- talks - lists.cs.princeton.edu

C Wang general exam
by Melissa Lawson 15 Jan '09

15 Jan '09

Chong Wang will present his research seminar/general exam on Wednesday Jan 21 at 2PM in Room 402. The members of his committee are: David Blei (advisor), Fei-Fei Li, and Rob Schapire. 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. -------------------------------------------- Learning topic models from multiple corpora Abstract In this talk, we consider the problem of learning topic models from multiple corpora. Examples of multi-corpora data include news articles from different times or different locations, and scientific papers from different conferences or different years. The majority of topic models, however, usually ignore this addition information. Simply combining multiple corpora into one large corpus or treating each corpus individually can't provide the ability to analyze the high-level relations among multiple corpora, e.g., are topics similar or different from time to time or from location to location? How are they related to each other? I will describe two new approaches for learning topic models from multiple corpora: continuous time dynamic topic model (cDTM) and Markov topic model(MTM). In cDTMs, documents from the same time point are considered as a corpus. The cDTM extends dynamic topic models (DTMs) by using Brownian motion to model the latent topics through the time line. We derive an efficient variational approximate inference algorithm that takes advantage of the sparsity of observations in text, a property that lets us easily handle many time points. Thus, cDTM is able to discover topic evolutions in a much finer time resolution. In MTMs, papers from the same conference are treated as a corpus. Then we apply Gaussian (Markov) random fields to model the correlations of different corpora. MTMs capture both the internal topic structure within each corpus and the relationships between topics across the corpora. In addition, we will show cDTMs and DTMs can be formulated as special cases of MTMs. Quantitative results and qualitative discoveries (interesting topic patterns) will also be presented. Books: 1) Pattern Recognition and Machine Learning, by Christopher M. Bishop, Springer, 2006 chapters: 1, 2, 3.1-3.5, 4, 5, 8, 9, 10, 11.1-11.3, 12.1-12.2, 13, 14. 2) An Introduction to Probabilistic Graphical Models (unpublished manuscript), by Michael I. Jordan, 2002 chapters: 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 14, 15 4) (Optional) Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig, Prentice Hall Series in Artificial Intelligence, 2003 chapters: 3.1-3.5, 4.1-4.3, 13, 14, 15.1-15.5, 18.1-18.3, 20.1-20.5, 23.2-23.3 Papers: 1) D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. JMLR, 3:993-1022, 2003. 2) D. Blei and J. Lafferty. Dynamic topic models. In ICML, 2006. 3) T. Griffiths and M. Steyvers. Finding scientific topics. Proc Natl Acad Sci USA, 101 Suppl 1:5228-5235, April 2004. 4) C. Andrieu, N. de Freitas, A. Doucet, and M. Jordan. An introduction to MCMC for machine learning. Machine Learning, 50(1):5-43, January 2003. 5) M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul. An introduction to variational methods for graphical models. Machine Learning, 37(2):183-233, 1999. 6) Y. Teh, M. Jordan, M. Beal, and D. Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 2006. 7) L. Fei-Fei and P. Perona. A Bayesian hierarchical model for learning natural scene categories. In CVPR, 2005. 8) Havard Rue and Turid Follestad, Gaussian Markov Random field models with applications in spatial statistics, preprint, 2003 9) D. Blei and J. Lafferty. Correlated topic models. In NIPS, 2005. 10) R. Kalman. A new approach to linear filtering and prediction problems. Transaction of the AMSE: Journal of Basic Engineering, 82:35-45, 1960. 11) L. Ruschendorf. Convergence of the iterative proportional fitting procedure. The Annals of Statistics, 23(4):1160-1174, 1995. 12) X. Wang and A. McCallum. Topics over time: a non-Markov continuous-time model of topical trends. In KDD, 2006.

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S Jafarpour general exam
by Melissa Lawson 09 Jan '09

09 Jan '09

Sina Jafarpour will present his research seminar/general exam on Friday January 16 at 2:30 PM in Room 402. The members of his committee are: Rob Calderbank (advisor), Rob Schapire, and Andrea LaPaugh. 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. ------------------------------------------- The magic of sparsity. Joint work with Robert Calderbank, Robert Schapire, and Ingrid Daubechies. Compressed Sensing is a novel data acquisition method suitable when either the measurement or the compression is costly. The traditional approach to compressed sensing uses convex optimization methods, which are based on some "near isometry" property of the sensing matrix. This approach is costly in terms of the recovery time as well as the required storage. In this talk, we present two new efficient sensing methods. We first show how expander graphs are appropriate for compressed sensing in terms of providing explicit and efficient sensing matrices as well as a simple and efficient recovery algorithm. Our second result provides efficient deterministic sensing. We show very simple conditions a matrix should satisfy in order to satisfy statistical restricted isometry property. This leads to deterministic sensing matrices in which the recovery time only depends on the number of measurements and might even bypass the random sensing lower bounds.. Finally, we show how compressed sensing can be regarded as a universal sparse dimensionality reduction technique. Especially, we provide tight bounds, demonstrating that the soft-margin SVM classifier in compressed domain is almost as accurate as the best classifier in high dimension. This is beneficial in the compressed sensing point of view by reducing the recovery time, and also in the machine learning point of view by bypassing the curse of dimensionality. Books: ====== 1. S. Russell and P. Norvig. Artificial Intelligence: A modern approach. Chapters: 3.1-3.5 4.1-4.3 6 7.1-7.6 11.1, 11.5 8.1-8.3 13.1-13.6 14.1-14.5 15.1-15.6 16.1-16.3 17.1-17.4 18.1-18.5 20.1-20.3,20.5-20.6 21.1-21.4 2. Lecture notes of Computer Science 511: Theoretical Machine learning, by Robert Schapire, spring 2008. 3. S. Boucheron1, O. Bousquet and G. Lugosi, Theory of classification, a survey of some recent advances, ESAIM: Probability and Statistics 9, 323 - 375, 2005. http://www.econ.upf.edu/~lugosi/esaimsurvey.pdf 4. S. Boyd and L. Vandenberghe, Convex Optimization. Chapters: 2.1-2.3 3.1.1-3.2 4.1-4.4 5.1-5.2 5.4-5.5 Papers: ======= 1. C. Burges. A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):955-974, 1998. http://www.umiacs.umd.edu/~joseph/support-vector-machines4.pdf 2. S. Shalev-Shwartz and N. Srebro. SVM Optimization: Inverse Dependence on Training set size. ICML, 2008. http://icml2008.cs.helsinki.fi/papers/266.pdf 3. A. Blum. Random Projection, Margins, Kernels, and Feature-Selection. LNCS vol 3940, pp. 52--68, 2006. www.cs.cmu.edu/~avrim/Papers/randomproj.pdf 4. R. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297-336, 1999. http://www.cs.princeton.edu/~schapire/uncompress-papers.cgi/SchapireSi98.ps 5. R. Baraniuk, Compressive sensing. IEEE Signal Processing Magazine, 24(4), pp. 118-121, July 2007 http://www.dsp.ece.rice.edu/cs/baraniukCSlecture07.pdf 6. E. Candès and M. Wakin, An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2), pp. 21 - 30, March 2008. http://www.dsp.ece.rice.edu/cs/CSintro.pdf 7. R. Berinde and P. Indyk, Sparse recovery using sparse random matrices. Preprint, 2008. http://people.csail.mit.edu/indyk/report.pdf 8. R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, A simple proof of the restricted isometry property for random matrices using Johnson-Lindenstrauss Lemma. To appear in Constructive Approximation. http://www.dsp.ece.rice.edu/cs/JL_RIP.pdf 9. V. Chandar, A negative result concerning explicit matrices with the restricted isometry property. Preprint, 2008 http://www.dsp.ece.rice.edu/cs/Venkat_CS.pdf 10. L. Applebaum, S. Howard, S. Searle, and R. Calderbank, Chirp sensing codes: Deterministic compressed sensing measurements for fast recovery. Applied and Computational Harmonic Analysis, September 2008. http://www.dsp.ece.rice.edu/cs/chirpCode.pdf 11.S. D. Howard, A. R. Calderbank, and S. J. Searle, A fast reconstruction algorithm for deterministic compressive sensing using second order Reed-Muller codes. (Conf. on Info. Sciences and Systems (CISS), Princeton, New Jersey, March 2008) http://www.dsp.ece.rice.edu/cs/HCS_CISS_2008.pdf 12.W. Xu and B. Hassibi, Efficient compressive sensing with deterministic guarantees using expander graphs. http://www.dsp.ece.rice.edu/cs/cs-expander2.pdf

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W Clarkson general exam
by Melissa Lawson 08 Jan '09

08 Jan '09

Will Clarkson will present his research seminar/general exam on Thursday January 15 at 2PM in Room 402. The members of his committee are; Ed Felten (advisor), Adam Finkelstein, and Andrew Appel. 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: I will discuss a new technique for authenticating physical documents by using random, naturally occurring imperfections in paper texture. I will show how the three-dimensional surface of a page can be measured using only a commodity scanner and without modifying the document in any way. Using measurements of this physical feature, a concise fingerprint is generated that uniquely identifies the document. This method is secure against counterfeiting, robust to harsh handling, and applicable even before any content is printed on a page. It has a wide range of applications, including detecting forged currency and tickets, authenticating passports, and halting counterfeit goods. On a more sinister note, document identification could be used to de-anonymize printed surveys and to compromise the secrecy of paper ballots. Books: 1. R. J. Anderson. Security Engineering: A Guide to Building Dependable Distributed Systems. Wiley Publishing, 2008. Selected Sections/Chapters: Part 1: All, Part 2:11-16 (Security Printing and Seals, Biometrics, Physical Tamper Resistance) 2. N. Ferguson and B. Schneier. Practical Cryptography. John Wiley & Sons, Inc., New York, NY, USA, 2003. Papers: 1. B. Zhu, J. Wu, and M. S. Kankanhalli. Print signatures for document authentication. In Proc. 10th ACM Confernce on Computer and Communications Security, pages 145-154, 2003. 2. E. Metois, P. Yarin, N. Salzman, and J. R. Smith. FiberFingerprint identification. In Proc. Third Workshop on Automatic Identification, pages 147-154, 2002. 3. A. Juels and M. Wattenberg. A fuzzy commitment scheme. pages 28-36. ACM Press, 1999. 4. Y. Dodis, R. Ostrovsky, L. Reyzin, and A. Smith. Fuzzy extractors: How to generate strong keys from biometrics and other noisy data. SIAM Journal on Computing, 38(1):97-137, 2008. 5. N. R. Wagner. Fingerprinting. In SP '83: Proceedings of the 1983 IEEE Symposium on Security and Privacy, page 18, Washington, DC, USA, 1983. IEEE Computer Society. 6. N. Degara-Quintela and F. Perez-Gonzalez. Visible encryption: using paper as a secure channel. volume 5020, pages 413-422. SPIE, 2003. 7. R. P. Cowburn, J. D. R. Buchanan, and P. R. Seem. Scanner authentication. US patent application 2008/0044096, June 2007. 8. I. Amidror. A new print-based security strategy for the protection of valuable documents and products using moire intensity profiles. In Proceedings of the SPIE - The International Society for Optical Engineering, volume 4677 of Proc. SPIE - Int. Soc. Opt. Eng. (USA), pages 89-100. SPIE-Int. Soc. Opt. Eng, 2002. Lab. de Systemes Peripheriques (LSP), Ecole Polytech. Fed. de Lausanne, Switzerland. 9. J. Daugman. High confidence visual recognition of persons by a test of statistical independence. In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 15, pages 1148-1161, November 93. 10. David J. Hurley, B. Arbab-Zavar, and Mark A. Nixon. The Ear as a Biometric, November 2007.

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H Kim general exam
by Melissa Lawson 31 Dec '08

31 Dec '08

Hanjun Kim will present his research seminar/general exam on Tuesday January 6 at 2PM in Room 402. The members of his committee are: David August (advisor), Margaret Martonosi, and Doug Clark. 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. --- Software Multithreaded Transactional Memory Abstract As chip multiprocessors have become ubiquitous, programs must be parallelized to effectively use computing resources. Recently proposed techniques, such as those by Bridges et al., by Theis et al., and by Tian et al., have demonstrated significant performance improvement by partitioning loops into long-running threads organized into a pipeline. Various studies have shown that speculation is the key to unlocking the full potential of these techniques for general purpose applications. Speculative pipelined multi-threading needs multi-threaded transactional memory that supports atomic units of code across multiple threads, but most transactional memory proposals cannot be used because they only support single-threaded atomic units. Multi-threaded transactions (MTX) address this problem, but they require expensive hardware support as currently proposed. Tian et al. offer CorD, a software technique which runs on existing hardware. CorD's limitations, that it does not support loops that speculatively write pointer addresses and that it only supports certain patterns of parallelism, restrict its utility. This work proposes a software MTX (SMTX) system that combines the applicability of hardware MTX with CorD's performance on real hardware. Across a set of sequential applications, SMTX yields a geomean speedup of 353% on a real dual 4-core CMP (8 cores in total) machine running speculatively parallelized applications. Reading List Books: [1] Modern Compiler Implementation in ML, by A. W. Appel, Cambridge University Press, 1998 [2] Computer Architecture: A Quantitative Approach 4th Edition, by J. L. Hennessy & D. A. Patterson, Morgan Kaufmann, 2006 Paper: [3] K. Agrawal, J. Fineman, and J. Sukha. Nested parallelism in transactional memory. In Proceedings of the Second ACM SIGPLAN Workshop on Transactional Computing, August 2007. [4] M. J. Bridges, N. Vachharajani, Y. Zhang, T. Jablin, and D. I. August. Revisiting the sequential programming model for multi-core. In Proceedings of the 40th Annual ACM/IEEE International Symposium on Microarchitecture, December 2007. [5] J. Giacomoni, T. Moseley, and M. Vachharajani. FastForward for efficient pipeline parallelism: a cache-optimized concurrent lock-free queue. In PPoPP '08: Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming, 2008. [6] L. Hammond, B. D. Carlstrom, V. Wong, M. Chen, C. Kozyrakis, and K. Olukotun. Transactional coherence and consistency: Simplifying parallel hardware and software. IEEE Micro, 24(6), Nov-Dec 2004. [7] M. Herlihy and J. E. B. Moss. Transactional memory: Architectural support for lock-free data structures. In Proceedings of the 20th Annual International Symposium on Computer Architecture, 1993. [8] L. Lamport. Specifying concurrent program modules. ACM Trans. Program. Lang. Syst., 5(2):190-222, 1983. [9] S. Papadimitriou and T. C. Mowry. Exploring thread-level speculation in software: The effects of memory access tracking granularity. Technical report, 2001. [10] E. Raman, G. Ottoni, A. Raman, M. Bridges, and D. I. August. Parallel-Stage Decoupled Software Pipelining. In Proceeding of the 2008 International Symposium on Code generation and Optimization, April 2008. [11] R. Rangan, N. Vachharajani, M. Vachharajani, and D. I. August. Decoupled software pipelining with the synchronization array. In Proceedings of the 13th International Conference on Parallel Architectures and Compilation Techniques, September 2004. [12] J. G. Steffan, C. B. Colohan, A. Zhai, and T. C. Mowry. A scalable approach to thread-level speculation. In Proceedings of the 27th International Symposium on Computer Architecture, June 2000. [13] H. Sutter. The free lunch is over: A fundamental turn toward concurrency in software. Dr. Dobb's Journal, 30(3), 2005. [14] W. Thies, V. Chandrasekhar, and S. Amarasinghe. A practical approach to exploiting coarse-grained pipeline parallelism in C programs. In Proceedings of the 40th Annual ACM/IEEE International Symposium on Microarchitecture, 2007. [15] C. Tian, M. Feng, V. Nagarajan, and R. Gupta. Copy or discard execution model for speculative parallelization on multicores. In Proceedings of the 41st International Symposium on Microarchitecture, November 2008. [16] N. Vachharajani, R. Rangan, E. Raman, M. J. Bridges, G. Ottoni, and D. I. August. Speculative decoupled software pipelining. In Proceedings of the 16th International Conference on Parallel Architectures and Compilation Techniques, September 2007.

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1-2pm Fri Dec 12 talk in B-205 on wireless interference by Ramakrishna Gummadi
by Jennifer Rexford 12 Dec '08

12 Dec '08

Highly recommended for folks interested in randomized algorithms, networking, or systems. Will also include a demo of their FPGA-based programmable wireless system... Speaker: Dr. Ramakrishna Gummadi, MIT Title: Interference rendered significantly harmless Location: B205 in the E-Quad (note: talk in ELE wing of E-Quad, *not* in CS building) Date/time: 1-2pm Friday December 12 Abstract: The throughput of existing wireless networks is often limited by interference. One fundamental reason is that the current designs are constrained by a "one-transmission-at-a-time" model at the link layer and a fixed-width spectrum allocation at the physical layer. We present a new wireless design that exploits traffic burstiness and node heterogeneity, thereby improving concurrency and spectrum usage. The main challenge is the unmanaged nature of many wireless networks such as 802.11 and mesh, which makes centralized resource allocation impractical. We show through analysis and implementation that simple randomized allocation policies can overcome this challenge, and improve throughput by 2x or more. This work is joint with Rabin Patra, Hari Balakrishnan and Eric Brewer. Bio: Ramakrishna Gummadi is a post-doc at the MIT Computer Science and Artificial Intelligence Laboratoray (CSAIL). He obtained his B.Tech. from IIT-Madras, M.S. from UC Berkeley and Ph.D. from USC, all in Computer Science. His dissertation was about reliable and efficient programming languages for sensor networks. He is interested in building scalable and reliable systems and networks based on sound principles. His awards include a UC Regents Graduate Fellowship, a best paper awarded out of all 2001 Journal of Computer Networks papers, a best poster/demo award at SenSys 2004, and an award at the ACM Student Research Competition (SRC) held at PLDI 2007.

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Distinguished colloquium today: Eva Tardos
by Sanjeev Arora 11 Dec '08

11 Dec '08

In Room 105 of CS Building, 4:30pm. --------- Games in Networks: the price of anarchy and learning Eva Tardos Cornell Network games play a fundamental role in understanding behavior in many domains, ranging from communication networks through markets to social networks. Such networks are used, and also evolve due to selfish behavior of the users and owners. In light of these competing forces, it is surprising how efficient these networks are. It is an exciting challenge to understand the operation and success of these networks in game theoretic terms: what principles of interaction lead selfish participants to form such efficient networks? We will focus on congestion games, and study the degradation of quality of solution caused by the selfish behavior of users. We model users as learning algorithms, and show that natural learning behavior can avoid bad outcomes predicted by the price of anarchy in atomic congestion games such as the load-balancing game. We use tools from the theory of dynamical systems and algebraic geometry to show when players use a class of natural learning algorithms the distribution of play converges to the set of weakly stable equilibria, and that the set of weakly stable equilibria are the pure Nash equilibria with probability 1 when congestion costs are selected at random independently on each edge (from any monotonically parametrized distribution). The talk is a survey and self-contained. ----- About the speaker: Eva Tardos is a Professor of Computer Science at Cornell University where she is currently chair. Her research is in Algorithm Design and Algorithmic Game Theory. Algorithmic game theory is an emerging new area of designing systems and algorithms for selfish users. She is a winner of the Fulkerson Prize and the Dantzig Prize.

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[Fwd: Lunch 'n Learn today at noon: Computational Intractability]
by Scott Karlin 10 Dec '08

10 Dec '08

-------- Original Message -------- Subject: Lunch 'n Learn today at noon: Computational Intractability Date: Wed, 10 Dec 2008 09:39:42 -0500 From: Jon Edwards <jedwards(a)Princeton.EDU> Reply-To: Jon Edwards <jedwards(a)Princeton.EDU> To: EOS-ANNOUNCE(a)Princeton.EDU Lunch 'n Learn banner Wednesday, December 10, 12:00 noon Computational Intractability: A Barrier for Computers, Man, and Science Sanjeev Arora Computers are increasingly important in society as well as every area of scholastic inquiry. Nevertheless, there appear to be tasks that are infeasible for current computers. In recent decades, we have begun to develop an understanding of what makes computational tasks "intractable" not just for current computers but for all foreseeable computers. This has implications for many scientific fields. The study of intractability can also yield positive benefits, such as cryptographic schemes, secure e-voting, and e-commerce. Recently the National Science Foundation decided to fund an "Expedition" center headquartered at Princeton that will study computational intractability. I will give an overview of both the field and what our center is trying to accomplish. *Sanjeev Arora* is a Professor of Computer Science and the Director of the Center for Theoretical Computer Science and the Director of the Center for Intractability. Most Lunch 'n Learn presentations take place in *Frist Multipurpose Room B*. Cookies and beverages are provided. /Bring your lunch and learn! / *Additional news:* facebookFacebook users take note. Lunch 'n Learn now has a Facebook page <http://www.facebook.com/pages/Princeton-NJ/Lunch-n-Learn-Information-Techno…>. Become a fan to keep up with our activities. For other Education and Outreach services offerings, visit our web site <http://www.princeton.edu/%7Eeos>.

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Talk on Thursday -12/11/2008
by Ginny Hogan 09 Dec '08

09 Dec '08

Eva Tardos, Cornell University will present her Talk. "Games in Networks: the price of anarchy and learning"- at 4:15pm Computer Science Room 105.

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H Ringberg preFPO - correction
by Melissa Lawson 08 Dec '08

08 Dec '08

The committee should be; Jennifer Rexford, Mike Freedman, Nicholas Duffield (AT&T), Rob Calderbank, and Brian Kernighan. --Melissa

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H Ringberg preFPO
by Melissa Lawson 08 Dec '08

08 Dec '08

Haakon Ringberg will present his preFPO on Friday December 12 at 10AM in Room 302. The members of his committee are: Jennifer Rexford, advisor; Nicholas Duffield (AT&T), Mike Freedman, Vivek Pai, and Rob Calderbank. Everyone is invited to attend his talk. His abstract follows below. ---------------------------------------------- Unwanted traffic is a major concern in the Internet today. The algorithms that attempt to identify this unwanted traffic are called network anomaly detectors, and their task is challenging in part because many network attacks mimic the behavior of normal traffic. The victims of these attacks should therefore collaborate by correlating their attack data, under the assumption that a given malicious host is likely to affect more than one victim. The major obstacle to such collaboration is that the corporations who manage these networks are hesitant to openly share information about the hosts (customers) that use their services. In this thesis, we propose and evaluate the feasibility of an architecture for privacy-preserving collaborative anomaly detection. First, we study the potential gain from collaboration on traces from operational networks. We do this by calculating the fraction of detected network anomalies (viz., IP scans, port scans, and DoS attacks) that could have been mitigated in these traces if some subset of the victim hosts collaborated to block attackers. Second, we propose and evaluate the efficiency of a novel cryptographic protocol that allows victims to collaborate to identify the hosts sending unwanted traffic. The protocol allows each participant to submit a set of IP addresses that they suspect might be engaging in said activity. The protocol preserves privacy because it never reveals who suspected whom, and a submitted IP address is only revealed when more than n participating networks suspect it. Finally, we close the loop by evaluating the feasibility of making a commonly-used single vantage-point anomaly detector faster and more scalable. Specifically, we propose and evaluate a system that leverages machine learning to bring the classification intelligence of the signature- and packet-based intrusion detection system (IDS) Snort to the IP flow realm. We demonstrate that our system can effectively learn to classify many Snort alarms on IP flows. In sum, the thesis presents a complete collaborative anomaly detection architecture, from the high-confidence detection of unwanted traffic at single networks to a privacy-preserving protocol that allows these networks to collectively increase their confidence in their detections.

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