Minisymposium on Simulator-Based Inference

Submitted by troos on April 6, 2018 - 20:02

Lecturer :

Event type:

HIIT seminar

Doctoral dissertation

Respondent:

Opponent:

Custos:

Event time:

2018-04-09 09:15 to 11:00

Place:

Chemicum A129

Web page:

Machine Learning Coffee seminar

Description:

Minisymposium on Simulator-Based Inference

Finnish Center for Artificial Intelligence (FCAI) is proud to present a series of longer machine learning coffee sessions on themes of the research programmes of FCAI. These minisymposiums will last for almost 2 hours and consist of some longer talks and a set of 5-min flash talks. The purpose is both for the researchers already working on the theme to get to know what the others are working on, and for those interested in the theme to get to know who to talk to. Suggestions of flash talk topics are welcome - contact the organizers of the specific symposium.

Organizers: Samuel Kaski and Jaakko Lehtinen

Talks

Inferring Cognitive Simulators From Data

Abstract: I will discuss the intriguing problem of inferring parameters of cognitive simulator models from user interaction data. The parameters can include the goals, interests and capabilities of the user, which become disentangled in the inference.

Speaker: Samuel Kaski

Negative Frequency-Dependent Selection Dictates the Fate of Bacteria

Abstract: In recent work we discovered that negative frequency-dependent selection (NFDS) acting on accessory genes appears as the dominating evolutionary force of populations of Streptococcus pneumoniae, which is a major human pathogen. This discovery was greatly facilitated by the recent advances in ABC inference brought by Bayesian optimization which has accelerated model fitting by several orders of magnitude. I will discuss the biological basis of the NFDS principle and show emerging evidence that it is commonly dictating the fate of bacteria also in ubiquitous organisms such as Escherichia coli.

Speaker: Jukka Corander

Why Learn Something You Know?

Abstract: Much of science can be seen as a search of mathematical models that predict observable quantities based on other observable quantities: future positions of stars based on their past positions, incidence of cancer based on biological health measurements, etc. Unlike handcrafted models derived from first principles employed in e.g. physics, many modern machine learning and AI techniques approach the same issue from a different perspective: fixing a highly powerful but general (not problem-specific) model, and setting its parameters based on data. Results are dramatic but often uninterpretable – we do not know precisely how the model comes to its conclusions. In this overview talk, I’ll present thoughts on combining the two approaches, along with recent exciting examples.

Speaker: Jaakko Lehtinen