Postdoctoral Position in Machine Learning at INRIA Lille – Team SequeL

Applications are invited for a Postdoctoral position on the general area of “Sequential Decision-making in Online Marketing” at INRIA Lille – Team SequeL. Below is the detail of this call.

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Title: Sequential Decision-making in Online Marketing: Optimizing the Lifetime Value of Customers

Keywords: sequential decision-making, reinforcement learning, online marketing and advertising, exploration/exploitation dilemma, bandit algorithms, adaptive resource allocation, regret minimization, optimization

Research Program:

The candidate is expected to conduct research on both theoretical and applied aspects of the problem of “Sequential Decision-making in Online Marketing” and related problems (see the description below), collaborate with researchers and Ph.D. students at INRIA and outside, and publish the results of her/his research in conferences and journals. The candidate will work with Mohammad Ghavamzadeh (http://chercheurs.lille.inria.fr/~ghavamza) and other researchers at Team SequeL (https://sequel.lille.inria.fr).

With the growth of online marketing, customers visit websites on a regular basis (sometimes daily in the case of banking, e-commerce, and media websites), and at each visit a stream of interactions occurs between the company (promotion, advertisement, email) and a customer (purchase, click on an ad, signing up for a newsletter). This creates many opportunities for a company to reach a customer and make decisions to optimize its objective function (revenue, customer satisfaction, etc.). Today, these marketing decisions are mainly made in a myopic way (mainly using contextual bandit algorithms) without taking into account the lifetime value of the customer. This myopic approach assumes that a decision made by the company does not affect the customer’s future interactions with the company. However, in many applications the sequential nature of the problem is significant (having a long-term relation with customers is important for the company), and thus, myopic decisions have po!
or performance in these problems. This creates an opportunity for reinforcement learning (RL) techniques to play a significant role in this emerging field.

The objective of this research program is to answer fundamental questions related to the use of myopic (contextual bandit algorithms) and non-myopic (sequential decision-making and RL algorithms) decision-making methods in the growing field of online marketing. Questions such as

– Feature selection and dealing with high dimensional data: discovering the right representation for the problem at hand and dealing with the size and dimensionality of the data are among the most important questions in these applications. The size and dimensionality of the data create difficulties for the standard sequential decision-making algorithms. This is closely related to another growing research direction: sequential decision-making with big data.

– Off-policy evaluation: how to evaluate a policy learned from a batch of historical data generated with a different policy (usually the company’s policy) with minimum interaction with the real-world environment. Running a strategy on the real system can be costly: it usually takes a long time to have a reasonable evaluation of its quality and more importantly is the risk of a big loss in case the strategy is not good.

– Discovering patterns in the sales funnel in order to find strategies to direct more customers through the funnel to the final sale.

– Dealing with the non-stationarity, mainly caused by change in the preferences of the customer, arrival and departure of customers, evolution of webpage contents, etc., and delayed feedback (significant delay between an action taken by the marketer and its effects on the customer) in the online marketing applications.

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Requirements:

The applicant will have a Ph.D. degree (by the starting date of the postdoctoral position) in Computer Science, Statistics, or related fields, with background in reinforcement learning, bandit algorithms, statistics, and optimization. Programming skills will be considered as a plus. The working language of the group is English, so the candidate is expected to have good communication skills in English.

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About INRIA and Team SequeL:

SequeL (https://sequel.lille.inria.fr) is one of the most dynamic teams at INRIA (http://www.inria.fr), with over 25 researchers and Ph.D. students working on several aspects of machine learning from theory to application, including statistical learning, reinforcement learning, and sequential decision-making. The SequeL team is involved in national and European research projects and has collaboration with international research groups. This allows the postdoctoral candidate to collaborate with leading researchers in the field at top universities in Europe and North America such as University College of London (UCL), University of Alberta, and McGill University. Moreover, in this project there is the possibility of close collaboration with an online marketing company in the US. Lille is the capital of the north of France, a metropolis with over one million inhabitants, and with excellent train connection to Brussels (30min), Paris (1h) and London (1h30).

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Benefits:

– Duration: 16 months – starting date of the contract : November 1, 2013
– Salary: 2620.84 Euros gross/month monthly salary
– Monthly salary after taxes: around 2138 Euros (medical insurance included)
– Possibility of French courses
– Help for housing
– Participation for transportation
– Scientific Resident card and help for husband/wife visa

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Application Submission:

The application should include a brief description of the applicant’s research interests and past experience, plus a CV that contains her/his degrees, GPAs, relevant publications, name and contact information of up to three references, and other relevant documents. Please send your application to mohammad.ghavamzadeh@inria.fr. The deadline for the application is April 15 but the applicants are encouraged to submit their application as soon as possible.

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This call has also been posted on

1) my webpage at

http://chercheurs.lille.inria.fr/~ghavamza/postdoc-ad-2013.html

2) the INRIA website at:

http://www.inria.fr/institut/recrutement-metiers/offres/post-doctorat/campagne-2013/%28view%29/details.html?id=PGTFK026203F3VBQB6G68LONZ&LOV5=4508&LG=FR&Resultsperpage=20&nPostingID=7352&nPostingTargetID=12788&option=52&sort=DESC&nDepartmentID=19

PhD Studentship in statistical machine translation – Xerox & University of Grenoble

PhD student, Statistical Machine Translation in Grenoble (France)

The Machine Learning for Document Access and Translation group of the Xerox Research Centre Europe (XRCE) conducts research in Statistical Machine Translation and Information Retrieval, Categorization and Clustering using advanced machine learning methods.

We are opening a position for a PhD studentship in the field of Statistical Machine Translation. The ideal candidate has a strong undergraduate curriculum in SMT, or otherwise in statistical NLP, and is passionate about devising, implementing and evaluating effective solutions to challenging language problems.

This PhD will be co-supervised with the Laboratory of Informatics of Grenoble (LIG) which conducts research in Machine Translation and Natural Language Processing. The candidate will register as a graduate PhD student at University of Grenoble.

Required experience and qualifications:

– Master-level degree in computer science, mathematics, statistics, or computational linguistics with focus on Machine Translation, statistical NLP, or machine Learning.
– A good command of English, as well as open-mindedness and the will to collaborate within a team.

Additional desirable features:

– A publication record
– Evidence of implementing systems.

Preferred starting date: ASAP

Application instructions

Inquiries can be sent to
Nicola.Cancedda at xrce.xerox.com
Laurent.Besacier at imag.fr

XRCE is a highly innovative place and we strongly encourage publication and interaction with the scientific community.

Postdoctoral Research Fellow on “Low-complexity source separation algorithms”, CVSSP, University of Surrey, U.K. (Closing on March 17th, 2013)

Research Fellow

Low-complexity source separation algorithms

Centre for Vision Speech and Signal Processing (CVSSP)
University of Surrey, United Kingdom
Salary: £29,541-£30,424 per annum
(Subject to qualifications and experience)
Applications are invited for a three-year postdoctoral research fellow position available at CVSSP, starting on Monday, April 1, 2013, to work on a project entitled “Signal Processing Solutions for a Networked Battlespace”, funded by the Engineering and Physical Sciences Research Council (EPSRC) and Defence Science and Technology Laboratory (Dstl), as part of the Ministry of Defence (MoD) University Defence Research Centre (UDRC) Scheme in signal processing. This project will be undertaken by a unique consortium of academic experts from Loughborough, Surrey, Strathclyde and Cardiff (LSSC) Universities together with six industrial project partners QinetiQ, Selex-Galileo, Thales, Texas Instruments, PrismTech and Steepest Ascent. The overall aim of the project is to provide fundamental signal processing solutions to enable intelligent and robust processing of the very large amount of multi-sensor data acquired from various networked communications and weapons platforms, in order to retain military advantage and mitigate smart adversaries who present multiple threats within an anarchic and extended operating area (battlespace). The research fellow will be expected to work in close collaboration with our academic and industrial partners together with members of the lead consortium based at Edinburgh and Heriott Watt Universities.
The prospective research fellow will be expected to develop low-complexity robust algorithms for underdetermined, convolutive signal separation, broadband distributed beamforming. The work will be facilitated by low-rank and sparse representations, and directed toward fast implementations. He/she will develop robust source separation algorithms in highly dense signal environments, with the presence of uncertainties, such as weak signals and the unknown number of targets.
Successful applicants will join the CVSSP, a leading research group in sensory (visual and auditory) data analysis and interpretation, and will work closely with Dr Wenwu Wang, Prof Josef Kittler and Dr Philip Jackson. CVSSP is one of the largest UK research groups in machine vision and audition with more than 120 researchers, with core expertise in Signal Processing, Image and Video Processing, Pattern Recognition, Computer Vision, Machine Learning and Artificial Intelligence, Computer Graphics and Human Computer Interaction. CVSSP forms part of the Department of Electronic Engineering, which received one of the highest ratings (joint second position across the UK) in the last research quality assessment, i.e. 2008 RAE, with 70% of its research classified as either 4* (“world-leading”) or 3* (“internationally excellent”).

Applicants should have a PhD degree or equivalent in electrical and electronic engineering, computer science, mathematical science, statistics, physics, or related disciplines. Applicants should be able to demonstrate excellent mathematical, analytical and computer programming skills. Advantages will be given to the applicants who have experience in sparse representations, blind source separation, low-rank linear algebra, and/or machine learning.
For informal inquiries about the position, please contact Dr Wenwu Wang (w.wang@surrey.ac.uk).
For an application pack and to apply on-line please go to our website: http://www.surrey.ac.uk/vacancies. If you are unable to apply on-line please contact Mr Peter Li, HR Assistant on Tel: +44 (0) 1483 683419 or email: k.li@surrey.ac.uk
The closing date for applications is Sunday March 17th, 2013.

For further information about the University of Surrey, please visit www.surrey.ac.uk.

We acknowledge, understand and embrace cultural diversity.

ICML ’13 Workshop – Call for papers

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ICML ’13 Workshop – Call for papers:
Prediction with Sequential Models
June 21st or 22nd, Atlanta, GA, USA
*** Website: http://psm.lal.in2p3.fr/ ***

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Supervised and unsupervised function learning is a vast domain with a plethora of standard algorithmic solutions. Most of these methods learn a monolithic predictor function in the sense that each test instance is processed in a single-step, atomic process. In contrast, some recent studies have proposed a different paradigm in which *prediction is reformulated as a sequential decision process* and *learning the predictor function corresponds to solving a dynamic control problem*. These new approaches bridge “classical” supervised and unsupervised learning problems with the fields of control theory and reinforcement learning (RL), and raise interesting questions on different domains ranging from reinforcement learning to representation learning.

This workshop aims at gathering the various machine learning sub-communities that have worked around the subject and discuss the aforementioned issues. The topics of interest include, but are not limited to:
• Generic topics:
– Classification, ranking,
– Budgeted and/or cost-sensitive classification
– Structured prediction
– Sparse coding with sequential models
– Feature selection.
• Reinforcement learning applied to learning sequential functions:
– RL with many discrete actions
– RL in high-dimensional spaces
– Inverse RL
• Applications:
– Real-time detection and classification
– Text/image classification and information extraction
– Trigger design in high-energy particle physics
– Web-page ranking
– Medical diagnosis
Program Committee
• Francis Bach – Laboratoire d’Informatique de l’Ecole Normale Superieure – INRIA-Sierra – France
• Aaron Courville – University of Montreal – Canada
• Jason Eisner – Johns Hopkins University – USA
• Damien Ernst – University of Liege – Belgium
• Hugo Larochelle – University of Sherbrooke – Canada
• Francis Maes – Catholic University of Leuven – Belgium
• Rémi Munos – INRIA Sequel – France
• Philippe Preux – University Lille 3 – INRIA Sequel – France
• Thomas Rückstieß – Technische Universitat Munchen – Germany
• Csaba Szepesvári – University of Alberta – Canada
• Kilian Weinberger – Washington University – USA

Organizers
• Djalel Benbouzid (Paris Sud – CNRS)
• Ludovic Denoyer (UPMC – LIP6)
• Gabriel Dulac-Arnold (UPMC – LIP6)
• Patrick Gallinari (UPMC – LIP6)
• Balàzs Kégl (Paris Sud – CNRS)
• Michèle Sébag (Paris Sud – CNRS)

PhD studentship in evolutionary theory at Royal Holloway, U. of London

Title
Intra-genomic Conflict and Medical Disorders

Supervisors
Dr Francisco Ubeda and Prof. Vincent A.A. Jansen

Research Outline
Intra-genomic conflict defies the logic of natural selection: why would natural selection favor any gene whose expression reduces the fitness of its host? However intra-genomic conflict has left its signature in many molecular mechanisms. A paradigmatic example of evolution driven by intra-genomic conflict is the case of genomic imprinting where conflict between paternally inherited and maternally inherited genes in the same individual results in silencing of one gene but not the other (1).

Recently, genomic imprinting (and intra-genomic conflict in general) has been linked to several diseases (2). For example, deletion of the PWS/AS cluster of imprinted genes causes Prader-Willi syndrome (PWS) when the deletion is paternally inherited but Angelman syndrome (AS) when it is maternally inherited (3). The clinical phenotype, regarding appetite and activity levels, of children suffering from these syndromes is the reverse: poor sucking and low weight in children with PWS but insatiable appetite and obesity in children with AS (3).

This intriguing reversal of the clinical phenotype of a deletion is best explained in the light of conflict between genes with different parental origin. In particular, it can be explained when paternally inherited copies favor a greater allocation of maternal resources to offspring than the maternally inherited copy does (4). We are interested in further exploring the role of intra-genomic conflict in disease. Can we predict the risk of developing diseases caused by genes in conflict? Can we suggest epigenetic modifications that may palliate some symptoms?

In this project we will formulate mathematical models for the evolution of intra-genomic conflict and make specific predictions about the outcomes. We will test the predictions of our models against the medical literature. This research will require a trans-disciplinary approach that uses mathematical and computational models to synthesize the fields of molecular biology, genetics, medicine, evolutionary biology, and behavioral ecology. We hope to apply this approach to understand the evolution of genomic imprinting, sex-determination, and disease virulence among others.

This project is suitable for candidates with some background or experience in mathematical modeling or simulation at undergraduate level. We are looking for candidates, either with a background in the life sciences, and experience in mathematical or simulation modeling, or for candidates with a background in a quantitative subject (e.g. mathematics, computer science, physics) and an affinity for research in ecology and evolution.

The studentship will be held in the School of Biological Sciences of Royal Holloway, University of London. The research in the School covers the breadth of biology and hosts a number of theoretical researchers. The School was ranked among the best UK Bioscience Departments in the last research assessment (RAE 2008). The scenic Royal Holloway campus is on the outskirts of London
www.telegraph.co.uk/education/expateducation/9480575/Beautiful-universities-around-the-world.html?frame=2312131

The studentship has a maintenance allowance of £15726 per annum for 3 years and a UK/EU tuition fee waiver . We expect candidates to have a 2.1 or first class degree (or equivalent if not a UK degree).

Apply before the 4th of March following the link http://www.rhul.ac.uk/biologicalsciences/prospectivestudents/postgraduateresearch/phdstudentships2013v2.aspx ; get in touch with Tracey Jeffries (Tracey.Jeffries@rhul.ac.uk) for any application queries. If you are interested in applying please contact us informally before the deadline at F.Ubeda@rhul.ac.uk or Vincent.jansen@rhul.ac.uk

Joint PhD position in computer vision at the University of Edinburgh and INRIA Grenoble

The LEAR research group at INRIA Grenoble and the CALVIN research group at the University of Edinburgh are looking for a joint PhD student. The candidate will be jointly supervised by Dr. Vittorio Ferrari and Dr. Cordelia Schmid. The candidate will spend time at both institutions.

The topic of the PhD is learning object classes from consumer videos. While traditional approaches learn object classes from annotated still images, in this project we want to exploit video as another source of data. A video shows a range of viewpoints, poses and lighting conditions, which are necessary for building richer models. Moreover, the temporal continuity facilitates the task of segmenting the object from its background, reducing the need for manual annotation. The PhD student will explore these aspects in order to advance the state-of-the-art in weakly supervised learning of object class models.

Starting date: as soon as possible

Applicants must have:
* Master degree in Computer Science or Mathematics
* Excellent programming skills (the project is in Matlab and C++)
* Solid mathematics foundations (especially algebra and statistics)
* Highly motivated
* Fluent in English, both written and spoken
* UK or EU nationality is mandatory
* Experience in computer vision and/or machine learning is a plus

The project will be funded by a starting and an advanced ERC grant hold by the respective supervisors. This offers a unique opportunity for the candidate to develop within a stimulating environment involving two prestigious institutions.

The School of Informatics at Edinburgh is one of the top-ranked departments of Computer Science in Europe and offers an exciting research environment. Edinburgh is a beautiful historic city with a high quality of life. For an overview of current research activities of the CALVIN team, please visit http://groups.inf.ed.ac.uk/calvin/.

INRIA is one of the top-ranked computer science institutes in Europe and the LEAR team at INRIA Grenoble has a track record of computer vision research (http://lear.inrialpes.fr).
Grenoble is locate in the French Alpes and offers excellent outdoor facilities.

Location:
This is a joint project between INRIA Grenoble and the University of Edinburgh.
The candidate will be required to spend time in both institutions.

Please send applications to the email address below, including:
* complete CV
* title and abstract of master thesis
* complete grades for all exams passed during both the bachelor and master
* the name and email address of one reference (preferably your master thesis supervisor)
* if you already have research experience, please include a publication list

Contacts:
Dr. Cordelia Schmid, schmid@inrialpes.fr
Dr. Vittorio Ferrari, vferrari@staffmail.ed.ac.uk

CFP: IJCAI Workshop on Machine Learning for Interactive Systems (abstract submission: April 13)

CALL FOR PAPERS:

IJCAI Workshop on Machine Learning for Interactive Systems (MLIS’13):
Bridging the Gap between Perception, Action and Communication

August 3-4, 2013, Beijing, China

http://mlis-workshop.org/2013

Intelligent systems or robots that interact with their environment
by perceiving, acting or communicating often face a challenge in
how to bring these different concepts together. One of the main
reasons for this challenge is the fact that the core concepts
in perception, action and communication are typically studied by
different communities: the computer vision, robotics and natural
language processing communities, among others, without much
interchange between them. As machine learning lies at the core of
these communities, it can act as a unifying factor in bringing
the communities closer together. Unifying these communities is
highly important for understanding how state-of-the-art
approaches from different disciplines can be combined
(and applied) to form generally interactive intelligent systems.

The goal of this workshop is to bring researchers from multiple
disciplines together who are in one way or another affected by
the gap between action, perception and communication that
typically exists for interactive systems or robots.
Topics of interest include, but are not limited to:

Machine Learning:
– Reinforcement Learning
– Supervised Learning
– Unsupervised Learning
– Semi-Supervised Learning
– Active Learning
– Learning from human feedback
– Learning from teaching, tutoring, instruction and demonstration
– Combinations or generalisations of the above

Interactive Systems:
– (Socially) Interactive Robotics
– Embodied Virtual Agents
– Avatars
– Multimodal systems
– Cognitive (robotics) architectures

Types of Communication:
– System interacting with a single human user
– System interacting with multiple human users
– System interacting with the environment
– System interacting with other machines

Example applications could include: (1) a robot may learn to
coordinate its speech with its actions, taking into account
visual feedback during their execution; (2) an autonomous car
may learn to coordinate its acceleration and steering behaviours
depending on observations of obstacles; (3) a team of robots
playing soccer may learn to coordinate their ball kicks depending
on the dynamic locations of their opponents; (4) a sensorimotor
system may learn to drive a wheelchair through feedback from
visual signals of the environment; (5) a mobile robot may
interactively learn from human guidance how to manipulate objects
and move through a building, based on human feedback using
language, gestures and interactive dialogue; or (6) a multimodal
smart phone can adapt its input and output modalities to
the user’s goals, workload and surroundings.

Submissions can take two forms. Long papers should not exceed
8 pages, and short (position) papers should not exceed 4 pages.
They should follow the ACM SIG proceedings format (option 1):
http://www.acm.org/sigs/publications/proceedings-templates.
All submissions should be anonymised for peer-review.

Submission link: https://www.easychair.org/conferences/?conf=mlis2013

Accepted papers will be published by ACM International Conference
Proceedings Series under ISBN 978-1-4503-2019-1. The proceedings
of MLIS’13 will be available on the ACM digital library on the day
of the workshop.

Invited Speakers:
Prof. Dr. Martin Riedmiller, University of Freiburg
Talk: “Learning Machines that Perceive, Act and Communicate”
Prof. Dr. Olivier Pietquin, Supélec, France
Title: “Inverse Reinforcement Learning for Interactive Systems”

Important Dates:
April 13, Abstract registration
April 20, Paper submission deadline
May 20, Notification of acceptance
May 30, Camera-ready deadline
August 3-4, MLIS workshop

Organising Committee:
Heriberto Cuayahuitl, Heriot-Watt University, Edinburgh, UK
Lutz Frommberger, University of Bremen, Germany
Nina Dethlefs, Heriot-Watt University, Edinburgh, UK
Martijn van Otterlo, Radboud University Nijmegen, The Netherlands

For all enquires, please mail: organizers@mlis-workshop.org

PhD position at NTNU

http://www.jobbnorge.no/job.aspx?jobid=91040

Upcoming Events

You can also view these events in the PASCAL Calendar.

IASD challenge
2 February 2013 – 2 April 2013

Vision and Sports Summer School
Zurich, Switzerland
16 – 20 August 2010

First workshop on Automated Knowledge Base Construction Workshop
Grenoble, France
17 – 19 May 2010

Active Learning and Experimental Design Workshop
Sardinia, Italy
16 May 2010

French Spring School in Machine Learning
Baie de Somme, France
2 – 7 May 2010

9th International Workshop on Multiple Classifier Systems
Cairo, Egypt
7 – 9 April 2010

Learning and Inference in Computational Systems Biology workshop
Warwick, U.K.,
30 – 31 March 2010.

Foundations and New Trends of PAC Bayesian Learning
London, UK
22 – 23 March 2010

Multiple Comparisons from Theory to Practice Workshop
Berlin, Germany
15 – 16 February 2010

Kernels for Multiple Outputs and Multi-task Learning: Frequentist and Bayesian Points of View Workshop
Whistler, Canada
12 December 2009

Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing Workshop
Whistler, Canada
12 December 2009

Learning from Multiple Sources with Applications to Robotics Workshop
Whistler, Canada
12 December 2009

Connectivity Inference in Neuroimaging Workshop
Whistler, Canada
12 December 2009

Bayesian Nonparametrics Workshop
Whistler, Canada
12 December 2009

Approximate Learning of Large Scale Graphical Models: Theory and Applications Workshop
Whistler, Canada
12 December 2009

Machine Learning in Computational Biology Workshop
Whistler, Canada
11 December 2009

Applications of Topic Models: Text and Beyond Workshop
Whistler, Canada
11 December 2009

Clustering: Science or Art? Towards Principled Approaches Workshop
Whistler, Canada
11 December 2009

Probabilistic Approaches for Robotics and Control Workshop
Whistler, Canada
11 December 2009

Grammar Induction, Representation of Language and Language Learning Workshop
Whistler, Canada
11 December 2009

Large-Scale Machine Learning: Parallelism and Massive Datasets Workshop
Whistler, Canada
11 December 2009

Assistive Machine Learning for People with disabilities Mini-Symposium
Whistler, Canada
10 December 2009

Causality and Time Series Mini-Symposium
Whistler, Canada
10 December 2009

Modelling Cognitive Behaviour Workshop
Bristol, U.K.
5 November 2009

Workshop on Spatiotemporal Modelling
Edinburgh, UK
12 – 14 October 2009

Intelligent Analysis and Processing of Web News Content Workshop
Milan, Italy
15 September 2009

SMART PASCAL Industrial Outreach Meeting
Bled, Slovenia
7 September 2009

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Conference
Bled, Slovenia
7 – 11 September 2009

Pattern Recognition in Bioinformatics Workshop
Sheffield, UK
7 – 9 September 2009

Third International Workshop on Machine Learning in Systems Biology Workshop
Ljubljana, Slovenia
5 – 16 September 2009

Vision and Sports Summer School 2009
Zurich, Switzerland
17 – 21 August 2009

Advances in Machine Learning for Computational Finance Workshop
London, UK
20 – 21 July 2009

International Workshop on Complex Systems and Networks
University of Bristol
20 – 22 July 2009

Machine Learning for Aerospace Workshop
Marseille, France
3 – 4 July 2009

SIM 2009 (SRL+ILP+MLG 2009) Workshop
Leuven, Belgium
2 – 4 July 2009

Regression in Robotics – Approaches and Applications Workshop
Seattle, USA
28 June 2009

ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery Workshop
Paris, France
28 June 2009

NUMML 2009 Numerical Mathematics in Machine Learning Workshop
Montreal, Canada
18 June 2009

On-line Learning with Limited Feedback Workshop
Montreal, Canada
18 June 2009

The 6th Annual European Semantic Web Conference (ESWC2009)
Heraklion, Greece
31 May – 4 June 2009

SMART Dissemination Workshop
Barcelona, Spain
13 May 2009

Sparsity in Machine Learning and Statistics Workshop
Cumberland Lodge, UK
1 – 3 April 2009

Learning and Inference in Computational and Systems Biology Workshop
London, UK
1 – 2 April 2009

Computational Linguistic Aspects of Grammatical Inference Workshop
Athens, Greece
30 March 2009

Learning from Multiple Sources Workshop
Whistler, Vancouver, Canada
13 December 2008

Kernel Learning: Automatic Selection of Optimal Kernels Workshop
Whistler, Canada
13 December 2008

Learning over Empirical Hypothesis Spaces Workshop
Whistler, Canada
13 December 2008

Optimization for Machine Learning
Whistler, Canada
13 December 2008

Machine Learning in Computational Biology
Whistler, Canada
13 December 2008

Structured Input – Structured Output
Whistler, Canada
13 December 2008

Algebraic and Combinatorial Methods in Machine Learning
Whistler, Canada
13 December 2008

Causality: objectives and assessment Workshop
Whistler, Canada
13 December 2008

NIPS Workshop on Machine Learning Open Source Software Workshop
Whistler,Vancouver
13 December 2008

Mini Symposia: Algebraic Methods in Machine Learning
Vancouver, Canada
11 December 2008

Workshop on Sparsity and Inverse Problems in Statistical Theory and Econometrics Workshop
Berlin, Germany
5 – 6 December 2008

Ph.D. Position in Machine Learning at INRIA Lille – Team SequeL

Applications are invited for a Ph.D. studentship on the general area of “Sequential Decision-making under Uncertainty” at INRIA Lille – Team SequeL. Below is the detail of this call.

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Title: Sequential Decision-Making with Big Data

Keywords: sequential decision-making, reinforcement learning, learning and planning in MDPs and POMDPs, exploration/exploitation dilemma, bandit algorithms, adaptive resource allocation, regret minimization, optimization

Research Program:

The candidate is expected to conduct research on both theoretical and applied aspects of the problem of “Sequential Decision-making with Big Data” (see the description below), collaborate with researchers and Ph.D. students at INRIA and outside, and publish the results of her/his research in conferences and journals. The candidate will work with Mohammad Ghavamzadeh (http://chercheurs.lille.inria.fr/~ghavamza) and other researchers at Team SequeL (https://sequel.lille.inria.fr).

This Ph.D. program is focused on the problem of dealing with big data and limited resources in sequential decision-making under uncertainty.

– Big Data: Sequential decision-making applications that need to handle Big Data can be classified into three categories, which define related research problems.

1) Very large number of data points: This is a typical case in time series data that are fairly simple, but sampled at high frequency, such as user clicks on the web and financial data. In this scenario, the most important issue is the computational cost.
2) Very high-dimensional input space: This case arises when each data point consists of a lot of measurements, leading to a curse of dimensionality. Examples are customer information in online marketing problems and problems with complex sensors (such as Kinect cameras). The best way to solve this type of problem is to leverage intrinsic regularities (e.g., smoothness, sparsity, dependencies in features) to reduce the dimensionality.
3) Partially observable input space: Often, the observed input measurements do not have sufficient information for accurate decision-making, but one can leverage the history of the observations to improve the situation. This often requires projecting the problem into a high-dimensional representation.

– Limited Resources: In many real-world sequential decision-making applications we only have a limited budget of resources such as number of samples or access to a system’s simulator etc. When the available resources (sample or computation) are limited and/or access to more resources is costly, it would be absolutely necessary to allocate the available resources (or ask for more resources) efficiently in order to find good strategies. The problem of adaptive resource allocation has been studied in bandits, planning, and stochastic optimization, but there still exist many open problems and challenges in this area that require further investigation.

– Other Related Problems that arise in real-world applications of sequential decision-making: (i) how to evaluate a policy learned from a batch of historical data (generated with a different policy) with minimum interaction with the real-world environment, (ii) learning risk-sensitive and robust strategies, (iii) learning interpretable policies (i.e., policies that are understandable by experts of the problem at hand, who do not necessarily know much about machine learning, like medical doctors or financial managers) etc.

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Requirements:

The applicant will have a Master’s (or equivalent) degree in Computer Science, Statistics, or related fields, with background in reinforcement learning, bandit algorithms, statistics, and optimization. Programming skills will be considered as a plus. The working language of the group is English, so the candidate is expected to have good communication skills in English.

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About INRIA and Team SequeL:

SequeL (https://sequel.lille.inria.fr) is one of the most dynamic teams at INRIA (http://www.inria.fr), with over 25 researchers and Ph.D. students working on several aspects of machine learning from theory to application, including statistical learning, reinforcement learning, and sequential decision-making. The SequeL team is involved in national and European research projects and has collaboration with international research groups. This allows the Ph.D. candidate to collaborate with leading researchers in the field at top universities in Europe and North America such as University College of London (UCL), University of Alberta, and McGill University. Lille is the capital of the north of France, a metropolis with over one million inhabitants, and with excellent train connection to Brussels (30min), Paris (1h) and London (1h30).

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Benefits:

– Duration: 36 months – starting date of the contract : October 2013, 15th
– Salary: 1957.54 Euros the first two years and 2058.84 Euros the third year
– Monthly salary after taxes: around 1597.11 Euros the first two years and 1679,76 Euros the 3rd year (benefits included)
– Possibility of French courses
– Help for housing
– Participation for transportation
– Scientific Resident card and help for husband/wife visa

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Application Submission:

The application should include a brief description of the applicant’s research interests and past experience, plus a CV that contains her/his degrees, GPAs, relevant publications, name and contact information of up to three references, and other relevant documents. Please send your application to mohammad.ghavamzadeh@inria.fr. The deadline for the application is April 15 but the applicants are encouraged to submit their application as soon as possible.

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This call has also been posted on

1) my webpage at

http://chercheurs.lille.inria.fr/~ghavamza/phd-ad-2013.html

2) the INRIA website at:

http://www.inria.fr/institut/recrutement-metiers/offres/theses/campagne-2013/%28view%29/details.html?id=PGTFK026203F3VBQB6G68LONZ&LOV5=4509&LG=FR&Resultsperpage=20&nPostingID=7222&nPostingTargetID=12647&option=52&sort=DESC&nDepartmentID=10