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IDA Machine Learning Seminars - Fall 2017


Wednesday, September 13, 3.15 pm, 2017

Open Research Problems in Computer Graphics and Games
J.P. Lewis
, SEED Research Lab, Electronic Arts
Abstract: The computer games and movie visual effects industries are increasingly tracking and adopting academic research in machine learning and computer vision. This talk will survey some of these applications. The talk will then mention some open research problems motivated by industry. Lastly, we will also identify assumptions in academic research that occasionally prevent promising results from being easily adopted.
Location: Ada Lovelace (Visionen)
Organizer: Mattias Villani


Friday, October 13, 3.15 pm, 2017

Multi-target prediction: a unifying view on problems and methods
Willem Waegeman
, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Belgium
Abstract: Traditional methods in machine learning and statistics provide data-driven models for predicting one-dimensional targets, such as binary outputs in classification and real-valued outputs in regression. In recent years, novel application domains have triggered fundamental research on more complicated problems where multi-target predictions are required. Such problems arise in diverse application domains, such as document categorization, tag recommendation of images, videos and music, information retrieval, medical decision making, drug discovery, marketing, biology, geographical information systems, etc. In this talk I will present a unifying view on multi-target prediction (MTP) in two directions. In the first part I will establish connections among different MTP problems settings, by formalizing several subfields of machine learning, such as multi-label classification, multivariate regression, multi-task learning, etc. In the second part I will describe general principles that lead to performance improvements in several types of MTP problems.
Location: Ada Lovelace (Visionen)
Organizer: Jose M. Peña


Wednesday, November 8, 3.15 pm, 2017

Approximate Bayesian inference: Variational and Monte Carlo methods
Christian A. Naesseth
, Division of Automatic Control, Department of Electrical Engineering, Linköping University
Abstract: Many recent advances in large scale probabilistic inference rely on the combination of variational and Monte Carlo (MC) methods. The success of these approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. My aim is to show how MC methods can be used not only for stochastic optimization of the variational parameters, but also for defining a more flexible parametric approximation in the first place. First, I will review variational inference (VI). Second, I describe some of the pivotal tools for VI, based on MC methods and stochastic optimization, that have been developed in the last few years. Finally, I will show how we can synthesize sequential Monte Carlo methods and VI to learn more accurate posterior approximations with theoretical guarantees.
Location: Ada Lovelace (Visionen)
Organizer: Mattias Villani


Wednesday, December 6, 3.15 pm, 2017

TBA
Richard Johansson
, Department of Computer Science and Engineering, Chalmers University
Abstract: TBA
Location: Ada Lovelace (Visionen)
Organizer: TBA



Future Seminars

Spring 2018   |   Fall 2018


Past Seminars

Spring 2017   |   Fall 2016   |   Spring 2016  |   Fall 2015   |   Spring 2015   |   Fall 2014



The seminars are typically held every fourth Wednesday at 15.15-16.15 in Ada Lovelace (Visionen).
For further information, or if you want to be notified about the seminars by e-mail, please contact Mattias Villani.


Page responsible: Mattias Villani
Last updated: 2017-11-02