- talks - lists.cs.princeton.edu

TODAY in B205 E-Quad - R. Srikant, University of Illinois at Urbana-Champaign
by Emily Lawrence 05 Mar '19

05 Mar '19

EE SEMINAR SERIES Speaker: R. Srikant, University of Illinois at Urbana-Champaign Title: Queues and Qs Day: Tuesday, March 5, 2019 Time: 4:30 pm Room: B205 Engineering Quadrangle Host: Prof. H. Vincent Poor Data Science relies on scalable resource allocation algorithms to operate data centers consisting of massive numbers of servers, as well as machine learning algorithms to extract information from data. In the first part of the talk, we will consider a resource allocation problem that arises in data centers and show that there exist low-complexity algorithms which use queue-length information to maximize throughput while ensuring that the packet delays do not scale with the size of the data center. Our result resolved a long-standing open conjecture at the intersection of probability, algorithms, and networks. In the second part of the talk, we will consider a reinforcement learning algorithm to estimate the Q-function of a given policy. We will present the first comprehensive finite-time performance bounds for the algorithm, which can potentially be used to characterize its sample complexity. The first part is joint work with Siva Theja Maguluri and the second part is joint work with Lei Ying. Bio: R. Srikant is the Fredric G. and Elizabeth H. Nearing Endowed Professor of Electrical and Computer Engineering and the Coordinated Science Lab at the University of Illinois at Urbana-Champaign. His research interests are in the areas of applied probability and stochastic networks, with applications to machine learning, cloud computing, and communication networks. He is the recipient of the 2019 IEEE Koji Kobayashi Computers and Communications Award and the 2015 IEEE INFOCOM Achievement Award. He has also received several Best Paper awards, including the 2017 Applied Probability Society Best Publication Award and the 2015 IEEE INFOCOM Best Paper Award. He was the Editor-in-Chief of the IEEE/ACM Transactions on Networking from 2013-2017. This seminar is supported with funds from the Korhammer Lecture Series

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Colloquium Speaker-Tianqi Chen, TODAY at 12:30pm
by Emily C. Lawrence 05 Mar '19

05 Mar '19

Colloquium Speaker Tianqi Chen from the University of Washington Tuesday, March 5 - 12:30pm Computer Science - Room 105 Host: Ryan Adams [ http://www.cs.princeton.edu/events/25820 | http://www.cs.princeton.edu/events/25820 ] Learning-based Learning Systems Data, models, and computing are the three pillars that enable machine learning to solve real-world problems at scale. Making progress on these three domains requires not only disruptive algorithmic advances but also systems innovations that can continue to squeeze more efficiency out of modern hardware. Learning systems are in the center of every intelligent application nowadays. However, the ever-growing demand for applications and hardware specialization creates a huge engineering burden for these systems, most of which rely on heuristics or manual optimization. In this talk, I will present a new approach that uses machine learning to automate system optimizations. I will describe our approach in the context of deep learning deployment problem. I will first discuss how to design invariant representations that can lead to transferable statistical cost models, and apply these representations to optimize tensor programs used in deep learning applications. I will then describe the system improvements we made to enable diverse hardware backends . TVM, our end-to-end system, delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned deep learning frameworks. Finally, I will discuss how to generalize our approach to do full-stack optimization of the model, system, hardware jointly, and how to build systems to support life-long evolution of intelligent applications. Bio: Tianqi Chen is a Ph.D. candidate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, working with Carlos Guestrin on the intersection of machine learning and systems. He has created three major learning systems that are widely adopted: XGBoost , TVM, and MXNet (co-creator). He is a recipient of the Google Ph.D. Fellowship in Machine Learning.

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Reminder: Colloquium Speaker-Tianqi Chen, tomorrow at 12:30pm
by Emily C. Lawrence 04 Mar '19

04 Mar '19

Colloquium Speaker Tianqi Chen from the University of Washington Tuesday, March 5 - 12:30pm Computer Science - Room 105 Host: Ryan Adams [ http://www.cs.princeton.edu/events/25820 | http://www.cs.princeton.edu/events/25820 ] Learning-based Learning Systems Data, models, and computing are the three pillars that enable machine learning to solve real-world problems at scale. Making progress on these three domains requires not only disruptive algorithmic advances but also systems innovations that can continue to squeeze more efficiency out of modern hardware. Learning systems are in the center of every intelligent application nowadays. However, the ever-growing demand for applications and hardware specialization creates a huge engineering burden for these systems, most of which rely on heuristics or manual optimization. In this talk, I will present a new approach that uses machine learning to automate system optimizations. I will describe our approach in the context of deep learning deployment problem. I will first discuss how to design invariant representations that can lead to transferable statistical cost models, and apply these representations to optimize tensor programs used in deep learning applications. I will then describe the system improvements we made to enable diverse hardware backends . TVM, our end-to-end system, delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned deep learning frameworks. Finally, I will discuss how to generalize our approach to do full-stack optimization of the model, system, hardware jointly, and how to build systems to support life-long evolution of intelligent applications. Bio: Tianqi Chen is a Ph.D. candidate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, working with Carlos Guestrin on the intersection of machine learning and systems. He has created three major learning systems that are widely adopted: XGBoost , TVM, and MXNet (co-creator). He is a recipient of the Google Ph.D. Fellowship in Machine Learning.

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EE and CS Speaker-Stephen Tu, 3/7 at 12:30pm
by Emily C. Lawrence 01 Mar '19

01 Mar '19

EE and CS Speaker Stephen Tu from University of California, Berkeley Thursday, March 7 - 12:30pm Computer Science - Room 105 Hosts: Ryan Adams, CS and Yuxin Chen, EE [ https://www.cs.princeton.edu/events/25824 | https://www.cs.princeton.edu/events/25824 ] Safe and Reliable Reinforcement Learning for Continuous Control Many autonomous systems such as self-driving cars, unmanned aerial vehicles, and personalized robotic assistants are inherently complex. In order to deal with this complexity, practitioners are increasingly turning towards data-driven learning techniques such as reinforcement learning (RL) for designing sophisticated control policies. However, there are currently two fundamental issues that limit the widespread deployment RL: sample inefficiency and the lack of formal safety guarantees. In this talk, I will propose solutions for both these issues in the context of continuous control tasks. In particular, I will show that in the widely applicable setting where the dynamics are linear, model-based algorithms which exploit this structure are substantially more sample efficient than model-free algorithms, such as the widely used policy gradient method. Furthermore, I will describe a new model-based algorithm which comes with provable safety guarantees and is computationally efficient, relying only on convex programming. I will conclude the talk by discussing the next steps towards safe and reliable deployment of reinforcement learning. Bio: Stephen Tu is a PhD student in Electrical Engineering and Computer Sciences at the University of California, Berkeley advised by Benjamin Recht . His research interests are in machine learning, control theory, optimization, and statistics. Recently, he has focused on providing safety and performance guarantees for reinforcement learning algorithms in continuous settings. He is supported by a Google PhD fellowship in machine learning.

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Olivier Savary Belanger will present his Pre FPO on Thursday, March 7, 2019 at 10am in CS 402.
by Nicki Mahler 28 Feb '19

28 Feb '19

Olivier Savary Belanger will present his Pre FPO on Thursday, March 7, 2019 at 10am in CS 402. The members of his committee are as follows: Andrew Appel (adviser), Greg Morrisett (Cornell), David Walker, Zak Kincaid, and Lennart Beringer All are welcome to attend. Please see below for the talk abstract. Interactive theorem provers allow for the development, in the same environment, of programs and of proofs about them. The programmatic portion of the development can then be extracted to code which is then compiled in an executable. However, unless both the extraction and compilation processes are formally verified, one has no guarantees that the proofs developed still apply to the resulting executable. In this talk, I will describe my work on CertiCoq, a verified extraction pipeline for the Coq theorem prover composing with the CompCert C verified compiler to achieve end-to-end correctness guarantees. First, I will present a proof framework to prove optimizations over the continuation-passing style (CPS) intermediate representation (IR) used in CertiCoq. This framework has been used by myself and others to prove the correctness of nontrivial optimizations. I will focus on a novel proof of correctness for a shrink reduction algorithm, a transformation combining in a single pass multiple optimizations which always result in smaller terms. Then, I will present a verified code generation translating the CPS IR into Clight, a front-end language of CompCert. I will show how it interfaces with a verified garbage collector and how its proof composes with the proof of correctness on CompCert. I will also describe the representation of values of inductive datatypes, inspired by OCaml, and the generated interface between the extracted program and regular C code.

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The "D" word: solving for "diversity" on high-tech teams, TODAY at 4:30pm
by Emily C. Lawrence 28 Feb '19

28 Feb '19

The “D” word: solving for “diversity” on high-tech teams Janet Vertesi, Assistant Professor of Sociology at Princeton University Thursday, February 28th at 4:30 PM Bowen Hall 222 This talk gives an overview of the sociological factors that affect the construction of diverse, high-performing teams. Building on a summary of key issues that affect gender and racial disparities in high-performing occupations, the talk covers the current social science and vocabulary for addressing the problem, as well as strategies for moving forward, avoiding common traps, and protecting performance-based advancement. Bio: Dubbed “Margaret Mead among the Starfleet” by the Times Literary Supplement, Janet Vertesi is Assistant Professor of Sociology at Princeton University and an expert in the sociology of science, technology, and organizations. Vertesi’s past decade of research, funded by the National Science Foundation, examines how distributed robotic spacecraft teams work together effectively to produce scientific and technical results. Her book Seeing Like a Rover (University of Chicago Press, 2015) describes the collaborative work of the Mars Exploration Rover mission including the people, the images, and the robots who do science on Mars. Vertesi is also a long-time contributor to the Association of Computing Machinery conferences on human-computer interaction and computer-supported cooperative work. She is an advisory board member of the Data and Society institute in New York City and is a member of Princeton University’s Center for Information Technology Policy. To join the talk, please email seasdiversity(a)princeton.edu. This event co-sponsored by School of Engineering and Applied Science and Department of Computer Science.

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The "D" word: solving for "diversity" on high-tech teams, tomorrow at 4:30pm
by Emily C. Lawrence 27 Feb '19

27 Feb '19

The “D” word: solving for “diversity” on high-tech teams Janet Vertesi, Assistant Professor of Sociology at Princeton University Thursday, February 28th at 4:30 PM Bowen Hall 222 This talk gives an overview of the sociological factors that affect the construction of diverse, high-performing teams. Building on a summary of key issues that affect gender and racial disparities in high-performing occupations, the talk covers the current social science and vocabulary for addressing the problem, as well as strategies for moving forward, avoiding common traps, and protecting performance-based advancement. Bio: Dubbed “Margaret Mead among the Starfleet” by the Times Literary Supplement, Janet Vertesi is Assistant Professor of Sociology at Princeton University and an expert in the sociology of science, technology, and organizations. Vertesi’s past decade of research, funded by the National Science Foundation, examines how distributed robotic spacecraft teams work together effectively to produce scientific and technical results. Her book Seeing Like a Rover (University of Chicago Press, 2015) describes the collaborative work of the Mars Exploration Rover mission including the people, the images, and the robots who do science on Mars. Vertesi is also a long-time contributor to the Association of Computing Machinery conferences on human-computer interaction and computer-supported cooperative work. She is an advisory board member of the Data and Society institute in New York City and is a member of Princeton University’s Center for Information Technology Policy. To join the talk, please email [ mailto:seasdiversity@princeton.edu | seasdiversity(a)princeton.edu ] . This event co-sponsored by School of Engineering and Applied Science and Department of Computer Science.

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Colloquium Speaker-Tianqi Chen, 3/5 at 12:30pm
by Emily C. Lawrence 27 Feb '19

27 Feb '19

Colloquium Speaker Tianqi Chen from the University of Washington Tuesday, March 5 - 12:30pm Computer Science - Room 105 Host: Ryan Adams [ http://www.cs.princeton.edu/events/25820 | http://www.cs.princeton.edu/events/25820 ] Learning-based Learning Systems Data, models, and computing are the three pillars that enable machine learning to solve real-world problems at scale. Making progress on these three domains requires not only disruptive algorithmic advances but also systems innovations that can continue to squeeze more efficiency out of modern hardware. Learning systems are in the center of every intelligent application nowadays. However, the ever-growing demand for applications and hardware specialization creates a huge engineering burden for these systems, most of which rely on heuristics or manual optimization. In this talk, I will present a new approach that uses machine learning to automate system optimizations. I will describe our approach in the context of deep learning deployment problem. I will first discuss how to design invariant representations that can lead to transferable statistical cost models, and apply these representations to optimize tensor programs used in deep learning applications. I will then describe the system improvements we made to enable diverse hardware backends . TVM, our end-to-end system, delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned deep learning frameworks. Finally, I will discuss how to generalize our approach to do full-stack optimization of the model, system, hardware jointly, and how to build systems to support life-long evolution of intelligent applications. Bio: Tianqi Chen is a Ph.D. candidate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, working with Carlos Guestrin on the intersection of machine learning and systems. He has created three major learning systems that are widely adopted: XGBoost , TVM, and MXNet (co-creator). He is a recipient of the Google Ph.D. Fellowship in Machine Learning.

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EE and CS Speaker-Jaime Fernández Fisac, TODAY at 4:30pm
by Emily C. Lawrence 26 Feb '19

26 Feb '19

EE and CS Speaker Jaime Fernández Fisac from the University of California, Berkeley Tuesday, February 26 - 4:30pm Computer Science 105 Hosts: Naveen Verma , EE and Szymon Rusinkiewicz, CS [ http://ee.princeton.edu/events/resilient-safety-assurance-robotic-systems-s… | http://ee.princeton.edu/events/resilient-safety-assurance-robotic-systems-s… ] Resilient Safety Assurance for Robotic Systems: Staying Safe Even When Models Are Wrong In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must be able to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads means that any precomputed guarantees may become invalid when modeling assumptions made at design time are violated once the system is deployed. My research aims to enable autonomous systems to proactively ensure safety during their operation by explicitly reasoning about the gap between their models and the real world. In this talk I will present recent contributions to safety assurance for autonomous systems. I will first discuss new advances in efficient safety computation, and demonstrate their use in large-scale unmanned air traffic. Next, I will present a general safety framework enabling the use of learning control schemes (e.g. reinforcement learning) for safety-critical robotic systems in uncertain environments. I will then turn my attention to the important problem of safe human-robot interaction, and introduce a real-time Bayesian method to monitor the reliability of predictive human models. The talk will end with a discussion of challenges and opportunities ahead, including the introduction of game-theoretic planning in autonomous driving and the bridging of safety analysis and deep reinforcement learning. Bio: Jaime Fernández Fisac is a Ph.D. candidate in Electrical Engineering and Computer Sciences at UC Berkeley. His research interests lie in control theory and artificial intelligence, with a focus on safety assurance for autonomous systems. He works to enable robotic systems to safely interact with uncertain environments and human beings despite using inaccurate models. Jaime received a B.S./M.S. degree in Electrical Engineering from the Universidad Politécnica de Madrid, Spain, in 2012, and a M.Sc. in Aeronautics from Cranfield University, U.K., in 2013. He is a recipient of the La Caixa Foundation fellowship.

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Reminder: EE and CS Speaker-Jaime Fernández Fisac, tomorrow at 4:30pm
by Emily C. Lawrence 25 Feb '19

25 Feb '19

EE and CS Speaker Jaime Fernández Fisac from the University of California, Berkeley Tuesday, February 26 - 4:30pm Computer Science 105 Hosts: Naveen Verma, EE and Szymon Rusinkiewicz, CS [ http://ee.princeton.edu/events/resilient-safety-assurance-robotic-systems-s… | http://ee.princeton.edu/events/resilient-safety-assurance-robotic-systems-s… ] Resilient Safety Assurance for Robotic Systems: Staying Safe Even When Models Are Wrong In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must be able to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads means that any precomputed guarantees may become invalid when modeling assumptions made at design time are violated once the system is deployed. My research aims to enable autonomous systems to proactively ensure safety during their operation by explicitly reasoning about the gap between their models and the real world. In this talk I will present recent contributions to safety assurance for autonomous systems. I will first discuss new advances in efficient safety computation, and demonstrate their use in large-scale unmanned air traffic. Next, I will present a general safety framework enabling the use of learning control schemes (e.g. reinforcement learning) for safety-critical robotic systems in uncertain environments. I will then turn my attention to the important problem of safe human-robot interaction, and introduce a real-time Bayesian method to monitor the reliability of predictive human models. The talk will end with a discussion of challenges and opportunities ahead, including the introduction of game-theoretic planning in autonomous driving and the bridging of safety analysis and deep reinforcement learning. Bio: Jaime Fernández Fisac is a Ph.D. candidate in Electrical Engineering and Computer Sciences at UC Berkeley. His research interests lie in control theory and artificial intelligence, with a focus on safety assurance for autonomous systems. He works to enable robotic systems to safely interact with uncertain environments and human beings despite using inaccurate models. Jaime received a B.S./M.S. degree in Electrical Engineering from the Universidad Politécnica de Madrid, Spain, in 2012, and a M.Sc. in Aeronautics from Cranfield University, U.K., in 2013. He is a recipient of the La Caixa Foundation fellowship.

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