An excursion into algebraic tools for combinatorial problems

Lecturer : 
Petteri Kaski
Event type: 
HIIT seminar
Event time: 
2011-03-07 13:15 to 14:00
Place: 
Computer Science Building, Hall T2
Description: 

 

Our next speaker for HIIT Otaniemi seminar series is Petteri Kaski from the "New Paradigms in Computing" group of the Helsinki Institute for Information Technology HIIT.
 
All ICS@Aalto researchers are also warmly welcome to attend the seminar!
 
HIIT Otaniemi Seminar, Monday March 07, 13:15
Location: Computer Science Building, Hall T2
 
Petteri Kaski
New Paradigms in Computing Group
Helsinki Institute for Information Technology HIIT
Aalto University School of Science
Department of Information and Computer Science
 
Title:
An excursion into algebraic tools for combinatorial problems
 
Abstract:
Currently the fastest known algorithms for a number of fundamental combinatorial tasks, including graph coloring, Hamilton path/cycle, k-path, k-clique, Steiner tree, counting bipartite perfect matchings, counting forests, set packing, all-terminal reliability, and so forth, rely on fundamentally algebraic tools. That is, the combinatorial problem is first reduced into an appropriate algebraic representation, and then algebraic algorithms are employed to solve the problem.
 
This talk makes a brief survey of the area and highlights some potential future directions.
 
Welcome!
 
UPCOMING TALKS OF HIIT OTANIEMI SEMINAR SERIES
----
Mar. 14, Simon Rogers
Mar. 28, Antti Oulasvirta/Teemu Roos
 
 
-- 
Mehmet Gönen
Helsinki Institute for Information Technology HIIT
Department of Information and Computer Science
Aalto University School of Science

 

Recommender systems for science: problems and prospects

Lecturer : 
Patrik Hoyer
Event type: 
HIIT seminar
Event time: 
2011-02-21 13:15 to 14:00
Place: 
Computer Science Building, Hall T2
Description: 

 

Our next speaker for HIIT Otaniemi seminar series is Patrik Hoyer from the "Neuroinformatics" group of the Helsinki Institute for Information Technology HIIT.
 
All ICS@Aalto researchers are also warmly welcome to attend the seminar!
 
HIIT Otaniemi Seminar, Monday February 21, 13:15
Location: Computer Science Building, Hall T2
 
Patrik Hoyer
Neuroinformatics Group
Helsinki Institute for Information Technology HIIT
University of Helsinki
 
Title:
Recommender systems for science: problems and prospects
 
Abstract:
Today's abundance of information and diversity of products, in a variety of domains, is making recommendation systems increasingly important in helping users and consumers find the items they are interested in. Such recommendation systems are often based on the idea of collaborative filtering, utilizing the correlations among ratings explicitly or implicitly provided by users, and systems of this kind are already in widespread use in recommending books, movies, songs, web pages, etc. However, this technology has yet to be successfully applied to the scientific research literature. I'll describe some of the problems involved, and discuss the prospects for collaborative filtering in helping scientists find high-quality articles on their topics of interest.
 
Welcome!
 
UPCOMING TALKS OF HIIT OTANIEMI SEMINAR SERIES
----
Mar. 07, Petteri Kaski
Mar. 14, Simon Rogers
 
 
-- 
Mehmet Gönen
Helsinki Institute for Information Technology HIIT
Department of Information and Computer Science
Aalto University School of Science

 

Primal-dual algorithms for distributed optimization

Lecturer : 
André Schumacher
Event type: 
HIIT seminar
Event time: 
2011-02-18 10:15 to 11:00
Place: 
Kumpula Exactum C222
Description: 
Talk announcement:
HIIT Seminar Kumpula, Friday Feb 18 10:15, Exactum C222

SPEAKER:
André Schumacher
Aalto University

TITLE:
Primal-dual algorithms for distributed optimization

ABSTRACT:
Recently, it was discovered that some protocols for computer networks
can be seen as algorithms that implicitly solve an optimization problem,
which characterizes optimal states of the distributed system.
Many of these protocols can be modeled as algorithms that simultaneously
solve a pair of primal and dual problems. The underlying "locality by
duality" principle was already exploited to design distributed algorithms
for various optimization problems.

In this talk I will highlight the algorithmic ideas that form the basis
for distributed primal-dual algorithms. I will then give an example in
the form of an algorithm for the minimum-weight dominating-set
problem that was proposed as part of an algorithmic scheme for
lifetime maximization in wireless sensor networks. This second part
of the talk is based on joint research carried out at the Department of
Information and Computer Science of Aalto University School of Science.

BIO:
André Schumacher received his doctoral degree from Aalto University
School of Science and Technology in 2010 and is currently a researcher
with the Distributed Computation group at the Department of
Information and Computer Science of Aalto University. His research
interests include distributed algorithms, approximation and online
algorithms, network optimization, as well as adhoc and sensor networks.



Welcome!
--Matti Järvisalo


HIIT SEMINAR TENTATIVE SCHEDULE Spring 2011
-------------------------------------------
Feb 18: André Schumacher
Feb 25: Jose A. Fernandes
Mar  4: Petteri Kaski
Mar 11: Esther Galbrun
Mar 18: Valentin Polishchuk
Mar 25: Esa Junttila
Apr  1: *** free ***
Apr  8: *** free ***
Apr 15: *** free ***

Local Algorithms: Past, Present, Future

Lecturer : 
Jukka Suomela
Event type: 
HIIT seminar
Event time: 
2011-02-14 13:15 to 14:00
Place: 
Computer Science Building, Hall T2
Description: 
Our next speaker for HIIT Otaniemi seminar series is Jukka Suomela from the "New Paradigms in Computing" group of the Helsinki Institute for Information Technology HIIT.
 
All ICS@Aalto researchers are also warmly welcome to attend the seminar!
 
HIIT Otaniemi Seminar, Monday February 14, 13:15
Location: Computer Science Building, Hall T2
 
Jukka Suomela
New Paradigms in Computing Group
Helsinki Institute for Information Technology HIIT
 
Title:
Local Algorithms: Past, Present, Future
 
Abstract:
A local algorithm is a distributed algorithm that runs in constant time, independently of the size of the network. Being highly scalable and fault-tolerant, such algorithms are ideal in the operation of large-scale distributed systems such as computer networks.
 
Even though the model of local algorithms is very limited, in recent years we have seen many positive results for non-trivial problems. In this talk, I will give an overview of the state-of-the-art in the field of local algorithms. I will show how the work in our group has advanced the field, and I will explore the current frontiers and fundamental open questions.
 
Welcome!
 
UPCOMING TALKS OF HIIT OTANIEMI SEMINAR SERIES
----
Feb. 21, Patrik Hoyer
Mar. 07, Petteri Kaski
Mar. 14, Simon Rogers
 
 
-- 
Mehmet Gönen
Helsinki Institute for Information Technology HIIT
Department of Information and Computer Science
Aalto University School of Science

Protein-protein network inference with regularized output and input kernel methods

Lecturer : 
Florence d'Alché Buc
Event type: 
HIIT seminar
Event time: 
2011-02-11 10:15 to 11:00
Place: 
Kumpula Exactum C222
Description: 
Talk announcement:
Combined Guest Lecture / HIIT Seminar Kumpula, Friday Feb 11 10:15, Exactum C222

SPEAKER:
Florence d'Alché Buc
IBISC, Université d'Evry-Val d'Essonne, Evry, France

TITLE:
Protein-protein network inference with regularized output and input  
kernel methods

Prediction of a physical interaction between two proteins has been  
addressed in the context of supervised learning, unsupervised  
learning and more recently, semi-supervised learning using various  
sources of information (genomic, phylogenetic, protein localization  
and function). The problem can be seen as a kernel matrix completion  
task if one defines a kernel that encodes similarity between  
proteins as nodes in a graph or alternatively, as a binary  
supervised classification task where inputs are pairs of proteins.
In this talk, we first make a review of existing works (matrix  
completion, SVM for pairs, metric learning, training set expansion),  
identifying the relevant features of each approach. Then we define  
the framework of output kernel regression (OKR) that uses the kernel  
trick in the output feature space and we develop a new family of  
methods based on Kernel Ridge Regression that benefit from the use  
of kernels both in the input feature space and the output feature  
space. The main interest of such methods is that imposing various  
regularization constraints still leads to closed form solutions. We  
show especially how such an approach allows to handle unlabeled data  
in a transductive setting of the network inference problem.
New results on simulated data and yeast data illustrate the talk.

Joint work with Céline Brouard and Marie Szafranski.


Welcome!
--Matti Järvisalo

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