- talks - lists.cs.princeton.edu

Sophie Qiu will present her MSE talk, "Streaming data analytics and visualization: An illustrative example" on Monday, May 08, 2017 at 11am in CS 301
by Nicki Gotsis 02 May '17

02 May '17

Sophie Qiu will present her MSE talk, "Streaming data analytics and visualization: An illustrative example" on Monday, May 08, 2017 at 11am in CS 301. The members of her committee are Jen Rexford (adviser) and Walter Willinger (Niksun). Everyone is invited to attend her talk. Her abstract follows below. The emergence of streaming data or “data in motion” has motivated the development of new “streaming” algorithms that provide up-to-date answers to continuous queries; that is, queries that are issued once and then run continuously as new data streams in. For example, in the context of network traffic management, continuous queries over streaming Netflow data may be used to detect anomalies in the network as they happen (e.g., performance degradation, onset of an attack). The main objective of this thesis is to demonstrate with an illustrative example how the continuous stream of answers produced by existing streaming algorithms can be visualized in an effective and meaningful manner. In particular, such visualization efforts should be able to capture the dynamics that is inherent in the answers that result from such continuous queries. Our illustrative example concerns the class of frequent itemset mining algorithms for streaming data, a generalization of the well-know algorithms for finding frequent items (e.g., top-k) in streaming data. Using the output of these streaming algorithms as input, our proposed visualization method c ombines two previously-considered types of flow diagrams (i.e., sankey and alluvial diagrams) to depict the temporal evolution of frequent itemsets. We apply our visualization method to real-world streaming data (e.g., unsampled Netflow data from a stub network, DNS records from a university network) and illustrate its basic features and capabilities.

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Jordan Ash will present his general exam on Tuesday, May 9th, 2017 at 10am in CS 401.
by Nicki Gotsis 02 May '17

02 May '17

Jordan Ash will present his general exam on Tuesday, May 9th, 2017 at 10am in CS 401. The members of his committee are: Barbara Engelhardt (adviser), Sebastian Seung, and Tom Funkhouser. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. Abstract: Domain adaptation addresses the problem created when training data is generated by a so-called source distribution, but test data is generated by a significantly different target distribution. In this work, we present approximate label matching (ALM), a new unsupervised domain adaptation technique that creates and leverages a rough labeling on the test samples, then uses these noisy labels to learn a transformation that aligns the source and target samples. We show that the transformation estimated by ALM has favorable properties compared to transformations estimated by other methods, which do not use any kind of target labeling. Our model is regularized by requiring that a classifier trained to discriminate source from transformed target samples cannot distinguish between the two. We experiment with ALM on simulated and real data, and show that it outperforms techniques commonly used in the field. Keywords: Generative Adversarial Networks, Domain Adaptation, Deep Learning Reading List: 1. Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. "Learning representations by back-propagating errors." Cognitive modeling (1988) 2. LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998): 2278-2324. 3. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444. 4. Ganin, Yaroslav, et al. "Domain-adversarial training of neural networks." Journal of Machine Learning Research 17.59 (2016): 1-35. 5. Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014. 6. Denton, Emily L., Soumith Chintala, and Rob Fergus. "Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks." Advances in neural information processing systems. 2015. 7. Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015). 8. Reed, Scott, et al. "Learning to disentangle factors of variation with manifold interaction." Proceedings of the 31st International Conference on Machine Learning (ICML-14). 2014. 9. Liu, Ming-Yu, and Oncel Tuzel. "Coupled generative adversarial networks." Advances in Neural Information Processing Systems. 2016. 10. Mathieu, Michael, Camille Couprie, and Yann LeCun. "Deep multi-scale video prediction beyond mean square error." arXiv preprint arXiv:1511.05440 (2015). 11. Lamb, Alex M., et al. "Professor forcing: A new algorithm for training recurrent networks." Advances In Neural Information Processing Systems. 2016.

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Ari Seff will present his general exam on Monday, May 8th at 9am in CS 302
by Nicki Gotsis 01 May '17

01 May '17

Ari Seff will present his general exam on Monday, May 8th at 9am in CS 302. The members of his committee are: Han Liu (adviser), Thomas Funkhouser, and Barbara Engelhardt. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. Title: Continual Learning in Deep Generative Models Abstract: Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions. Recently introduced frameworks, such as generative adversarial networks and variational autoencoders, can map a noise distribution to the data space, producing realistic samples. In addition, these deep generative models are amenable to conditional training, where a conditional input (e.g., class label, data from another modality) guides the sampling process. At test time, manually selected conditional inputs lead to samples with desired attributes on demand. However, the standard training regime for deep generative models assumes that training data is i.i.d with respect to the conditional inputs. For example, when class labels serve as the conditional inputs, if we wish to extend the capability of a trained network to a new class, training solely on data from the new class will interfere with previously learned distributions. This is due to the general susceptibility of neural networks to catastrophic forgetting when trained on multiple tasks sequentially. Instead, the standard training regime requires that while the network trains on the new class, it simultaneously retrains on the old to circumvent this phenomenon. In this work, we propose a scalable approach to training deep generative models on multiple tasks sequentially, where training data for any previous task (a set of target conditional inputs) is assumed to be inaccessible. To this end, we leverage recent work with discriminative models where plasticity is reduced for network parameters determined to be salient for previously encountered tasks. Experimental evaluation of our approach demonstrates that deep generative models may be extended to settings where the observed data distribution changes over time, enabling continual learning of novel generative tasks. Reading List: Books: I. Goodfellow, Y. Bengio, and A. Courville: Deep learning Papers: Y. LeCun, J. S. Denker, and S. A. Solla: Optimal brain damage I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio: Generative adversarial nets D. P. Kingma and M. Welling: Auto-encoding variational bayes J. Kirkpatrick, R. Pascanu, N. C. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell: Overcoming catastrophic forgetting in neural networks M. McCloskey and N. J. Cohen: Catastrophic interference in connectionist networks: The sequential learning problem M. Mirza and S. Osindero: Conditional generative adversarial nets K. Sohn, H. Lee, and X. Yan: Learning structured output representation using deep conditional generative models F. Zenke, B. Poole, and S. Ganguli: Improved multitask learning through synaptic intelligence D. Hoiem and Z. Li: Learning without forgetting A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell: Progressive neural networks

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FPO ANNOUNCEMENT - ALI JAVADIABHARI
by Nicki Gotsis 01 May '17

01 May '17

Department of Electrical Engineering Final Public Oral Examination of Ali JavadiAbhari (advisor: Prof. Margaret Martonosi) Towards a Scalable Software Stack for Resource Estimation and Optimization in General-Purpose Quantum Computers Monday, May 8, 2017 Room J-401 Engineering Quadrangle 10:00 AM All those interested are welcome to attend. A copy of the thesis is on display in the F-wing You can read the thesis abstract here “Please click on the attached .ics file to add this event to your calendar.”

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Physics 42nd annual Hamilton Lecture: Thurs. May 4; Charles Kane (U.Penn), “Topological Phases of Matter,” 8 p.m., McDonnell A-02
by Nicole E. Wagenblast 28 Apr '17

28 Apr '17

Princeton University Department of Physics 42 nd annual Donald R. Hamilton Lecture Charles Kane University of Pennsylvania “ Topological Phases of Matter ” Matter can arrange itself in the most ingenious ways. In addition to the solid, liquid and gas phases that are familiar in classical physics, quantum mechanics enables the existence of electronic phases of matter that can have both exotic and useful properties. In the last century, the thorough understanding of the simplest quantum electronic phase - the electrical insulator - enabled the development of the solid state electronics technology that is ubiquitous in today's information age. In the present century, new "topological" electronic phases are being discovered that may enable future technologies by allowing the seemingly impossible to occur: indivisible objects, like an electron or a quantum bit of information, can be split into two, allowing mysterious features of quantum mechanics to be harnessed. Our understanding of topological phases, which was celebrated by the 2016 Nobel Prize in physics, builds on deep ideas in mathematics. We will try to convey that they are as beautiful as they are fundamental. 8 p.m., Thursday, May 4, 2017 McDonnell Hall, Auditorium A-02 Host: Paul Steinhardt, Princeton Center for Theoretical Science A free lecture open to the public. Princeton University Featured Event Save

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Yingfei Wang will present her FPO, "Advances in decision-making under uncertainty: inference, finite-time analysis, and health applications" on Tuesday, 5/9/2017 at 1pm in CS 302.
by Nicki Gotsis 27 Apr '17

27 Apr '17

Yingfei Wang will present her FPO, "Advances in decision-making under uncertainty: inference, finite-time analysis, and health applications" on Tuesday, 5/9/2017 at 1pm in CS 302. The members of her committee are: Adviser: Warren Powell (ORFE) Readers: Bernard Chazelle and Mengdi Wang (ORFE) Examiners: Szymon Rusinkiewicz, Han Liu (ORFE) and Warren Powell (ORFE) A copy of her thesis is available in Room 310. Everyone is invited to attend her talk. The talk abstract follow below: This thesis considers the problem of sequentially making decisions under uncertainty, exploring the ways where efficient information collection influences and improves decision-making strategies. Most previous optimal learning approaches are restricted to fully sequential settings with Gaussian noise models where exact analytic solutions can be easily obtained. In this thesis, we bridge the gap between statistics, machine learning and optimal learning by providing a comprehensive set of techniques that span from designing appropriate stochastic models to describe the uncertain environment, to proposing novel statistical models and inferences, to finite-time and asymptotic guarantees, with an emphasis on how ecient information collection can expand access, decrease costs and improve quality in health care. Specifically, we provide the first finite-time bound for the knowledge gradient policy. Since there are many situations where the outcomes are dichotomous, we consider the problem of sequentially making decisions that are rewarded by “successes” and “failures”. The binary outcome can be predicted through an unknown relationship that depends on partially controllable attributes of each instance. With the adaptation of an online Bayesian linear classifier, we design a knowledge gradient (KG) policy to guide the experiment. Motivated by personalized medicine where a treatment regime is a function that maps individual patient information to a recommended treatment, hence explicitly incorporating the heterogeneity in need for treatment across individuals, we further extend our knowledge gradient policy to a Bayesian contextual bandits setting. Since the sparsity and the relatively small number of patients make leaning more dicult, we design an ensemble optimal learning method, in which multiple models are strategically generated and combined to minimize the incorrect selection of a particularly poorly performing statistical model. Driven by numerous needs among materials science society, we developed a KG policy for sequential experiments when experiments can be conducted in parallel and/or there are multiple tunable parameters which are decided at di↵erent stages in the process. Finally, we present a new Modular, Optimal Learning Testing Environment (MOLTE) as a public-domain test environment to facilitate the process of more comprehensive comparisons, on a broader set of test problems and a broader set of policies.

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Talk: Building a new decentralized internet with Blockstack- Today 1:30pm
by Nicole E. Wagenblast 26 Apr '17

26 Apr '17

Time : 1:30pm to 2:50pm Location : Friend 003 Title : Building a new decentralized internet with Blockstack Abstract : The internet was designed more than 30 years ago and is showing signs of age. Recent attacks on internet infrastructure, like DDoS attacks on the Domain Name System (DNS), and cases of fraudulent security certificates issued by certificate authorities (CAs) have exposed the ugly underbelly of the internet along with flaws in the internet's original design. Centralized services and hidden middlemen introduce points of failure and control. In this talk, we present the design and implementation of a new internet architecture that explicitly follows the trust-to-trust design principle and remotes any centralized point of trust from the middle of the network. More specifically, we present the design of (1) a decentralized naming and PKI system, (2) a decentralized storage system that gives comparable performance to traditional cloud storage services and (3) discuss how blockchains can be used to bootstrap trust in these new decentralized systems. Further, we present our lessons from operating one of the largest production deployments of blockchain-based systems, with over 70,000 domains registered, and discuss how our experiences helped evolve our design over time. Our new system, called Blockstack, is released as open-source and has an open-source community of 5000+ members. Bio : Muneeb Ali is the co-founder of Blockstack, a new decentralized internet where users own their data and apps run locally without remote servers. Muneeb is an alumnus of Y Combinator and has raised $5.1M from Union Square Ventures, Lux Capital, Naval Navikant, and others for his startup. Muneeb is also a final-year PhD candidate at Princeton University, where he has worked in the Systems and Networks group and at PlanetLab. He helped start a new course at Princeton on “How to be a CTO” and gives guest lectures on cloud computing at Princeton. Muneeb has been awarded a J. William Fulbright Fellowship and has served as the Program Co-Chair of ACM NSDR. He has built a wide range of production systems and published research papers with over 900 citations.

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Colloquium Speaker: Po-Ru Loh, Today, 12:30pm
by Nicole E. Wagenblast 25 Apr '17

25 Apr '17

Colloquium Speaker Po-Ru Loh, Harvard University Tuesday, April 25, 2017 - 12:30pm Computer Science 105 Host: Prof. Mona Singh Fast statistical methods for big data genetics As genomic data sizes have grown exponentially over the past decade, efficient statistical analysis has become a key challenge in quantitative genetics. I will describe three ongoing research thrusts in the fields of mixed model analysis, haplotype phasing and imputation, and somatic structural variant detection, each taking place at the intersection of mathematics, computer science, and genetics. I am currently a Postdoctoral Research Associate in Statistical Genetics at the Harvard T.H. Chan School of Public Health working with Dr. Alkes Price. The broad aim of my research is to develop efficient computational algorithms to analyze very large genetic data sets. My current areas of focus include (1) fast algorithms for genotype phasing and imputation, (2) linear mixed model methods for heritability analysis, association testing, and risk prediction, and (3) computational phase-based detection of pre-cancerous mosaic chromosomal aberrations. Several of my research projects have resulted in software packages now in use by the genetics community. Prior to joining the Harvard Chan School in 2013, I received my Ph.D. in Applied Mathematics from the Massachusetts Institute of Technology and my B.S. in Mathematics from the California Institute of Technology.

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Colloquium Speaker: Po-Ru Loh, Tueseday, April 25th, 12:30pm- reminder
by Nicole E. Wagenblast 24 Apr '17

24 Apr '17

Colloquium Speaker Po-Ru Loh, Harvard University Tuesday, April 25, 2017 - 12:30pm Computer Science 105 Host: Prof. Mona Singh Fast statistical methods for big data genetics As genomic data sizes have grown exponentially over the past decade, efficient statistical analysis has become a key challenge in quantitative genetics. I will describe three ongoing research thrusts in the fields of mixed model analysis, haplotype phasing and imputation, and somatic structural variant detection, each taking place at the intersection of mathematics, computer science, and genetics. I am currently a Postdoctoral Research Associate in Statistical Genetics at the Harvard T.H. Chan School of Public Health working with Dr. Alkes Price. The broad aim of my research is to develop efficient computational algorithms to analyze very large genetic data sets. My current areas of focus include (1) fast algorithms for genotype phasing and imputation, (2) linear mixed model methods for heritability analysis, association testing, and risk prediction, and (3) computational phase-based detection of pre-cancerous mosaic chromosomal aberrations. Several of my research projects have resulted in software packages now in use by the genetics community. Prior to joining the Harvard Chan School in 2013, I received my Ph.D. in Applied Mathematics from the Massachusetts Institute of Technology and my B.S. in Mathematics from the California Institute of Technology.

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Colloquium Speaker: Po-Ru Loh, Tueseday, April 25th, 12:30pm
by Nicole E. Wagenblast 20 Apr '17

20 Apr '17

Colloquium Speaker Po-Ru Loh, Harvard University Tuesday, April 25, 2017 - 12:30pm Computer Science 105 Host: Prof. Mona Singh Fast statistical methods for big data genetics As genomic data sizes have grown exponentially over the past decade, efficient statistical analysis has become a key challenge in quantitative genetics. I will describe three ongoing research thrusts in the fields of mixed model analysis, haplotype phasing and imputation, and somatic structural variant detection, each taking place at the intersection of mathematics, computer science, and genetics. I am currently a Postdoctoral Research Associate in Statistical Genetics at the Harvard T.H. Chan School of Public Health working with Dr. Alkes Price. The broad aim of my research is to develop efficient computational algorithms to analyze very large genetic data sets. My current areas of focus include (1) fast algorithms for genotype phasing and imputation, (2) linear mixed model methods for heritability analysis, association testing, and risk prediction, and (3) computational phase-based detection of pre-cancerous mosaic chromosomal aberrations. Several of my research projects have resulted in software packages now in use by the genetics community. Prior to joining the Harvard Chan School in 2013, I received my Ph.D. in Applied Mathematics from the Massachusetts Institute of Technology and my B.S. in Mathematics from the California Institute of Technology.

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