Welcome to the homepage of the DIG seminar, which is the regular seminar of the DIG team of LTCI at Télécom ParisTech. The seminar features talks by members of the team and guests from other research groups, as well as discussions on topics of relevance to the team. Attendance is generally open to the public, feel free to contact us if you are interested in coming. Talks are held at Télécom ParisTech, 46 rue Barrault, Paris, France, métro Corvisart. You can contact Antoine Amarilli for any inquiry about the seminar.
Abstract: As humans we interpret and react to the world around us in terms of narratives. At a basic level, a narrative is comprised of principal actors and entities, their interactions, and finally the decisions they make to reinforce and protect their interests. The primary question we address in this talk is whether a computer can automatically distill and create such narrative maps from millions of posts and discussions that happen in the online world. How much and which parts of the underlying narratives can be extracted via unsupervised statistical methods, and how much "humanness" needs to becoded into a computer? We provide a framework that uses statistical techniques to generate automated summaries, and show that when augmented with a small-size dictionary that encodes "humanness," the framework can generate effective narratives from a number of domains. We will present several sets of empirical results where millions of posts are processed to generate story graphs and plots of the underlying discussions.
Biography: Vwani Roychowdhury is a Professor of Electrical and Computer Engineering at University of California, Los Angeles (UCLA). He specializes in interdisciplinary work that deal with the modeling and design of information and computing systems, ranging from the physical, biological and engineered systems. He has done pioneering work in Quantum Computing, Nanoelectronics, Peer-to-Peer (P2P), social and complex networks, machine learning, text mining, artificial neural networks, computer vision, and Internet-Scale data processing. He has published more than 200 peer reviewed journal and conference papers, and co-authored several books. He has also cofounded several silicon valley startups, including www.netseer.com and www.stieleeye.com.
Abstract: Much of scientific research involves the generation and testing of hypotheses that can facilitate the development of accurate models for a system. In machine learning the automated building of accurate models is desired. However traditional machine learning often assumes that the underlying models are static and unchanging over time. In reality there are many applications that analyse data streams where the underlying model or system changes over time. This may be caused by changes in the conditions of the system, or a fundamental change in how the system behaves. In this talk, I will present a change detector called SEED, and how we capture stream volatility. We coin the term stream volatility, to describe the rate of changes in a stream. A stream has a high volatility if changes are detected frequently and has a low volatility if changes are detected infrequently. I will also present a drift prediction algorithm to predict the location of future drift points based on historical drift trends which we model as transitions between stream volatility patterns. Our method uses a probabilistic network to learn drift trends and is independent of the drift detection technique. I will then present a meta-learner, Concept Profiling Framework (CPF) that uses a concept drift detector and a collection of classification models to perform effective classification on data streams with recurrent concept drifts, through relating models by similarity of their classifying behaviour.
Biography: Yun Sing Koh is a Senior Lecturer at the Department of Computer Science, The University of Auckland, New Zealand. She completed her PhD at the Department of Computer Science, University of Otago, New Zealand in 2007. Her current research interest is in the area of data mining and machine learning, specifically data stream mining and pattern mining.
During this time, the DBWeb seminar was held as part of the IC2 group seminar. These seminars are listed on the IC2 seminar Web page.