- talks - lists.cs.princeton.edu

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

12 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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Keller Center 10th Anniversary Symposium- Tuesday, October 13
by Nicole E. Wagenblast 09 Oct '15

09 Oct '15

Keller Center 10th Anniversary Symposium- Tuesday, October 13 Please join us as we celebration a decade of Keller. Our afternoon program will include discussions on Education Innovation and Entrepreneurship in higher education with some of Princeton’s leaders in the field, a keynote address by Tom Leighton ’78 with introduction by President Eisgruber ’83, and of course there will be cake! For more information on the speakers and to register, please visit: http://kellercenter.princeton.edu/engage/e-vents/keller-center-10-years/

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Colloquium Speaker Martial Hebert, Fri Oct 9, 12:30pm
by Nicole E. Wagenblast 08 Oct '15

08 Oct '15

Colloquium Speaker Martial Hebert, Carnegie Mellon University Friday, October 9, 12:30pm Computer Science 105 The task of visual prediction is important for two main reasons: (a) For intelligent agents and systems, prediction is vital for decision making. For example, in order to perform assistive activities, robots must be able to predict the intentions of other agents in the scene. Even a task as simple as walking through a crowded hallway requires the prediction of human trajectories. (b) More importantly, prediction requires deep understanding of the visual world and complex interplay between different elements of the scene. Therefore, prediction can act as a way to define “what does it mean to understand an image,” and the task of visual prediction can act as an enabler for scene understanding. In this talk, I will go over several techniques for prediction developed over the past few years. These techniques operates at different levels of descriptions from high-level object motions, to mid-level patch motion and appearance changes, to low-level optical flow. They operate at different levels of supervision from strong the supervision requiring explicit object tracking to unsupervised learning for patch-based prediction. I will discuss the pros and cons of different levels, ideas for combining them and example applications in robotics and transportation applications. Martial Hebert is a Professor of Robotics Carnegie Mellon University and Director of the Robotics Institute, which he joined in 1984. His interest includes computer vision, especially recognition in images and video data, model building and object recognition from 3D data, and perception for mobile robots and for intelligent vehicles. His group has developed approaches for object recognition and scene analysis in images, 3D point clouds, and video sequences. In the area of machine perception for robotics, his group has developed techniques for people detection, tracking, and prediction, and for understanding the environment of ground vehicles from sensor data. He has served on the editorial boards the IEEE Transactions on Robotics and Automation, the IEEE transactions on Pattern Analysis and Machine Intelligence, and the International Journal of Computer Vision (for which he currently serves as Editor-in-Chief).

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Perspectives from a Young Alumni Founder Patrick Wendell'11- Keller Center Event
by Nicole E. Wagenblast 07 Oct '15

07 Oct '15

Perspectives from a Young Alumni Founder, Patrick Wendell'11 Keller Center Event Thursday, October 8, 5:30pm Hub 34 Chambers Street Event Room RSVP Required This talk will feature Patrick Wendell, class of 2011. Two years after graduating from Princeton in Computer Science, Patrick co-founded Databricks, a company commercializing the Apache Spark software platform for large scale data processing. Databricks helps companies extract value from large amounts of data. Over the last two and a half years, Databricks has raised more than $40 million inventure funding from lead investors Andreessen Horowitz and NEA Ventures. The company employs more than 70 people at its San Francisco headquarters. In this talk, which will be followed by a "meet-and-greet" session, Patrick will revisit the founding of the company and reflect on lessons learned along the path from Princeton, to Berkeley, and finally to the start-up world. Topics will include fundraising, product strategy, building successful teams, transferring technical innovation into usable products, and perspectives on how the undergraduate life differs from the life of an entrepreneur. He will also discuss different avenues to an entrepreneurial career for Princeton students. The session will be highly interactive and allow for questions from the audience. Patrick Wendell is a co-founder of Databricks as well as a founding Committer and PMC member of the Apache Spark software project. In the Spark project, Patrick has acted as release manager for several Spark releases, in addition to maintaining several subsystems of Spark's core engine. At Databricks, Patrick directs the company's maintenance and development of Spark.Patrick holds an M.S. in Computer Science at UC Berkeley, where his research focused on low latency scheduling for large scale analytics workloads. He holds a B.S.E in Computer Science from Princeton University. RSVP for this event to bjarvie(a)princeton.edu

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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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Colloquium Speaker- Ming Zhang Tues Sept 29, 4:30pm
by Nicole E. Wagenblast 29 Sep '15

29 Sep '15

Colloquium Speaker Ming Zhang, Microsoft Research Tuesday, September 29, 4:30pm Computer Science 105 Software-Defined Datacenter Networks Today ’s cloud-scale services (e.g., search, social networking and cloud computing) operate on large datacenter networks (DCNs). Keeping these networks running smoothly is difficult, due to the sheer number of physical devices, the complexity of network software stack, and the dynamic nature of the environment. At any given moment, multiple devices experience component failures, are brought down for planned maintenance, are upgraded with new firmware, or are reconfigured to adapt to prevailing traffic demand. In this talk, I will go through some of the automated systems that are developed for managing the routing, configuration, and firmware and power of network devices in Microsoft Azure. I will first describe how NetPilot achieves fast, safe mitigation of common network failures by automatically deactivating or restarting offending links or devices. I will then describe how SWAN boosts the utilization of inter-datacenter networks by centrally controlling when and how much traffic each service sends and frequently reconfiguring the network’s data plane to match current traffic demand. Finally, I will present Network State Service (NSS) that manages the entire network state for Azure datacenter and wide-area networks. It serves as the foundation of multiple network management applications (including NetPilot and SWAN), allowing these applications to operate independently, while maintaining network-wide safety. Ming Zhang is a Senior Researcher in the Mobility and Networking group at Microsoft Research, where he focuses on datacenter and wide-area networking. He creates cutting-edge technologies that power Microsoft’s massive cloud networks. His research is published in leading systems and networking conferences including SIGCOMM, NSDI, OSDI, and MobiSys. His work on MobiPerf won the Open Internet App Award and People's Choice App Award in FCC Open Internet Challenge. He received his Ph.D. in Computer Science from Princeton University in 2005 and his B.S. in Computer Science from Nanjing University in 1999.

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Colloquium Speaker- Ming Zhang Tues Sept 29, 4:30pm
by Nicole E. Wagenblast 28 Sep '15

28 Sep '15

Colloquium Speaker Ming Zhang, Microsoft Research Tuesday, September 29, 4:30pm Computer Science 105 Software-Defined Datacenter Networks Today ’s cloud-scale services (e.g., search, social networking and cloud computing) operate on large datacenter networks (DCNs). Keeping these networks running smoothly is difficult, due to the sheer number of physical devices, the complexity of network software stack, and the dynamic nature of the environment. At any given moment, multiple devices experience component failures, are brought down for planned maintenance, are upgraded with new firmware, or are reconfigured to adapt to prevailing traffic demand. In this talk, I will go through some of the automated systems that are developed for managing the routing, configuration, and firmware and power of network devices in Microsoft Azure. I will first describe how NetPilot achieves fast, safe mitigation of common network failures by automatically deactivating or restarting offending links or devices. I will then describe how SWAN boosts the utilization of inter-datacenter networks by centrally controlling when and how much traffic each service sends and frequently reconfiguring the network’s data plane to match current traffic demand. Finally, I will present Network State Service (NSS) that manages the entire network state for Azure datacenter and wide-area networks. It serves as the foundation of multiple network management applications (including NetPilot and SWAN), allowing these applications to operate independently, while maintaining network-wide safety. Ming Zhang is a Senior Researcher in the Mobility and Networking group at Microsoft Research, where he focuses on datacenter and wide-area networking. He creates cutting-edge technologies that power Microsoft’s massive cloud networks. His research is published in leading systems and networking conferences including SIGCOMM, NSDI, OSDI, and MobiSys. His work on MobiPerf won the Open Internet App Award and People's Choice App Award in FCC Open Internet Challenge. He received his Ph.D. in Computer Science from Princeton University in 2005 and his B.S. in Computer Science from Nanjing University in 1999.

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PACM/CS Colloquium Speaker- Ben Recht, Monday Sept 28, 4:30pm
by Nicole E. Wagenblast 28 Sep '15

28 Sep '15

Ben Recht , University of California, Berkeley The Resilience of the Perceptron Monday, September 28th- 4:30pm 214 Fine Hall The most widely used optimization method in machine learning practice is the Perceptron Algorithm, also known as the Stochastic Gradient Method (SGM). This method has been used since the fifties to build statistical estimators, iteratively improving models by correcting errors observed on single data points. SGM is not only scalable, robust, and simple to implement, but achieves the state-of-the-art performance in many different domains. In contemporary systems, SGM powers enterprise analytics systems and is the workhorse tool used to train complex pattern-recognition systems in speech and vision. In this talk, I will explore why SGM has had such staying power, focusing on notions of stability and robustness. I will first discuss how SGM is robust to perturbations of the model and the updates. From a computing systems perspective, this robustness enables parallel implementations with minimal communication, with no locking or synchronization, and with strong spatial locality. I will then show how SGM is robust to perturbations of the data itself, and prove that any model trained with stochastic gradient method in a reasonable amount of time attains small generalization error. I will subsequently provide a new interpretation of common practices in neural networks, and provide a formal rationale for many popular techniques in training large, deep models.

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