Machine Learning Coffee seminar "On Priors and Bayesian Variable Selection in Large p, Small n Regression"

Lecturer : 
Aki Vehtari
Event type: 
HIIT seminar
Event time: 
2017-05-22 09:15 to 10:00
Place: 
Exactum D123, Kumpula
Description: 

Aki Vehtari, Associate Professor in Computational Science, Aalto University

On Priors and Bayesian Variable Selection in Large p, Small n Regression

Abstract: The Bayesian approach is well known for using priors to improve inference, but equally important part is the integration over the uncertainties. I first present recent development in hierarchical shrinkage priors for presenting sparsity assumptions in covariate effects. I then present a projection predictive variable selection approach, which is a Bayesian decision theoretical approach for variable selection which can preserve the essential information and uncertainties related to all variables in the study. I also present recent excellent experimental results and easy to use software.

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:

May 29, Otaniemi: Jaakko Lehtinen, Graphics Meets Vision Meets Machine Learning
June 5, Kumpula: Jarno Vanhatalo
June 12, Otaniemi: Zhirong Yang: Learning Data Representation by Large-Scale Neighbor Embedding
--after June 12, we'll have a summer break and continue on September 4, 2017--

Welcome!

Allan Tucker: "Three Algorithms Inspired by Data from the Life Sciences"

Lecturer : 
Allan Tucker, PhD, Senior Lecturer
Event type: 
Guest lecture
Event time: 
2017-05-22 10:15 to 11:00
Place: 
Exactum D123, Kumpula
Description: 

Dr Allan Tucker (Brunel University, UK) will visit us on Monday, 22nd May, and present the following talk. Dr Tucker will also be available for discussions on the day of the talk.

A simulated time series from a three-state HMM for modelling disease progression. Source: Allan Tucker. 
Three Algorithms Inspired by Data from the Life Sciences
Abstract:  In this talk I will discuss how the analysis of real-world data from health and the environment can shape novel algorithms. Firstly, I will discuss some of our work on modelling clinical data using probabilistic graphical models (dynamic Bayesian networks, and hidden Markov models). In particular I will discuss the collection of longitudinal data and how this creates challenges for diagnosis and the modelling of disease progression that can be overcome with latent variables. I will then discuss how cross-sectional studies offer additional useful information that can be used to model disease diversity within a population but lack valuable temporal information. Finally, I will discuss the importance of inferring models that generalise well to new independent data and how this can sometimes lead to new challenges, where the same variables can represent subtly different phenomena. Some examples in ecology and genomics will be described.
Short bio: Dr Tucker's first degree was in Cognitive Science at Sheffield University, UK, where he became interested in models of brain function and human and animal behaviour. His other interests include learning models of time-series data in order to try and understand the underlying processes, with a focus on biological, clinical and ecological data. He received his Ph.D at Birkbeck College, University of London sponsored by the Engineering and Physical Sciences Research Council; Honeywell Hi-Spec Solutions, UK; and Honeywell HTC, USA. As a Senior Lecturer at Brunel University London he leads the Intelligent Data Analytics Research group. He has worked in conjunction with Leiden University Medical School and University College London on gene regulatory networks. His current projects include modelling high dimensional gene expression data with Rothamsted Research; modelling visual field test data from Moorfield's Eye Hospital, London; text mining flora with the Royal Botanical Gardens,  Kew London; an epidemiological big data analytics project in Kazakhstan funded by the British Council; and exploring the dynamics of fish populations in the Northern Atlantic in conjunction with the Canadian Department of Fisheries and Oceans and DEFRA.
 
 
 
Welcome!
 
Host: Teemu Roos, teemu.roos@cs.helsinki.fi.

 

Guest lecture by Professor Vijay Raghavan

Lecturer : 
Vijay V. Raghavan
Event type: 
Guest lecture
Event time: 
2017-05-26 10:15 to 11:00
Place: 
Exactum D123, Kumpula
Description: 

Time: Friday, May 26th at 10:15-11:00  (Coffee available at 10:00)
Place: Kumpula, Exactum D123.

Speaker: 
Vijay V. Raghavan
School of Computing and Informatics
University of Louisiana at Lafayette, USA

Title: A Framework for Real-Time Event Detection for Emergency Situations using Social Media Streams

Abstract:

In this presentation, we propose an event detection approach to aid in real-time event detection. Social media generates information about news and events in real-time. Given the vast amount of data available and the rate of information propagation, reliably identifying events can be a challenge. Most state of the art techniques are post hoc techniques, which detect an event after it happened. Our goal is to detect the onset of an event as it is happening, using the user-generated information from Twitter streams. To achieve this goal, we use a discriminative model to identify a sudden change in the pattern of conversations over time. We also use a topic evolution model to identify credible events and propose an approach to eliminate random noise that is prevalent in many of the existing topic detection approaches. The simplicity of our proposed approach allows us to perform fast and efficient event detection, permitting discovery of events within minutes of the first conversation relating to an event started. We also show that this approach is applicable for other social media datasets to detect change over the longer periods of time.

We extend the proposed event detection approach to incorporate information from multiple data sources with different velocity and volume. We study the event clusters generated from event detection approach for changes in events over time. We also propose and evaluate a location detection approach to identify the location of a user or an event based on tweets related to them.

Bio:

Dr. Vijay Raghavan is the Alfred and Helen Lamson/ BoRSF Endowed Professor in Computer Science at the Center for Advanced Computer Studies and the Director of the NSF-sponsored Industry/ University Cooperative Research Center for Visual and Decision Informatics. As the director, he co-ordinates several multi-institutional, industry-driven research projects and manages a budget of over $750K/year. His research interests are in data mining, information retrieval, machine learning and Internet computing. He has published over 275 peer-reviewed research papers- appearing in top-level journals and proceedings- that cumulatively accord him an h-index of 35, based on citations at Google Scholar. He has served as major advisor for 29 doctoral students. Besides substantial technical expertise, Dr. Raghavan has vast experience managing interdisciplinary and multi- institutional collaborative projects. He has also directed industry-sponsored research, on projects pertaining to Neuro-imaging based dementia detection and Literature-based biomedical hypotheses generation, respectively, for GE Healthcare and Araicom Research L.L.C.

He received the IEEE International Conference on Data Mining (ICDM) 2005 Outstanding Service Award. Dr. Raghavan serves as a member of the Executive Committee of the IEEE Technical Committee on Intelligent Informatics (IEEE-TCII), the Web Intelligence Consortium (WIC) Technical Committee and the Web Intelligence and Intelligent Agent Technology Conferences’ Steering Committee. He was one of the Conference Co-Chairs of IEEE 2013 Big Data Conference, its inaugural edition. For many years of service to the community, he received the WIC 2013 Outstanding Service Award. He was a member of the Steering Committee of IEEE BigData 2014 and 2015 conferences held at Washington, D.C. and Santa Clara, CA, respectively. He is one of the Editors-in-Chief of the Web Intelligence journal, an Associate Editor of the ACM Transactions on Internet Technology and the Elsevier Journal of King Saud University - Computer and Information Sciences, and a member of the International Rough Set Society Advisory Board. He is an ACM Distinguished Scientist and served as an ACM Distinguished Lecturer from 1993 - 2006. In addition, he served as a member of the Advisory Committee of the NSF Computer and Information Science and Engineering directorate (CISE-AC) during 2008 - 2010.

Welcome! 

Machine Learning Coffee seminar "Machine Learning for Image-Based Localization"

Lecturer : 
Juho Kannala, Professor of Computer Science, Aalto University
Event type: 
Event
Doctoral dissertation
Respondent: 
Opponent: 
Custos: 
Event time: 
2017-05-15 09:15
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.

Welcome!

Machine Learning Coffee seminar "Empirical Parameterization of Exploratory Search Systems Based on Bandit Algorithms"

Lecturer : 
Dorota Glowacka, Department of Computer Science, University of Helsinki
Event type: 
Event
Doctoral dissertation
Respondent: 
Opponent: 
Custos: 
Event time: 
2017-05-08 09:15 to 10:00
Place: 
seminar room Exactum D123, Kumpula
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.

Welcome!

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