March 2008 - talks - lists.cs.princeton.edu

Talk TOMORROW
by Ginny Hogan 26 Mar '08

26 Mar '08

Computer Science Talk Tomorrow - 4:15pm Computer Science Room 105. "Recent Directions in Nonparametric Bayesian Machine Learning Zoubin Ghahramani Carnegie Mellon University Machine learning is an interdisciplinary field which seeks to develop both the mathematical foundations and practical applications of systems that learn, reason and act. Machine learning draws from many fields, ranging from Computer Science, to Engineering, Psychology, Neuroscience, and Statistics. Because uncertainty, data, and inference play a fundamental role in the design of systems that learn, statistical methods have recently emerged as one of the key components of the field of machine learning. In particular, Bayesian methods, based on the work of Reverend Thomas Bayes in the 1700s, describe how probabilities can be used to represent the degrees of belief of a rational agent. Bayesian methods work best when they are applied to models that are flexible enough to capture to complexity of real-world data. Recent work on non-parametric Bayesian methods provides this flexibility. I will touch upon key developments in the field, including Gaussian processes, Dirichlet processes, and the Indian buffet process (IBP). Focusing on the IBP, I will describe how this can be used in a number of applications such as collaborative filtering, bioinformatics, cognitive modelling, independent components analysis, and causal discovery. Finally, I will outline the main challenges in the field: how to develop new models, new fast inference algorithms, and compelling applications.

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Talk Today - 4:15 PM
by Ginny Hogan 26 Mar '08

26 Mar '08

Dan Grossman, University of Washington, will present his talk "Software Transacations: A Programming- Languages Perspective" at 4:14 today in Computer Science Room 105.

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Talk tomorrow - 3/26 -
by Ginny Hogan 25 Mar '08

25 Mar '08

Talk Wednesday 3/26 - 4:15pm - Computer Science Room 105 Software Transactions: A Programming-Languages Perspective Dan Grossman University of Washington With multicore processors bringing parallel computing to the masses, there is an urgent need to make concurrent programming easier. Software transactions hold great promise for simplifying shared-memory concurrency,and they have received enormous attention from the research community in the last couple years. This talk will provide an overview of work done at the University of Washington to help bring transactions to the next generation of programming languages. No prior knowledge of software transactions will be necessary. Our work complements research done on hardware and software algorithms for implementing transactions by considering essential issues regarding how transactions affect language semantics and language implementation. Our motivation takes the novel view that transactions can improve language support for concurrency much like garbage collection can improve language support for memory management. Our language design and language semantics work has considered the pitfalls of so-called "weak isolation" and how to avoid them, interaction with other language features like native calls and exceptions, and the implications for shared-memory consistency models. Our implementation work includes techniques for the special case of a uniprocessor, a whole-program static optimization that uses pointer information to remove unnecessary read- and write-barriers while providing "strong isolation", and ongoing work for allowing parallelism within transactions. Dan Grossman is an Assistant Professor in the Department of Computer Science & Engineering at the University of Washington. His research in the design and implementation of programming languages is aimed at improving software quality.

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S Comandur preFPO
by Melissa M Lawson 19 Mar '08

19 Mar '08

Seshadhri Comandur will present his preFPO on Wednesday March 26 at 3:30PM in Room 402. The members of his committee are: Bernard Chazelle, advisor; Moses Charikar and Sanjeev Arora, readers; Boaz Barak and Rob Schapire, nonreaders. Everyone is invited to attend his talk. The abstract follows below. --------------------------------------- Title : Dealing with data : Online reconstruction and self-improving algorithms Abstract : With the ubiquity of large data sets, reading the whole input might itself by a luxury. Consider some large dataset that is supposed to (but does not) have some structural property, e.g., a set of numbers should be sorted, a set of points should in convex position, or a graph should be an expander. Let the input be represented by a function f (over a suitable domain and range). Our aim is to output a function g that satisfies the property, and does not differ too much from f. Furthermore, we want to generate g in an online sublinear fashion : given a domain point x, we can output g(x) in sublinear time. This is the problem of online reconstruction, which naturally has connections to property testing and other sublinear slgorithms. We study the reconstruction problem in various settings - monotonicity of functions over lattices, convexity of surfaces, and bounded degree expanders. The results obtained are also interesting in their own right, and provide sublinear algorithmic tools to study these properties. In an interesting aside, we use sublinear tools for online learning problems. We define stronger performance measures for learning problems, and provde very efficient algorithms that attain this performance. One of the key insights is how tools from the sublinear and streaming world allow us to construct efficient learning algorithms. Input in the real world is not always worst-case, and probably comes from some low entropy distribution over the input space. It seems natural that algorithms should run faster given this constraint. Is it possible to learn enough about the input distribution to optimize running time? This is the problem of designing self-improving algorithms, which can tune themselves to the input distribution and cut down their running time. We prove a variety of results for the classic problem of sorting numbers, and then extend these to computational geometry world.

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11am Fri March 14 seminar on road/traffic monitoring in CS 105
by Jennifer Rexford 14 Mar '08

14 Mar '08

Hi folks, On Friday March 14, Venkat Padmanabhan -- who leads the Mobility, Networks, and Systems group at Microsoft Research India -- will be visiting. See http://research.microsoft.com/research/mns/default.aspx His talk is at 11am in the small auditorium (CS 105), with the title/abstract below. He'd also like to give interested folks an overview of the MSR lab in Bangalore, and to discuss career opportunities for internships, research positions, and visiting researchers. That session will start run from 2:15-3:30pm, in a room to-be-announced later. Please let me know if you'd like to meet with Venkat and/or join for lunch at noon. -- Jen Speaker: Venkat Padmanabhan Title: TrafficSense: Rich Road and Traffic Monitoring Using Mobile Smartphones Abstract: We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present TrafficSense, a system that piggybacks on smartphones that users carry around with them. Each participating smartphone performs rich sensing using its communication radios, GPS, accelerometer, and microphone, scans for and exchanges information from other participating phones in its neighbourhood, and reports processed information back to a central server for aggregation and reporting. We discuss key technical challenges, including energy optimization and the need for hands-free operation, present the design of TrafficSense, including how information from multiple sensors is combined to make inferences, and evaluate it in various road and traffic settings in Bangalore. (Joint work with Prashanth Mohan and Ram Ramjee.) Bio: Venkat Padmanabhan is a Senior Researcher and Research Manager at Microsoft Research India in Bangalore, where he founded and now leads the Mobility, Networks, and Systems group. Venkat was previously with Microsoft Research Redmond for 8.5 years. His research interests are in networked systems and his current projects focus on mobile and sensor systems, and network management. His professional service has included serving as program (co-)chair for ACM NOSSDAV 2004, ACM IMC 2005, and IEEE HotWeb 2008, and as an affiliate faculty member at the University of Washington, where he has taught and served in student thesis committees. Venkat holds a B.Tech. from IIT Delhi and an M.S. and Ph.D. from UC Berkeley, all in Computer Science.

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Talk today 3/12 - 4:15PM - Computer Science Room 105 -
by Ginny Hogan 12 Mar '08

12 Mar '08

Reinventing Partially Observable Reinforcement Learning Eyal Amir University of Illinois, Urbana-Champaign Many complex domains offer limited information about their exact state and the way actions affect them. There, autonomous agents need to make decisions at the same time that they learn action models and track the state of the domain. This combined problem can be represented within the framework of reinforcement learning in POMDPs, and is known to be computationally difficult. In this presentation I will describe a new framework for such decision making, learning, and tracking. This framework applies results that we achieved about updating logical formulas (belief states) after deterministic actions. It includes algorithms that represent and update the set of possible action models and world states compactly and tractably. It makes a decision with this set, and updates the set after taking the chosen action. Most importantly, and somewhat surprisingly, the number of actions that our framework takes to achieve a goal is bounded polynomially by the length of an optimal plan in a fully observable, fully known domain, under lax conditions. Finally, our framework leads to a new stochastic-filtering approach that has better accuracy than previous techniques. * Joint work with Allen Chang, Hannaneh Hajishirzi, Stuart Russell, Dafna Shahaf, and Afsaneh Shirazi (IJCAI'03,IJCAI'05,AAAI'06,ICAPS'06,IJCAI'07,AAAI'07).

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Talk by Yaron Lipman today at noon
by Tom Funkhouser 07 Mar '08

07 Mar '08

Yaron Lipman from Tel Aviv University will give a talk today at noon in CS 401. Title: Geometric Deformations. Abstract: Deforming objects in a "shape preserving" manner is a challenging problem with various applications in geometric modeling and computer graphics. In this talk I will examine two geometric-driven approaches to the problem. First, reformulating the problem using the notion of Cartan's moving frames reveals an intriguing relationship between harmonic maps into the group of rotations and shape preserving deformations. The moving frames are known in differential geometry for their ability to simplify some surface theory argumentation. Their employment leads to rather simple algorithms for shape deformation and shape blending of discrete surfaces (meshes). I will present the moving frames as rigid-motion invariant surface representation which is suited for deformations and shape blending. Then I will discuss the optimal rotation field between two isometric surfaces and its use in the context of surface deformation. Second, I will present a recent result demonstrating a closed-form solution to shape preserving space deformations. The new scheme guarantees a pure conformal mapping in 2D and quasi-conformal mappings in 3D. This generalizes recent interesting affine-invariant free form deformation techniques and provides an extremely fast algorithm for shape preserving deformations. Short Bio: Yaron Lipman is a PhD candidate at Tel-Aviv University. He received the BSc degree in mathematics and computer science from Tel Aviv University in 2003. His research interests are in applied geometry, approximations and computer graphics.

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P Shilane FPO
by Melissa M Lawson 06 Mar '08

06 Mar '08

Phil Shilane will present his FPO on Thursday March 13 at 9AM in Room 402. The members of his committee are: Tom Funkhouser, advisor; Szymon Rusinkiewicz and Adam Finkelstein, readers; Andrea LaPaugh and David Dobkin, nonreaders. A copy of his thesis, "Shape Distinction for 3D Object Retrieval", is on display in Room 310. Everyone is invited to attend his talk.

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talk is starting in 10 minuetes
by Ginny Hogan 05 Mar '08

05 Mar '08

Konstantinos Daskalakis talk is starting in 10 minutes - COS 105

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Speaker Today 4:15pm COS 105
by Ginny Hogan 05 Mar '08

05 Mar '08

Konstantinos Daskalakis, UC Berkeley, will present his talk "Computing Equilibria in Games" at 4:15pm Computer Science Room 105.

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