October 2015 - talks - lists.cs.princeton.edu

Colloquium Speaker Jeffrey Siskind Thurs Oct 15, 12:30pm
by Nicole E. Wagenblast 07 Oct '15

07 Oct '15

Colloquium Speaker Jeffrey Siskind, from Purdue University Thursday, October 15, 12:30pm Computer Science 105 Seeing, Saying, Doing, and Learning: Integrating Computer Vision, Natural Language Processing, Robotics, and Machine Learning Through Multidirectional Inference The semantics of natural language can be grounded in perception and motor control with a unified cost function that supports multidirectional inference. I will present several instances of this approach. The first is a cost function relating sentences, video, and a lexicon. Performing inference from video and a lexicon to sentences allows it to generate sentential descriptions of video. Performing inference from sentences and a lexicon to video allows it to search a video database for clips that match a sentential query. Performing inference from sentences and video to a lexicon allows it to learn a lexicon. The second is the functional inverse of video captioning. Instead of mapping video and object detections to sentences, one can map video and sentences to object detections. This allows one to use sentential constraint on a video object codetection process to find objects without pretrained object detectors. The third is a cost function relating sentences, robotic navigation paths, and a lexicon. Performing inference from sentences and navigation paths to a lexicon allows it to learn a lexicon. Performing inference from navigation paths and a learned lexicon to sentences allows it to generate sentential descriptions of paths driven by a mobile robot. Performing inference from sentences and a learned lexicon to navigation paths allows it to plan and drive navigation paths that satisfy a sentential navigation request. Finally, one can perform object codetection on the video stream from a robot-mounted camera during navigation to satisfy sentential requests and use the collection of constraints from vision, language, and robotics to detect, localize, and label objects in the environment without any pretrained object detectors. Joint work with Andrei Barbu, Daniel Paul Barrett, Scott Alan Bronikowski, N. Siddharth, and Haonan Yu. Jeffrey M. Siskind received the B.A. degree in computer science from the Technion, Israel Institute of Technology, Haifa, in 1979, the S.M. degree in computer science from the Massachusetts Institute of Technology (M.I.T.), Cambridge, in 1989, and the Ph.D. degree in computer science from M.I.T. in 1992. He did a postdoctoral fellowship at the University of Pennsylvania Institute for Research in Cognitive Science from 1992 to 1993. He was an assistant professor at the University of Toronto Department of Computer Science from 1993 to 1995, a senior lecturer at the Technion Department of Electrical Engineering in 1996, a visiting assistant professor at the University of Vermont Department of Computer Science and Electrical Engineering from 1996 to 1997, and a research scientist at NEC Research Institute, Inc. from 1997 to 2001. He joined the Purdue University School of Electrical and Computer Engineering in 2002 where he is currently an associate professor. His research interests include computer vision, robotics, artificial intelligence, neuroscience, cognitive science, computational linguistics, child language acquisition, automatic differentiation, and programming languages and compilers.

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Colloquium Speaker: Shie Mannor, Mon, Oct 12- 12:30pm
by Nicole E. Wagenblast 06 Oct '15

06 Oct '15

Colloquium Speaker Shie Mannor, Technion, Israel Institute of Technology Monday, October 12, 12:30pm Computer Science 105 Co-hosted by the Computer Science Department and the Center for Statistics and Machine Learning Actionable big data: from data to decisions and back In many sequential decision problems all that we have is a record of historical trajectories. Building dynamic models from these trajectories and ultimately sequential decision policies may result in much uncertainty and bias. In this talk we consider the question of how to create control policies from existing historical data and how to better sample trajectories so that future control policies would be better. This question has been central in reinforcement learning in the last decade if not more, and involves methods from statistics, machine learning, optimization, and control theory. I will start my talk with demonstrating why planning with parameter uncertainty is an important issue. I will then describe several approaches: Bayesian uncertainty model over the unknown parameters, a robust approach that takes a worst case view, and a frequentist approach. I will then discuss the challenges that are posed when the model itself rather than just the parameters may not be fully known. I will then describe two challenging real-world domains that have been studied in my research group in collaboration with experts from industry and academia: diabetes care management in healthcare and asset management in high-voltage transmission grids. For each domain I will describe our efforts to reduce the problem to its bare essentials as a reinforcement learning problem, the algorithms for learning the control policies, and some of the lessons we learned. I graduated from the Technion with a BSc in Electrical Engineering and BA in mathematics (both summa cum laude) in 1996. After that I spent almost four years as an intelligence officer with the Israeli Defense Forces. I was subsequently involved in a few ventures in the high-tech industry. I earned my PhD in Electrical Engineering from the Technion at 2002, under the supervision of Nahum Shimkin. I was then a Fulbright postdoctoral associate with LIDS (MIT) working with John Tsitsiklis for two years. I was at the Department of Electrical and Computer Engineering in McGill University from July 2004 until August 2010, where I held a Canada Research Chair in Machine Learning from 2005 to 2009. I have been with the Department of Electrical Engineering at the Technion since 2008 where I am a professor. I am married to Tally and father to Liam, Oliver and Dylan.

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Colloquium Speaker- Percy Liang Thurs Oct 1, 4:30pm
by Nicole E. Wagenblast 01 Oct '15

01 Oct '15

Colloquium Speaker Percy Liang, Stanford University Thursday, October 1st- 4:30pm Computer Science 105 Learning Hidden Computational Processes We are interested in prediction problems in which evaluating the learned function requires multiple intermediate steps of computation. One motivation is building a system that can answer complex questions: here the function would need to map "How many countries have held the Summer Olympics more than once?" to "3" by applying a sequence of aggregation and filtering operations on a database. In this talk, we examine two key machine learning problems that arise in this setting. First, how do we model the computational process? We argue that the classic paradigm of decoupling modeling from inference is inadequate, and we propose techniques that directly model the inference procedure. Second, learning is very difficult: in our example, the supervision "3" constrains the hidden computational process in a very indirect way. We propose methods that relax the output supervision in a parameterized way, and learn both the relaxation and model parameters jointly subject to an explicit computational constraint. Finally, we show some empirical progress on a new challenging question answering task. Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research interests include (i) modeling natural language semantics, (ii) developing machine learning methods that infer rich latent structures from limited supervision, (iii) and studying the tradeoff between statistical and computational efficiency. He is a 2015 Sloan Research Fellow, 2014 Microsoft Research Faculty Fellow, a 2010 Siebel Scholar, and won the best student paper at the International Conference on Machine Learning in 2008.

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