- talks - lists.cs.princeton.edu

Yichen Chen will present his Pre FPO on Monday, April 1, 2019 at 2pm in CS 105.
by Nicki Mahler 26 Mar '19

26 Mar '19

Yichen Chen will present his Pre FPO on Monday, April 1, 2019 at 2pm in CS 105. The members of his committee are as follows: Mengdi Wang (advisor), Sanjeev Arora (nonreader), Robert Schapire (nonreader), Karthik Narasimhan (reader), Yuxin Chen (reader) The talk title and abstract follow below. All are welcome to attend. Title: Sample-efficient reinforcement learning via utilizing state features Abstract: The field of reinforcement learning has made enormous progress in the last few years, largely due to deep neural networks. Despite success on many practical problems, our reinforcement learning algorithms require huge amount of data. In many applications such as healthcare, data of this large scale is simply not available. In this talk, I explore novel approaches from stochastic optimization that open up opportunities for designing sample-efficient reinforcement learning algorithms. First, I will show how state features, which might come from machine learning algorithms or expert domain knowledge, can facilitate the design of data-efficient reinforcement learning algorithms. Next, I will show that reinforcement learning problems are easier to solve compared to Markov decision processes with full knowledge due to the powerful sampling mechanism in reinforcement learning.

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Colloquium Speaker-Jiajun Wu, Monday, April 1 at 12:30pm
by Emily C. Lawrence 26 Mar '19

26 Mar '19

Colloquium Speaker Jiajun Wu from the Massachusetts Institute of Technology Monday, April 1 - 12:30pm Computer Science - Room 105 Host: Tom Griffiths Learning to See the Physical World [ http://www.cs.princeton.edu/events/25840 | http://www.cs.princeton.edu/events/25840 ] Human intelligence is beyond pattern recognition. From a single image, we're able to explain what we see, reconstruct the scene in 3D, predict what's going to happen, and plan our actions accordingly. In this talk, I will present our recent work on physical scene understanding---building versatile, data-efficient, and generalizable machines that learn to see, reason about, and interact with the physical world. The core idea is to exploit the generic, causal structure behind the world, including knowledge from computer graphics, physics, and language, in the form of approximate simulation engines, and to integrate them with deep learning. Here, deep learning plays two major roles: first, it learns to invert simulation engines for efficient inference; second, it learns to augment simulation engines for constructing powerful forward models. I'll focus on a few topics to demonstrate this idea: building scene representation for both object geometry and physics; learning expressive dynamics models for planning and control; perception and reasoning beyond vision. Bio: Jiajun Wu is a Ph.D. student in Electrical Engineering and Computer Science at Massachusetts Institute of Technology. He received his B.Eng. from Tsinghua University in 2014. His research interests lie in the intersection of computer vision, machine learning, robotics, and computational cognitive science. His research has been recognized through the IROS Best Paper Award on Cognitive Robotics and fellowships from Facebook, Nvidia , Samsung, Baidu, and Adobe, and his work has been covered by major media outlets including CNN, BBC, WIRED, and MIT Tech Review.

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TODAY March 21 at 4:30pm in B205 E-Quad - Rajalakshmi Nandakumar, University of Washington
by Emily Lawrence 21 Mar '19

21 Mar '19

Speaker: Rajalakshmi Nandakumar, University of Washington Title: Computational wireless sensing at scale Day: Thursday, March 21, 2019 Time: 4:30 pm Room: B205 Engineering Quadrangle Host: Prof. Niraj Jha Abstract: Computational wireless sensing is an exciting field of research where we use wireless signals from everyday computing devices to enable sensing. The key challenge is to enable new sensing capabilities that can be deployed at scale and have an impact in the real world. In this talk, I will show how to enable computational wireless sensing at scale by leveraging ubiquitous hardware such as smartphones. Specifically, I will present core technology that can wirelessly sense motion and physiological signals such as breathing using just a smartphone, in a contactless manner. To achieve this, we transform smartphones into active sonar systems. I will show how we can use this technology to detect potentially life-threatening conditions such as opioid overdoses as well as sleep apnea. Finally, I will briefly talk about my work that leverages new hardware trends in micro-controllers to enable wireless sensing applications ranging from object tracking to sensing using live insects such as bees. Bio: Rajalakshmi Nandakumar is a Ph.D. candidate at the Paul G. Allen School of Computer Science and Engineering at the University of Washington. Her research focuses on developing wireless sensing technologies that enable novel applications in various domains including mobile health, user interfaces and IoT networks. She developed the first contactless smartphone-based sleep apnea diagnosis system that was licensed by ResMed Inc. and now used by millions of users for sleep staging. She was recognized with the UW Medicine Judy Su Clinical Research award, Paul Baran Young Scholar award by the Marconi Society and also named as the rising star in EECS by MIT. She has first author papers in top medical journals including Science translational medicine as well as computing venues (CHI, SIGCOMM, SenSys, MobiCom, MobiSys). Her research was awarded multiple accolades and nominations including MobiSys 2015 best paper nominee, CHI 2016 Honorable mention award and SenSys 2018 best paper award.

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NOW in B205 E-Quad - Pat Pannuto, University of California, Berkeley
by Emily Lawrence 18 Mar '19

18 Mar '19

EE SEMINAR SERIES Speaker: Pat Pannuto, University of California, Berkeley Title: A Modular Platform for Nanopower Computing Day: Monday, March 18, 2019 Time: 4:30 pm Room: B205 Engineering Quadrangle Host: Prof. Naveen Verma Beyond the Internet of Things that is emerging today lies the Internet of Everything-the vision of ubiquitous and pervasive computing, of a digital sensory layer enveloping the world. Realizing this vision relies on expanding our capability to place computation in the wild, to then network these sensors, to manage them, and most challengingly, to power them. This talk is about a suite of solutions working in concert to usher in the nanopower computing class. We will discuss architectural innovations that enable power oblivious operation and modular design for millimeter-scale devices. Next, we will look at how to bring multiprocessing, safety, and resource isolation to the deeply embedded computing class. Then, we will consider the some of services, in particular localization, necessary to make pervasive computers useful and viable. Finally, we will look toward the future, with an eye towards what technical challenges remain and how our interactions with computational infrastructure will change as we realize the ubiquitous computing vision. Bio: Pat Pannuto is currently completing his PhD in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He received his MSE in Computer Science and BSE in Computer Engineering from the University of Michigan. Pat's research is in the broad area of networked embedded systems, with contributions to computer architecture, wireless communications, mobile computing, operating systems, and development engineering. Pat's work has been recognized as a Top Pick in Computer Architecture and selected as a Best Paper Finalist at IPSN, and has been awarded NSF, NDSEG, and Qualcomm Innovation fellowships. Pat has also received teaching awards from the Computer Science Department, the College of Engineering, and the Rackham Graduate School at the University of Michigan.

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Unsolved Data Problems: HAPPENING NOW!
by Emily Lawrence 13 Mar '19

13 Mar '19

Wednesday, March 13 4:30 – 5:30 pm Computer Science, Rm 105 https://cdh.princeton.edu/events/2019/03/unsolved-data-problems/ Unsolved Data Problems will introduce faculty and students in the computer and data sciences to the untapped research possibilities inherent in humanities data. A panel of Princeton faculty - Meredith Martin (English), Marina Rustow (History and Near Eastern Studies) and Dan Trueman (Music) - will discuss some of Princeton’s landmark digital humanities projects, and the challenges they’ve faced when transforming historical, multilingual and experimental source material into data and code. Projects discussed include the Princeton Prosody Archive, the Princeton Geniza Lab, and bitKlavier. Jennifer Rexford and Brian Kernighan (Computer Science) will moderate the panel. Help discover innovative algorithmic solutions to these unsolved computational problems. This panel will be of particular interest to researchers working in the fields of: computer vision, natural language processing, machine learning, and audio/music engineering. This event is collaboratively organized by the Center for Digital Humanities, the Department of Computer Science and the Center for Statistics and Machine Learning. Reception to follow immediately after the panel.

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Brian Bullins will present his Pre-FPO "Efficient Higher-Order Optimization for Machine Learning" on Friday, March 15, 2019 at 10am in CS 402.
by Nicki Mahler 11 Mar '19

11 Mar '19

Brian Bullins will present his Pre-FPO "Efficient Higher-Order Optimization for Machine Learning" on Friday, March 15, 2019 at 10am in CS 402. The members of his committee are as follows: Elad Hazan (adviser), Sanjeev Arora, Mark Braverman, Yuxin Chen (ELE), and Yoram Singer All are welcome to attend. The talk title and abstract follow below. Title: Efficient Higher-Order Optimization for Machine Learning Abstract: In recent years, stochastic gradient descent (SGD) has taken center stage for training large-scale models in machine learning. Although some higher-order methods have improved iteration complexity in theory, the per-iteration costs render them unusable when faced with millions of parameters and training examples. In this talk, I will present several of my works which enable higher-order optimization to be as scalable as first-order methods. The first method is a stochastic second-order algorithm for convex optimization, called LiSSA, which uses Hessian information to construct an unbiased Newton step in time linear in the dimension. To bypass the typical efficiency barriers for second-order methods, we harness the ERM structure in standard machine learning tasks. While convex problems allow for global convergence, recent state-of-the-art models, such as deep neural networks, highlight the importance of developing a better understanding of non-convex guarantees. In order to handle this challenging setting, I will present FastCubic, a Hessian-based method which provably converges to first-order critical points faster than gradient descent, while additionally converging to second-order critical points. Finally, I will show how our algorithm FastQuartic combines even higher-order information with “highly smooth acceleration” to guarantee even faster convergence for a large class of convex quartic problems.

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Colloquium Speaker-Jason Lee TODAY at 12:30pm
by Emily C. Lawrence 11 Mar '19

11 Mar '19

Colloquium Speaker Jason Lee from University of Southern California Monday, March 11 - 12:30pm Computer Science - Room 105 Host: Elad Hazan [ https://www.cs.princeton.edu/events/25822 | https://www.cs.princeton.edu/events/25822 ] On the Foundations of Deep Learning: SGD, Overparametrization, and Generalization Deep Learning has had phenomenal empirical successes in many domains including computer vision, natural language processing, and speech recognition. To consolidate and boost the empirical success, we need to develop a more systematic and deeper understanding of the elusive principles of deep learning. In this talk, I will provide analysis of several elements of deep learning including non-convex optimization, overparametrization, and generalization error. First, we show that gradient descent and many other algorithms are guaranteed to converge to a local minimizer of the loss. For several interesting problems including the matrix completion problem, this guarantees that we converge to a global minimum. Then we will show that gradient descent converges to a global minimizer for deep overparametrized networks. Finally, we analyze the generalization error by showing that a subtle combination of SGD, logistic loss, and architecture combine to promote large margin classifiers, which are guaranteed to have low generalization error. Together, these results show that on overparametrized deep networks SGD finds solution of both low train and test error. Bio: Jason Lee is an assistant professor in Data Sciences and Operations at the University of Southern California. Prior to that, he was a postdoctoral researcher at UC Berkeley working with Michael Jordan. Jason received his PhD at Stanford University advised by Trevor Hastie and Jonathan Taylor. His research interests are in statistics, machine learning, and optimization. Lately, he has worked on the foundations of deep learning, non-convex optimization algorithm, and adaptive statistical inference. He has received a Sloan Research Fellowship in 2019 and NIPS Best Student Paper Award for his work.

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Reminder: Colloquium Speaker-Jason Lee tomorrow at 12:30pm
by Emily C. Lawrence 10 Mar '19

10 Mar '19

Colloquium Speaker Jason Lee from University of Southern California Monday, March 11 - 12:30pm Computer Science - Room 105 Host: Elad Hazan [ https://www.cs.princeton.edu/events/25822 | https://www.cs.princeton.edu/events/25822 ] On the Foundations of Deep Learning: SGD, Overparametrization, and Generalization Deep Learning has had phenomenal empirical successes in many domains including computer vision, natural language processing, and speech recognition. To consolidate and boost the empirical success, we need to develop a more systematic and deeper understanding of the elusive principles of deep learning. In this talk, I will provide analysis of several elements of deep learning including non-convex optimization, overparametrization, and generalization error. First, we show that gradient descent and many other algorithms are guaranteed to converge to a local minimizer of the loss. For several interesting problems including the matrix completion problem, this guarantees that we converge to a global minimum. Then we will show that gradient descent converges to a global minimizer for deep overparametrized networks. Finally, we analyze the generalization error by showing that a subtle combination of SGD, logistic loss, and architecture combine to promote large margin classifiers, which are guaranteed to have low generalization error. Together, these results show that on overparametrized deep networks SGD finds solution of both low train and test error. Bio: Jason Lee is an assistant professor in Data Sciences and Operations at the University of Southern California. Prior to that, he was a postdoctoral researcher at UC Berkeley working with Michael Jordan. Jason received his PhD at Stanford University advised by Trevor Hastie and Jonathan Taylor. His research interests are in statistics, machine learning, and optimization. Lately, he has worked on the foundations of deep learning, non-convex optimization algorithm, and adaptive statistical inference. He has received a Sloan Research Fellowship in 2019 and NIPS Best Student Paper Award for his work.

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Fwd: ACM Princeton Chapter - Mar. 14 meeting and upcoming March events
by Jennifer Rexford 07 Mar '19

07 Mar '19

> > From: Dennis Mancl <dmancl(a)ACM.ORG> > Subject: ACM Princeton Chapter - Mar. 14 meeting and upcoming March events > Date: March 7, 2019 at 2:48:34 PM EST > To: PRINCETON-ACM-NOTICE(a)LISTSERV.ACM.ORG > Reply-To: Dennis Mancl <dmancl(a)ACM.ORG> > > Upcoming Princeton ACM / IEEE Computer Society Meetings and Events > > Thursday Mar. 14 - "Bias in Face Recognition: Definitions, Magnitude, Mitigation," Patrick Grother, National Institute of Standards and Techology > > Friday Mar. 22 - IT Professional Conference at TCF (at The College of New Jersey) -- see http://PrincetonACM.acm.org/tcfpro for list of speakers and registration information > > ----- > > PRINCETON ACM / IEEE-CS CHAPTERS > MARCH 2019 JOINT MEETING > > Bias in Face Recognition: Definitions, Magnitude, Mitigation > > As the poster-child of the AI revolution, face recognition has undergone a period of rapid improvement to the point that individuals can be readily identified when a single photograph is searched against a database containing tens of millions of faces. This task is non-trivial. Algorithms are fully proprietary and not commoditized as some are far more accurate than others. Yet all fail more or less gracefully when fed poor photographs, when a subject changes appearance with age, and on images for certain sub-populations. This latter aspect constitutes an existential threat to use of face recognition as end users, system owners, algorithm developers, and regulators struggle to understand the problem, its magnitude and impact. > > The talk will give an overview of face recognition applications and technology, then describe how and where biometric systems fail and the range of demographic influences. The talk will review recent publications, and give a preview of an upcoming government report on demographic effects in face recognition. The talk will define metrics, give extensive empirical results for contemporary leading commercial algorithms, make conjectures to explain the observations, and discuss impact and possible mitigation. > > Patrick Grother is a scientist at the National Institute of Standards and Technology responsible for biometric algorithm evaluation, and biometric performance testing standardization. He leads evaluations of face and iris recognition algorithms at NIST. Patrick has worked in image processing, pattern recognition, optics and biometrics. > > Date: Thursday March 14, 2019, 8:00pm > (refreshments at 7:30pm) > Place: Princeton University Computer Science Building > Small Auditorium, Room CS 105 > 35 Olden Street, Princeton NJ > Information: Dennis Mancl (908) 285-1066 > On-line meeting notice: http://PrincetonACM.acm.org/meetings/mtg1903.pdf > > All ACM / IEEE-CS meetings are open to the public. Students and their parents are welcome. There is no admission charge. > > A pre-meeting dinner is held at 6:00 p.m. at Ruby Tuesday's Restaurant on Route 1. Please send email to princetonacm(a)gmail.org in advance if you plan to attend the dinner. > > ===================== > > Future Princeton ACM / IEEE Computer Society Meetings > > NOTE: The location for all of our regular monthly meetings -- Computer Science Building at Princeton University. All monthly meetings are at 8pm. > > Saturday Mar. 16 - IEEE Integrated STEM Education Conference (at McDonnell Hall, Princeton University) > http://ewh.ieee.org/conf/stem > > Friday Mar. 22 - IT Professional Conference at TCF (at The College of New Jersey) > ** Registration is now open ** > $135 (Professionals), $35 (students and senior citizens) > http://PrincetonACM.acm.org/tcfpro > > Saturday Mar. 23 - Trenton Computer Festival (at The College of New Jersey) > http://www.tcf-nj.org > > Thursday Apr. 11 - "Natural Language Processing at the National Library of Medicine," Dina Demner-Fushman, National Library of Medicine > http://PrincetonACM.acm.org/meetings/mtg1904.pdf > (NOTE: this meeting is the SECOND Thursday of April) > > Thursday May 16 - Annual Dinner Meeting (at Grafton House, Hamilton Township) > > June TBA - Annual Elections and Planning Meeting

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Unsolved Data Problems: Wednesday, March 13 at 4:30
by Emily C. Lawrence 07 Mar '19

07 Mar '19

Wednesday, March 13 4:30 – 5:30 pm Computer Science, Rm 105 [ https://cdh.princeton.edu/events/2019/03/unsolved-data-problems/ | https://cdh.princeton.edu/events/2019/03/unsolved-data-problems/ ] Unsolved Data Problems will introduce faculty and students in the computer and data sciences to the untapped research possibilities inherent in humanities data. A panel of Princeton faculty - Meredith Martin (English), Marina Rustow (History and Near Eastern Studies) and Dan Trueman (Music) - will discuss some of Princeton’s landmark digital humanities projects, and the challenges they’ve faced when transforming historical, multilingual and experimental source material into data and code. Projects discussed include the Princeton Prosody Archive, the Princeton Geniza Lab, and bitKlavier . Jennifer Rexford and Brian Kernighan (Computer Science) will moderate the panel. Help discover innovative algorithmic solutions to these unsolved computational problems. This panel will be of particular interest to researchers working in the fields of: computer vision, natural language processing, machine learning, and audio/music engineering. This event is collaboratively organized by the Center for Digital Humanities, the Department of Computer Science and the Center for Statistics and Machine Learning.

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