mlsplogo MLSP2016
IEEE International Workshop on
Machine Learning for Signal Processing

September 13-16, 2016  Vietri sul Mare, Salerno, Italy


Tuesday September 13, 2016
12:30-14:00Tutorial: Probabilistic Programming for Augmented Intelligence

Vikash K. Mansinghka
Massachusetts Institute of Technology, Boston, USA

14:15-15:45Tutorial: Probabilistic graphical models for bayesian learning of state flow models in non stationary environments

Carlo Regazzoni
University of Genova, Italy

15:45-16:30Coffee Break
Poster Session 1: Machine Learning for Speech and Audio Analysis
Chair: Aurelio Uncini, Sapienza University of Rome, Italy

19:00-21:30Welcome Reception

Wednesday September 14, 2016
9:00-10:00Keynote Lecture: Towards Socially-aware AI: Human Behavior Understanding at Scale

Alexandre Alahi
Stanford University, USA

10:00-10:30Coffee Break
10:30-12:30Lecture Session 1: Special session on Advances in Gaussian Processes for Machine Learning and Signal Processing
Chair: Gustau Camps-Valls, Univ. Valencia, Spain, and Simo Särkkä, Aalto Univ., Finland

10:30On The Use Of Gradient Information In Gaussian Process Quadratures
Jakub Prüher, Simo Särkkä
10:50Scalable Transformed Additive Signal Decomposition By Non-Conjugate Gaussian Process Inference
Adam Vincent, James Hensman, Maneesh Sahani
11:10Variational Gaussian Process For Missing Label Crowdsourcing Classification Problems
Pablo Ruiz, Emre Besler, Rafael Molina, Aggelos K. Katsaggelos
11:30On The Relationship Between Online Gaussian Process Regression And Kernel Least Mean Squares Algorithms
Steven Van Vaerenbergh, Jesus Fernandez-Bes, Victor Elvira
11:50Latent Force Models For Earth Observation Time Series Prediction
David Luengo, Manuel Campos-Taberner, Gustau Camps-Valls
12:10Projection Predictive Model Selection For Gaussian Processes
Juho Piironen, Aki Vehtari
14:00-16:00Lecture Session 2: Special Session on Bayesian Machine Learning for Neural Signal Processing
Chair: Zhe Chen, New York Univ., USA

14:00Score-Matching Estimators For Continuous-Time Point-Process Regression Models
Maneesh Sahani, Gergo Bohner, Arne Meyer
14:20Bayesian Learning For Speech Dereverberation
Jen-Tzung Chien, You-Cheng Chang
14:40A Multimodal Multiple Kernel Learning Approach To Alzheimer's Disease Detection
Michele Donini, João M. Monteiro, Massimiliano Pontil, John Shawe-Taylor, Janaina Mourao-Miranda
15:00The Influence Of Hyper-Parameters In The Infinite Relational Model
Kristoffer Jon Albers, Morten Mørup, Mikkel Schmidt
15:20Fully Bayesian Tensor-Based Regression
Flavio Camarrone, Marc Van Hulle
15:40Bayesian Latent Feature Modeling For Modeling Bipartite Networks With Overlapping Groups
Philip Johan Havemann Jørgensen, Morten Mørup, Mikkel Nørgaard Schmidt, Tue Herlau
16:00-17:00Coffee table in poster room
Poster Session 2: Machine Learning Applications
Chair: Vince Calhoun, The Mind Research Network, USA

Thursday September 15, 2016
9:00-10:00Keynote Lecture: Gaussian Processes for Signal Processing

Richard E. Turner
University of Cambridge, United Kingdom

10:00-10:30Coffee Break
10:30-12:30Lecture Session 3: Special session on Computational Methods for Audio Analysis
Chair: Michele Scarpiniti, Sapienza University of Rome, Italy

10:30Variance Reduction For Optimization In Speech Recognition
Jen-Tzung Chien, Pei-Wen Huang
10:50A Fully Convolutional Deep Auditory Model For Musical Chord Recognition
Filip Korzeniowski, Gerhard Widmer
11:10Bird Detection In Audio: A Survey And A Challenge
Dan Stowell, Mike Wood, Yannis Stylianou, Hervé Glotin
11:30Learning To Reproduce A Sound Field
Hanieh Khalilian, Ivan V Bajic, Rodney G Vaughan
11:50A Neural Network Based Algorithm For Speaker Localization In A Multi-Room Environment
Fabio Vesperini, Paolo Vecchiotti, Emanuele Principi, Stefano Squartini, Francesco Piazza
12:10On The Use Of Machine Learning In Microphone Array Beamforming For Far-Field Sound Source Localization
Daniele Salvati, Carlo Drioli, Gian Luca Foresti
14:00-16:00Lecture Session 4: Machine Learning Applications
Chair: Konstantinos Diamantaras, TEI of Thessaloniki, Greece

14:00Data Privacy Protection By Kernel Subspace Projection And Generalized Eigenvalue Decomposition
Konstantinos Diamantaras, Sun-Yuan Kung
14:20Discriminant-Component Eigenfaces For Privacy-Preserving Face Recognition
Thee Chanyaswad, J. Morris Chang, Prateek Mittal, S.y. Kung
14:40Image Restoration With Locally Selected Class-Adapted Models
Afonso M. Teodoro, José M. Bioucas-Dias, Mário A. T. Figueiredo
15:00Combining Clusterings With Different Detail Levels
Oded Kaminsky, Jacob Goldberger
15:20Automatic Classification Of Irregularly Sampled Time Series With Unequal Lengths: A Case Study On Estimated Glomerular Filtration Rate
Santosh Tirunagari, Simon C Bull, Norman Poh
15:40Articulatory And Spectrum Features Integration Using Generalized Distillation Framework
Jianguo Yu, Konstantin Markov, Tomoko Matsui
16:00-17:00Coffee table in poster room
Poster Session 3: Machine Learning Theory and Algorithms
Chair: Ahmed Tewfik, Univ. Texas at Austin, USA


Friday September 16, 2016
9:00-10:00Keynote Lecture: Toward Causal Machine Learning

Bernhard Schölkopf
Max Planck Institute for Intelligent Systems Tübingen, Germany

10:00-10:30Coffee Break
10:30-12:30Lecture Session 5: Machine Learning Theory and Algorithms
Chair: Jan Larsen, Technical University of Denmark

10:30Enhanced Distance Subset Approximation Using Class-Specific Subspace Kernel Representation For Kernel Approximation
Yinan Yu, Konstantinos Diamantaras, Tomas Mckelvey, S.y. Kung
10:50A Model Explanation System
Ryan Turner
11:10Parallel And Distributed Training Of Neural Networks Via Successive Convex Approximation
Paolo Di Lorenzo, Simone Scardapane
11:30Localizing Users And Items From Paired Comparisons
Matthew Robert O'shaughnessy, Mark Andrew Davenport
11:50Underwater Target Classification Using A Pose-Invariant Matched Manifold Classifier
Pooria Pakrooh, Louis L. Scharf, Mahmood R. Azimi-Sadjadi
12:10Sparse-Coded Net Model And Applications
Youngjune Gwon, Miriam Cha, William Campbell, Ht Kung, Cagri Dagli
14:00-16:00Lecture Session 6: Sparse Dictionary Learning and Source Separation
Chair: Kush Varshney, IBM, USA

14:00BDL.NET: Bayesian Dictionary Learning In
Tom Diethe, Niall Twomey, Peter Flach
14:20A Robust Maximum Correntropy Criterion For Dictionary Learning
Carlos Loza, Jose Principe
14:40Doubly Sparse Structure In Image Super Resolution
Toshiyuki Kato, Hideitsu Hino, Noboru Murata
15:00Towards Optimal Nonlinearities For Sparse Recovery Using Higher-Order Statistics
Steffen Limmer, Slawomir Stanczak
15:20Convolutional Higher Order Matching Pursuit
Gergő Bohner, Maneesh Sahani
15:40Online Estimation Of Inter-Channel Phase Differences Using Non-Negative Matrix Factorization
Kamil Adiloğlu, Graham Coleman, Hendrik Kayser, Volker Hohmann
16:00-17:00Coffee table in poster room
Poster Session 4: Dictionary Learning and Matrix Methods for Signal Analysis
Chair: Simo Särkkä, Aalto Univ., Finland

18:30-19:00End of the conference
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