Machine Learning Group - Talks

Machine Learning Group

Temas de la siguiente ronda

De momento, tenemos estos.


Lunes a la hora y en aula el aula correspondiente.


Ronda 7

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

Ronda 6

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

Ronda 5

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
25/11/2013 4.0E02 16.00 Luis Azpicueta Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
Authors: R. Johnson and T. Zhang
02/12/2013 4.0E02 16.00 Emilio Parrado Sparse coding for multitask and transfer learning
Authors: A. Maurer, M. Pontil, B. Romera-Paredes
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
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
10/02/2014 4.0E06 11.00 Jesús Cid Bayesian optimization explains human active search
Authors: Ali Borji, Laurent Itti

Ronda 4

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)

Ronda 3

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 email
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

Ronda 2

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

Ronda 1

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)

Lista de participantes (Faltas consecutivas)

© Miguel Lázaro-Gredilla
Last modified: 2014-07-20, 19:28