Helsinki Algorithms Seminar: "Delegatable and error-tolerant algorithms" Petteri Kaski, Aalto

Event type: 
HIIT seminar
Event time: 
2017-10-19 16:15 to 17:00
Place: 
Exactum B222, Gustaf Hällströmin katu 2B, Helsinki
Description: 

Speaker: Petteri Kaski
Assistant Professor, Aalto University

Delegatable and error-tolerant algorithms

Abstract:

Is it possible to delegate a computation to an unreliable and more powerful counterparty? Can we design algorithms in such a way that not only can their execution be delegated, but a controlled number of adversarial errors can take place during the execution and yet one can recover the desired result? This talk will review theory and engineering efforts to bring such algorithm designs to the computing practice, including some of our recent work.

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Helsinki Algorithms Seminar is a weekly meeting of researchers in the Helsinki area interested in the art of algorithms and algorithm design, broadly interpreted to cover both theoretical ideas and algorithm engineering on concrete computing platforms. In most cases we have a presentation prepared for each meeting to communicate an idea, a recent result, work-in-progress, or demo, but this should not be at the expense of discussion and simply having fun with algorithms.

Our affiliations are with Aalto University and the University of Helsinki, and accordingly our activities alternate between the Otaniemi Campus of Aalto University and the Kumpula Campus of University of Helsinki, catalyzed by the Helsinki Institute for Information Technology HIIT, under the Algorithmic Data Analysis (ADA) programme.

Welcome!

Machine Learning Coffee seminar "Machine learning for Materials Research"

Lecturer : 
Flyura Djurabekova
Event type: 
HIIT seminar
Event time: 
2017-10-02 09:15 to 10:00
Place: 
Exactum D122, Kumpula
Description: 

Flyura Djurabekova, Department of Physics, University of Helsinki

Machine learning for Materials Research

Abstract: In materials research, we have learnt to predict the evolution of microstructure starting with the atomic level processes. We know about defects -- point and extended, -- and we know that these can be crucial for the final structural (and related mechanical and electrical) properties. Often simple macroscopic differential equations, which are used for the purpose, fail to predict simple changes in materials. Many questions remain unanswered. Why a ductile material suddenly becomes brittle? Why a strong concrete bridge suddenly cracks and eventually collapses after serving for tens of years? Why the wall of high quality steels in fission reactors suddenly crack? Or, why the clean smooth surface roughens under applied electric fields? All these questions can be answered, if one peeks in to atom's behavior imagining it jumping inside the material. But how the atoms "choose" where to jump amongst the numerous possibilities in complex metals? Tedious parameterization can help to deal with the problem, but machine learning can provide a better and more elegant solution to this problem. 

In my presentation, I will explain the problem at hand and show a few examples of former and current application of Neural Network for calculating the barriers for atomic jumps with the analysis of how well the applied NN worked.

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. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Next talks:

October 9, Otaniemi: Daniel Simpson
October 16, Kumpula: Elja Arjas
October 23, Otaniemi: Aristides Gionis
October 30, Kumpula: Guido Consonni

Welcome!

Rajapinta Unconference

Lecturer : 
Event type: 
Conference
Doctoral dissertation
Respondent: 
Opponent: 
Custos: 
Event time: 
2017-11-02 09:00 to 2017-11-03 17:00
Place: 
Dipoli, (Otakaari 24)
Description: 

The first annual Rajapinta unconference will be organized in Dipoli, Otakaari 24. The event is open to all interested in the study of digital society or digital methods for social sciences. Potential themes include using machine learning for social sciences, the design of digital systems used by public government, sociology motivated studies of software and developers and any other cross-disciplinary themes involving social sciences and computing.  Enroll by 10.10, it is free of charge thanks to generous support by HIIT.

Thursday 2.11. is a workshop day has two themes: online research ethics and infrastructures of digital data collection. Click here for more info.

Friday 3.11.is an unconference day, which builds upon the ideas and proposals of the participants. Please see guidelines for making your own proposal here.

The unconference is supported by HIIT and Kone Foundation.

Machine Learning Coffee seminar "Probabilistic Preference Learning With The Mallows Rank Model"

Lecturer : 
Elja Erjas
Event type: 
HIIT seminar
Event time: 
2017-10-16 09:15 to 10:00
Place: 
Exactum D122, Kumpula
Description: 

Elja Arjas, Professor Emeritus of Mathematics and Statistics, University of Helsinki

Probabilistic Preference Learning With The Mallows Rank Model

Abstract: Ranking and comparing items is crucial for collecting information about preferences in many areas, from marketing to politics. The Mallows rank model is among the most successful approaches to analyse rank data, but its computational complexity has limited its use to a form based on Kendall distance. Here, new computationally tractable methods for Bayesian inference in Mallows models are developed that work with any right-invariant distance. The method performs inference on the consensus ranking of the items, also when based on partial rankings, such as top-k items or pairwise comparisons. When assessors are many or heterogeneous, a mixture model is proposed for clustering them in homogeneous subgroups, with cluster-specific consensus rankings. Approximate stochastic algorithms are introduced that allow a fully probabilistic analysis, leading to coherent quantification of uncertainties. The method can be used, for example, for making probabilistic predictions on the class membership of assessors based on their ranking of just some items, and for predicting missing individual preferences, as needed in recommendation systems..

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. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Next talks:

October 23, Otaniemi: Aristides Gionis

October 30, Kumpula: Guido Consonni "Learning Markov Equivalence Classes of Directed Acyclic Graphs: an Objective Bayes Approach

Welcome!

Machine Learning Coffee Seminar "Latent Stochastic Models for Comparing Tumor Samples of Unknown Purity"

Lecturer : 
Antti Häkkinen
Event type: 
HIIT seminar
Event time: 
2017-09-11 09:15 to 10:00
Place: 
Seminar room T5, CS building, Konemiehentie 2, Otaniemi
Description: 

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.

Next talks:

September 18, Kumpula - Elja Arjas: Probabilistic Preference Learning With the Mallows Rank Model

September 25, Otaniemi - Hannu Toivonen

October 2, Kumpula - Flyura Djurabekova: Machine Learning for Material Research

Welcome!

 

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