| Monday 24th September |
| Padhraic Smyth, University of California, Irvine |
| Analyzing Text and Social Network Data with Probabilistic Models |
| Tuesday 25th September |
| Pieter Abbeel, University of California, Berkeley |
| Machine Learning for Robotics |
| Wednesday 26th September |
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| Cancelled |
| Thursday 27th September |
| Daniel Keim, University of Konstanz |
| Solving Problems with Visual Analytics: Challenges and Applications |
| Friday 28th September |
| Luc De Raedt, University of Leuven |
| Declarative modeling for machine learning and data mining |
Pieter Abbeel, University of California, Berkeley
Machine Learning for Robotics
Robots are typically far less capable in autonomous mode than in tele-operated mode. The few exceptions tend to stem from long days (and more often weeks, or even years) of expert engineering for a specific robot and its operating environment. Current control methodology is quite slow and labor intensive.
I believe advances in machine learning have the potential to revolutionize robotics. In this talk, I will present new machine learning techniques we have developed that are tailored to robotics. I will describe in depth “Apprenticeship learning,” a new approach to high-performance robot control based on learning for control from ensembles of expert human demonstrations. Our initial work in apprenticeship learning has enabled the most advanced helicopter aerobatics to-date, including maneuvers such as chaos, tic-tocs, and auto-rotation landings which only exceptional expert human pilots can fly. Our most recent work in apprenticeship learning is providing traction on learning to perform challenging robotic manipulation tasks, such as knot-tying. I will also briefly highlight three other machine learning for robotics developments: Inverse reinforcement learning and its application to quadruped locomotion, Safe exploration in reinforcement learning which enables robots to learn on their own, and Learning for perception with application to robotic laundry.
Bio
Pieter Abbeel received a BS/MS in Electrical Engineering from KU Leuven (Belgium) and received his Ph.D. degree in Computer Science from Stanford University in 2008. He joined the faculty at UC Berkeley in Fall 2008, with an appointment in the Department of Electrical Engineering and Computer Sciences. He has won various awards, including best paper awards at ICML and ICRA, the Sloan Fellowship, the Air Force Office of Scientific Research Young Investigator Program (AFOSR-YIP) award, the Okawa Foundation award, the 2011′s TR35, and the IEEE Robotics and Automation Society (RAS) Early Career Award. He has developed apprenticeship learning algorithms which have enabled advanced helicopter aerobatics, including maneuvers such as tic-tocs, chaos and auto-rotation, which only exceptional human pilots can perform. His group has also enabled the first end-to-end completion of reliably picking up a crumpled laundry article and folding it. His work has been featured in many popular press outlets, including BBC, New York Times, MIT Technology Review, Discovery Channel, SmartPlanet and Wired. His current research focuses on robotics and machine learning with a particular focus on challenges in personal robotics, surgical robotics and connectomics.
Luc De Raedt, University of Leuven
Declarative modeling for machine learning and data mining
Despite the popularity of machine learning and data mining today, it remains challenging to develop applications and software that incorporates machine learning or data mining techniques. This is because machine learning and data mining have focussed on developing high-performance algorithms for solving particular tasks rather than on developing general principles and techniques.
I propose to alleviate these problems by applying the constraint programming methodology to machine learning and data mining and to specify machine learning and data mining problems as constraint satisfaction and optimization problems. What is essential is that the user be provided with a way to declaratively specify what the machine learning or data mining problem is rather than having to outline how that solution needs to be computed. This corresponds to a model + solver-based approach to machine learning and data mining, in which the user specifies the problem in a high level modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. This should be much easier for the user than having to implement or adapt an algorithm that computes a particular solution to a specific problem.
Throughout the talk, I shall use illustrations from our work on constraint programming for itemset mining and probabilistic programming.
Bio
Luc De Raedt is a full professor (of research) at the University of Leuven (KU Leuven) in the Department of Computer Science and a former chair of Machine Learning at the Albert-Ludwigs-University in Freiburg.
Luc De Raedt has been working in the areas of artificial intelligence and computer science, especially on computational logic, machine learning and data mining, probabilistic reasoning and constraint programming and their applications in bio- and chemoinformatics, vision and robotics, natural language processing, and engineering. His work has typically crossed boundaries between different research areas, often working towards an integration of their principles. He is well-known for his early work on inductive logic programming (combining logic with learning). Since 2000, he has been working towards a further integration of logical and relational learning with probabilistic reasoning (statistical relational learning and probabilistic programming) and on inductive querying in databases. During the last three years he has been fascinated by the possibility of combining constraint programming principles with data mining and machine learning. He is currently coordinating a European IST FET project in this area (ICON — Inductive Constraint Programming) and is the program chair of the 20th European Conference on Artificial Intelligence (Montpellier, 2012). He was a program co-chair of ICML 2005 and ECML/PKDD 2001.
, Google Research
Machine learning methods for music discovery and recommendation – “Google Keynote talk”
CANCELLED
Unfortunately Douglas Eck had to cancel his talk due to personal reasons.
Daniel Keim, University of Konstanz
Solving Problems with Visual Analytics: Challenges and Applications
Never before in history data is generated and collected at such high volumes as it is today. As the volumes of data available to business people, scientists, and the public increase, their effective use becomes more challenging. Keeping up to date with the flood of data, using standard tools for data analysis and exploration, is fraught with difficulty. The field of visual analytics seeks to provide people with better and more effective ways to explore and understand large datasets, while also enabling them to act upon their findings immediately. Visual analytics integrates the analytic capabilities of the computer and the perceptual and intellectual abilities of the human analyst, allowing novel discoveries and empowering individuals to take control of the analytical process. Visual analytics enables unexpected insights, which may lead to beneficial and profitable innovation. The talk presents the challenges of visual analytics and exemplifies them with several application examples, which illustrate the exiting potential of current visual analysis techniques but also their limitations.
Bio
Daniel A. Keim is full professor and head of the Information Visualization and Data Analysis Research Group at the University of Konstanz, Germany. He has been actively involved in information visualization and data analysis research for about 20 years and developed a number of novel visual analysis techniques for very large data sets with applications to a wide range of application areas including financial analysis, network analysis, geo-spatial analysis, as well as text and multimedia analysis. His research resulted in two recent books “Solving problems with Visual Analytics” and “Interactive Data Visualization” which he both co-authored.
Dr. Keim has been program co-chair of the IEEE InfoVis and IEEE VAST symposia as well as the SIGKDD conference, and he is or was member of the IEEE InfoVis, IEEE VAST, and EuroVis steering committees. He is an associate editor of Palgrave’s Information Visualization Journal (since 2001) and has been an associate editor of the IEEE Transactions on Visualization and Computer Graphics (1999 – 2004), the IEEE Transactions on Knowledge and Data Engineering (2002 – 2007), and the Knowledge and Information System Journal (2006-2011). He is coordinator of the German Strategic Research Initiative (SPP) on Scalable Visual Analytics and he was the scientific coordinator of the EU Coordination Action on Visual Analytics called VisMaster.
Dr. Keim got his Ph.D. and habilitation degrees in computer science from the University of Munich. Before joining the University of Konstanz, Dr. Keim was associate professor at the University of Halle, Germany and Technology Consultant at AT&T Shannon Research Labs, NJ, USA.
Padhraic Smyth, University of California, Irvine
Analyzing Text and Social Network Data with Probabilistic Models
Exploring and understanding large text and social network data sets is of increasing interest across multiple fields, in computer science, social science, history, medicine, and more. This talk will present an overview of recent work using probabilistic latent variable models to analyze such data. Latent variable models have a long tradition in data analysis and typically hypothesize the existence of simple unobserved phenomena to explain relatively complex observed data. In the past decade there has been substantial work on extending the scope of these approaches from relatively small simple data sets to much more complex text and network data. We will discuss the basic concepts behind these developments, reviewing key ideas, recent advances, and open issues. In addition we will highlight common ideas that lie beneath the surface of different approaches including links (for example) to work in matrix factorization. The concluding part of the talk will focus more specifically on recent work with temporal social networks, specifically data in the form of time-stamped events between nodes (such as emails exchanged among individuals over time).
Bio
Padhraic Smyth is a Professor at the University of California, Irvine, in the Department of Computer Science with a joint appointment in Statistics, and is also Director of the Center for Machine Learning and Intelligent Systems at UC Irvine. His research interests include machine learning, data mining, pattern recognition, and applied statistics and he has published over 150 papers on these topics. He was a recipient of best paper awards at the 2002 and 1997 ACM SIGKDD Conferences, received the ACM SIGKDD Innovation Award in 2009, and was named a AAAI Fellow in 2010. He is co-author of Modeling the Internet and the Web: Probabilistic Methods and Algorithms (with Pierre Baldi and Paolo Frasconi in 2003), and co-author of Principles of Data Mining, MIT Press (with David Hand and Heikki Mannila in 2001).
Padhraic has served in editorial and advisory positions for journals such as the Journal of Machine Learning Research, the Journal of the American Statistical Association, and the IEEE Transactions on Knowledge and Data Engineering. While at UC Irvine he has received research funding from agencies such as NSF, NIH, IARPA, NASA, and DOE, and from companies such as Google, IBM, Yahoo!, Experian, and Microsoft. In addition to his academic research he is also active in industry consulting, working with companies such as eBay, Yahoo!, Microsoft, Oracle, Nokia, and AT&T, as well as serving as scientific advisor to local startups in Orange County. He also served as an academic advisor to Netflix for the Netflix prize competition from 2006 to 2009.
Padhraic received a first class honors degree in Electronic Engineering from National University of Ireland (Galway) in 1984, and the MSEE and PhD degrees (in 1985 and 1988 respectively) in Electrical Engineering from the California Institute of Technology. From 1988 to 1996 he was a Technical Group Leader at the Jet Propulsion Laboratory, Pasadena, and has been on the faculty at UC Irvine since 1996.
