02 10.00 | Angel Navia | Caso de éxito: Creación de una app para móvil/tablet | |
| 09/06/2014 | 4.1E04 11.30 | Miguel Lázaro | Trabajo colaborativo usando GitHub | Slides |
| 16/06/2014 | 4.1E02 10.00 | Emilio Parrado | Discovering brain regions relevant to obsessive–compulsive disorder identification through bagging and transduction | Slides |
| 01/07/2014 | 4.1E02 10.00 | Manel Martínez | Propuestas en marcha para la NSF | Slides |
| 17/07/2014 | 4.0B01 12.30 | Jerónimo Arenas | MALLET | Slides |
| Vanessa Gómez | TBA | |||
| Sergio Muñoz | TBA | |||
| Jesús Fernández | TBA | |||
| Jesús Cid | TBA | |||
| Luis Azpicueta | TBA | |||
| Bijit Kumar | TBA |
| Fecha | Aula | Ponente | Tema | Enlace(s) |
|---|---|---|---|---|
| 17/02/2014 | 4.0E06 11.00 | Miguel Lázaro | Variational Bayes: Tutorial and recent developments | Slides |
| 24/02/2014 | 4.0E06 11.00 | Vanessa Gómez | Compressive Feature Learning by HS Paskov, R West, JC Mitchell, T Hastie |
|
| 03/03/2014 | 4.0E06 11.00 | Emilio Parrado | Latent Maximum Margin Clustering by H. Guang-Tong Zhou, Tian Lan, Arash Vahdat, and Greg Mori | |
| 17/03/2014 | 4.0E06 10.00 | Rocío Arroyo | Distributed wideband spectrum sensing for cognitive radio networks | |
| 24/03/2014 | 4.0E06 10.00 | Sergio Muñoz | Deep content-based music recommendation Aaron van den Oord, Sander Dieleman, Benjamin Schrauwen | |
| 31/03/2014 | 4.0E06 10.00 | Jesús Fernández | Stochastic variational inference by Hoffman, Wang, Blei and Paisley | |
| 07/04/2014 | 4.0E06 10.00 | Jerónimo Arenas | Practical Bayesian Optimization of Machine Learning Algorithms by Snoek, Larochelle and Adams | |
| 28/04/2014 | 4.0E06 10.00 | Angel Navia | Representation Learning: A Review and New Perspectives by Yoshua Bengio, Aaron Courville, Pascal Vincent | |
| 12/05/2014 | 4.0E06 10.00 | Jesús Cid | Kernel Bayes Rule by Fukumizu, Song, Gretton | |
| 19/05/2014 | 4.0E06 10.00 | Luis Azpicueta | Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex by Sam Patterson, Yee Whye Teh | |
| 26/05/2014 | 4.0E06 10.00 | Bijit Kumar | On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation by Harikrishna Narasimhan, Shivani Agarwal |
| Fecha | Aula | Ponente | Tema | Enlace(s) |
|---|---|---|---|---|
| 04/11/2013 | 4.1E02 11.00 | Miguel Lázaro | Gaussian process covariance kernels for pattern discovery and extrapolation Authors: Andrew G. Wilson, Ryan P. Adams Extended: GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes Authors: Andrew G. Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham |
Paper, Extended |
| 11/11/2013 | 4.2E03 16.00 | Vanessa Gómez | Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering Authors: Varoquaux, G., Gramfort, A., Thirion, B |
Paper |
| 25/11/2013 | 4.0E02 16.00 | Luis Azpicueta | Accelerating Stochastic Gradient Descent using
Predictive Variance Reduction Authors: R. Johnson and T. Zhang |
Paper |
| 02/12/2013 | 4.0E02 16.00 | Emilio Parrado | Sparse coding for multitask and transfer learning Authors: A. Maurer, M. Pontil, B. Romera-Paredes |
Paper |
| 13/01/2014 | 4.1E02 11.00 | Sergio Muñoz | Robust Regression on MapReduce Authors: X. Meng and M. Mahoney |
Paper, Presentación |
| 20/01/2014 | 4.1E02 11.00 | Angel Navia | Domain Generalization via Invariant Feature Representation Authors: K. Muandet, D. Balduzzi, B Schölkopf |
Paper, Slides |
| 27/01/2014 | 4.0B01C 11.00 | Jesús Fernández | Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC Authors: Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen |
Paper |
| 03/02/2014 | 4.2E03 11.00 | Jerónimo Arenas | New Subsampling Algorithms for Fast Least Squares
Regression Authors: P.S. Dhillon, Y. Lu, D. Foster, L. Ungar |
Paper |
| 10/02/2014 | 4.0E06 11.00 | Jesús Cid | Bayesian optimization explains human active search Authors: Ali Borji, Laurent Itti |
Paper |
| Fecha | Aula | Ponente | Tema | Enlace(s) |
|---|---|---|---|---|
| 29/04/2013 | 4.2E02 | Harold Molina | Hadoop (I/II) | Slides |
| 06/05/2013 | 4.2E02 | Harold Molina | Hadoop (II/II) | |
| 13/05/2013 | 4.2E02 | Manel Martínez | Natural Language Processing using NLTK (I/II) | Slides |
| 20/05/2013 | 4.2E02 | Vanessa Gómez | Natural Language Processing using NLTK (II/II) | |
| 03/06/2013 | 4.0B01 | Jesús Fernández | SPARK | Spark |
| 10/06/2013 | 4.1E02 | Miguel Lázaro | GPUs (CUDA) for Parallel Machine Learning | Slides, Code |
| 17/06/2013 | 4.2E02 | Emilio Parrado | Mahout | |
| 24/06/2013 | 4.2E02 | Roberto Díaz | Amazon EC2 | Slides |
| 01/07/2013 | 4.2E02 | Sergio Muñoz | SciDB | Slides |
| 08/07/2013 | 4.2E02 | Angel Navia | Deep Learning (RBM, DBNs, Conv. Nets: Practical use) | Slides |
| 14/10/2013 | 4.1E02 | Jerónimo Arenas | GraphLab | |
| 21/10/2013 | 4.1E02 | Chakraborty | Generalized Orthogonal Matching Pursuit : A Review and Some New Results | Slides |
| 28/10/2013 | 4.0E06 | Jesús Cid | Active Learning (Machine-aided labelling and practical use) |
| Fecha | Aula | Ponente | Tema | Enlace(s) |
|---|---|---|---|---|
| 17/10/2012 | 4.0E02 | Manel Martínez | ML applications for clinical neuroimaging (I/II) | Slides |
| 24/10/2012 | 4.0E02 | Manel Martínez | ML applications for clinical neuroimaging (II/II) | |
| 31/10/2012 | 4.0E02 | Chakraborty | Sparse filtering | |
| 07/11/2012 | 4.0E02 | Vanessa Gómez | Recommender systems | Slides, Adv. in Collab. Filtering |
| 14/11/2012 | 4.0E02 | Jesús Fernández | Reinforcement Learning | Slides |
| 28/11/2012 | 4.0E02 | Fran Valverde | Co-clustering using Formal Concept Analysis and Inductive Databases | Slides |
| 12/12/2012 | 4.0E02 | Luis Muñoz | Preference Learning | Slides |
| 09/01/2013 | 4.0E02 | Miguel Lázaro | Probabilistic Programming | Slides |
| 16/01/2013 | 4.0E02 | Emilio Ortiz | Natural Language Processing | Slides |
| 01/02/2013 | 4.2E02 | Emilio Parrado | Metric Learning | Slides |
| 04/02/2013 | 4.2E02 | Roberto Díaz | Probabilistic Numerics | Slides, NIPS 1, NIPS 2 |
| 11/02/2013 | 4.2E02 | Sergio Muñoz | Big Data | |
| 18/02/2013 | 4.2E02 | Angel Navia | Deep Learning | Slides |
| 25/02/2013 | 4.2E02 | Jerónimo Arenas | Change-point Detection | Slides |
| 04/03/2013 | 4.2E02 | Jesús Cid | Active Learning | Slides |
| 11/03/2013 | 4.2E02 | Luca Martino | A brief journey through the MCMC world | Slides | 08/04/2013 | 4.2E02 | Todos | Concretar temas para la Ronda 4 | 15/04/2013 | 4.2E02 | João Sato | Machine Learning methods in Neuroimaging: hot topics! | 22/04/2013 | 4.2E02 | Luis Azpicueta | TrueSkill | Slides |
| Fecha | Aula | Ponente | Tema | Enlace(s) |
|---|---|---|---|---|
| 16/02/2012 | 4.2E02 | Miguel Lázaro | Adaptive KRLS: Many ways to get it wrong | One way to get it right, slides, code |
| 01/03/2012 | 4.2E02 | Emma Izquierdo | Semisupervised Kernel Feature Extraction for Remote Sensing Image Classification | Under review, check email |
| 15/03/2012 | 4.2E02 | Magno T.M. Silva | Algoritmos para igualación ciega de señales QAM | ICASSP paper, also check email |
| 29/03/2012 | 4.2E02 | Emilio Ortiz | On Similarity Preserving Feature Selection | Paper |
| 12/04/2012 | 4.2E02 | Emilio Parrado | Multiway Spectral Clustering: A Margin-Based Perspective | Paper |
| 26/04/2012 | 4.2E02 | Roberto Díaz | Statistical Comparisons of Classifiers over Multiple Data Sets | JMLR paper |
| 10/05/2012 | 4.2E02 | Sergio Muñoz | Reduced-Rank Adaptive Filtering for Space-Time Interference Suppression | Paper, background |
| 24/05/2012 | 4.2E02 | Jerónimo Arenas | A least-squares scheme for the adaptation of combiners over diffusion networks | background |
| 31/05/2012 | 4.2E02 | Angel Navia | Data visualization | JMLR paper, AISTATS paper |
| 14/06/2012 | 4.2E02 | Jerónimo Arenas | Semisupervised unbiased estimation via convex combination | Check email |
| 21/06/2012 | 4.2E02 | Jesús Cid | Proper Losses for Learning from Partial Labels | Check email |
| Fecha | Aula | Ponente | Tema | Enlace(s) |
|---|---|---|---|---|
| 18/10/2011 | 4.2E02 | Miguel Lázaro | Marginalized Variational Approximation for Heteroscedastic Regression | Paper, extra material, code |
| 25/10/2011 | 4.1E02 | Emilio Parrado | Métricas para datos secuenciales | Paper PAMI, Paper PR |
| 08/11/2011 | 4.0E04 | Roberto Díaz | Semiparametric SVMs | Paper IJCNN, Paper Neurocomputing |
| 15/11/2011 | 4.2E03 | Sergio Muñoz | OPLS en problemas mal condicionados y sus extensiones | Sparse OPLS, Paper NIPS |
| 22/11/2011 | 4.0E04 | Jerónimo Arenas | Mejora de la generalización de clasificadores máquina mediante correción de varianza en el conjunto de test |
Paper JMLR |
| 29/11/2011 | 4.1E02 | Jesús Cid | Learning from Partial Labels | Paper JMLR |
| 20/12/2011 | 4.2E03 | Darío GG | Degrees of supervision | Paper |
| 10/01/2012 | 4.2E03 | Rocío Arroyo | Aprendizaje por refuerzo. Aplicación a redes de sensores. | Paper, recent survey, old survey | 17/01/2012 | 4.2E03 | Manel Martínez | A Support Vector Machine Music Algorithm | Paper unavailable | 26/01/2012 | 4.1E02 | Fran Valverde | Two information-theoretic tools to assess the performance of multi-class classifiers | Paper | 02/01/2012 | 4.2E02 | Luis Muñoz | Additive Gaussian processes | Paper, code | 09/02/2012 | 4.2E02 | Vanessa Gómez | Ongoing research on sparsity methods via L0 regularization. | Reference 1 (pp. 1-2), Reference 2 (pp. 1-4) |
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| Universidad Carlos III de Madrid, Dpto. Teoría de la Señal y Comunicaciones |