Machine Learning Coffee seminar "Inverse Modeling in Behavioral Sciences and HCI"

Lecturer : 
Antti Oulasvirta
Event type: 
HIIT seminar
Event time: 
2017-02-27 09:15 to 10:00
Place: 
Exactum D123, Kumpula
Description: 
This week's speaker at the Machine Learning Coffee seminar is Antti Oulasvirta, Associate Professor, Aalto University.

Inverse Modeling in Behavioral Sciences and HCI

Abstract: Can one make deep inferences about a person based only on observations of how she acts? I discuss methodology for inverse modeling in behavioral sciences, where the goal is to estimate a cognitive model from limited behavioral data. Given substantial diversity in people's intentions, strategies and abilities, this is a difficult problem and previously unaddressed. I discuss advances achieved with an approach that combines (1) computational rationality, to predict how a person adapts to a task when her capabilities are known, and (2) Approximate Bayesian Computation (ABC) to estimate those capabilities. The benefit is that model parameters are conditioned on both prior knowledge and observations, which improves model validity and helps identify causes for observations. Inverse modeling methods can advance theory-formation by bringing complex behavior within reach of modeling. This talk is based on on-going collaborations with Antti Kangasraasio, Samuel Kaski, Jukka Corander, Andrew Howes, Kumaripaba Athukorala, Jussi Jokinen, Sayan Sarcar, and Xiangshi Ren.

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held on Mondays at 9 am at Aalto University and the University of Helsinki every other week. At Aalto University, talks will be held in Konemiehentie 2, seminar room T5 and at the University of Helsinki in Kumpula, seminar room D123, unless otherwise noted. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Welcome!

 

Machine Learning Coffee seminar "Compressed Sensing for Semi-Supervised Learning From Big Data Over Networks"

Lecturer : 
Alexander Jung
Event type: 
HIIT seminar
Doctoral dissertation
Respondent: 
Opponent: 
Custos: 
Event time: 
2017-02-20 09:15 to 10:00
Place: 
Konemiehentie 2, seminar room T5
Description: 

This week's speaker at the Machine Learning Coffee seminar is Alexander Jung.

Alexander Jung, Assistant Professor, Department of Computer Science, Aalto University

Compressed Sensing for Semi-Supervised Learning From Big Data Over Networks

Abstract: In this talk I will present some of our most recent work on the application of compressed sensing to semi-supervised learning from massive network-structured datasets, i.e., big data over networks. We expect the user of compressed sensing ideas to be game-changing for machine learning from big data in a similar manner as it was for digital signal processing. In particular, I will present a sparse label propagation algorithm which efficiently learn from large amounts of network-structured unlabeled data by leveraging the information provided by a few initially labelled training data points. This algorithm is inspired by compressed sensing recovery methods and allows for a simple sufficient condition on the network structure which guarantees accurate learning.

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held on Mondays at 9 am at Aalto University and the University of Helsinki every other week. At Aalto University, talks will be held in Konemiehentie 2, seminar room T5 and at the University of Helsinki in Kumpula, seminar room D123, unless otherwise noted. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Welcome!

Machine Learning Coffee seminar "Variable Selection From Summary Statistics"

Lecturer : 
Matti Pirinen
Event type: 
HIIT seminar
Event time: 
2017-02-13 09:15 to 10:00
Place: 
Exactum D123, Kumpula
Description: 

This week's speaker at our Machine Learning Coffee seminar will be

Matti Pirinen, Assistant Professor/Academy Research Fellow, Department of Mathematics and Statistics/Faculty of Medicine/FIMM, University of Helsinki

 

Variable Selection From Summary Statistics

Abstract:  With increasing capabilities to measure a massive number of variables, efficient variable selection methods are needed to improve our understanding of the underlying data generating processes. This is evident, for example, in human genomics, where genomic regions showing association to a disease may contain thousands of highly correlated variants, while we expect that only a small number of them are truly involved in the disease process. I outline recent ideas that have made variable selection practical in human genomics and demonstrate them through our experiences with the FINEMAP algorithm (Benner et al. 2016, Bioinformatics).

  1. Compressing data to light-weight summaries to avoid logistics and privacy concerns related to complete data sharing and to minimize the computational overhead.
  2. Efficient implementation of sparsity assumptions.
  3. Efficient stochastic search algorithms.
  4. Use of public reference databases to complement the available summary statistics

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held on Mondays at 9 am at Aalto University and the University of Helsinki every other week. At Aalto University, talks will be held in Konemiehentie 2, seminar room T5 and at the University of Helsinki in Kumpula, seminar room D123, unless otherwise noted. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Welcome!

Machine Learning Coffee seminar "Toward perfect density estimation"

Lecturer : 
Petri Myllymäki
Event type: 
HIIT seminar
Doctoral dissertation
Respondent: 
Opponent: 
Custos: 
Event time: 
2017-02-06 09:15 to 10:00
Place: 
Konemiehentie 2, seminar room T5
Description: 

The Machine Learning Coffee seminar (now featuring delicious porridge as well!) will continue on Monday, February 6th in Otaniemi.

Petri Myllymäki, Professor, Department of Computer Science, University of Helsinki

 

Toward Perfect Density Estimation

Abstract: We start by addressing a most simple problem, estimation of a one dimensional density function, and argue that despite of the apparent simplicity of the problem, it is surprisingly difficult to solve it in a holistic manner that is both computationally feasible and theoretically justifiable without strong distributional or other assumptions. We demonstrate how the information-theoretic MDL framework can be used for reaching this goal (almost) perfectly, and show how this simple setup gives interesting perspectives on the fundamental concepts in probabilistic modelling and statistical inference. We also discuss ideas for extending the framework to more complex models with additional practical applications.

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held on Mondays at 9 am at Aalto University and the University of Helsinki every other week. At Aalto University, talks will be held in Konemiehentie 2, seminar room T5 and at the University of Helsinki in Kumpula, seminar room D123, unless otherwise noted. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Welcome!

Adventures in building Emotional Intelligence Technologies

Lecturer : 
Rosalind Picard
Event type: 
Guest lecture
Event time: 
2017-02-03 15:15 to 16:30
Place: 
Small Hall, Main Building, University of Helsinki, Fabianinkatu 33, Helsinki
Description: 

The next lecture in the Helsinki Distinguished Lecture Series on Future Information Technology will be given by Professor Rosalind Picard from the MIT Media Lab.

The lecture is free of charge and open to everyone interested in the latest research in information technology. The lecture will be followed by an informal cocktail event.

Registration is now closed (but we still have some space remaining).

 

Adventures in building Emotional Intelligence Technologies

Abstract

Years ago, I set out to create technology with emotional intelligence, demonstrating the ability to sense, recognize, and respond intelligently to human emotion. At MIT, we designed studies and developed signal processing and machine learning techniques to see what affective insights could be reliably obtained. In this talk I will highlight the most surprising findings during this adventure. These include new insights about the "true smile of happiness," discovering new ways cameras (and your smartphone, even in your handbag) can compute your bio-signals, finding electrical signals on the wrist that reveal insight into deep brain activity, and learning surprising implications of wearable sensing for autism, anxiety, sleep, memory, epilepsy, and more. What is the grand challenge we aim to solve next?

About the Speaker

Rosalind Picard, ScD, FIEEE is founder and director of the Affective Computing Research Group at the MIT Media Laboratory, co-founder of Affectiva, providing emotional intelligence technology used by 1/3 of the Global Fortune 100, and co-founder and Chief Scientist of Empatica, improving lives with clinical-quality wearable sensors and analytics. Picard is the author of over 250 articles in computer vision, pattern recognition, machine learning, signal processing, affective computing, and human-computer interaction. She is known internationally for her book, Affective Computing, which helped launch the field by that name. Picard holds bachelors in Electrical Engineering (EE) from Georgia Tech and Masters and Doctorate degrees in EE and CS from MIT. Picard’s inventions have been twice named to "top ten" lists, including the New York Times Magazine's Best Ideas of 2006 for the Social Cue Reader, and 2011's Popular Science Top Ten Inventions for a Mirror that Monitors Vital Signs.

 

 

Pages