October 2017 - talks - lists.cs.princeton.edu

Distinguished Colloquium Speaker Jeff Heer, Monday, November 6th - 12:30pm
by Jennifer Cirillo 31 Oct '17

31 Oct '17

Distinguished Colloquium Speaker Professor Jeffrey Michael Heer, University of Washington Monday, November 6, 2017 - 12:30pm Computer Science - Room 105 Host: Adam Finkelstein Data analysis is a complex process with frequent shifts among data formats, tools and models, as well as between symbolic and visual thinking. How might the design of improved tools accelerate people's exploration and understanding of data? Covering both interactive demos and principles from academic research, this talk will examine how to craft a careful balance of interactive and automated methods, combining concepts from data visualization, machine learning, and computer systems to design novel interactive analysis tools. Jeffrey Heer is an Associate Professor of Computer Science & Engineering at the University of Washington, where he directs the Interactive Data Lab and conducts research on data visualization, human-computer interaction and social computing. The visualization tools developed by Jeff and his collaborators (Vega, D3.js, Protovis, Prefuse) are used by researchers, companies, and thousands of data enthusiasts around the world. Jeff’s research papers have received awards at the premier venues in Human-Computer Interaction and Visualization (ACM CHI, UIST, CSCW; IEEE InfoVis, VAST, EuroVis). Other honors include MIT Technology Review’s TR35 (2009), a Sloan Fellowship (2012), and the ACM Grace Murray Hopper Award (2016). Jeff holds B.S., M.S., and Ph.D. degrees in Computer Science from UC Berkeley, whom he then betrayed to join the Stanford faculty (2009–2013). He is also a co-founder of Trifacta, a provider of interactive tools for scalable data transformation.

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popular talk on ML at IAS today 5:30pm
by Sanjeev Arora 27 Oct '17

27 Oct '17

I am involved in a popular lecture + panel discussion today on ML at IAS. 5:30pm. https://www.ias.edu/events/arora-zemel-publiclecture Best Sanjeev

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Yonatan Naamad will present his FPO, "Hardness from Densest Subgraph Conjectures"on Monday, October 30, 2017 at 4pm in CS 402
by Nicki Gotsis 24 Oct '17

24 Oct '17

Yonatan Naamad will present his FPO, "Hardness from Densest Subgraph Conjectures"on Monday, October 30, 2017 at 4pm in CS 402. The members of his committee are: Moses Charikar (adviser); Readers: Sanjeev Arora and Anthony Wirth (University of Melbourne); Examiners: Moses Charikar, Mark Braverman, and Matt Weinberg. A copy of his thesis is available in Room 310. Everyone is invited to attend his talk. The talk abstract is attached to this email.

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Fwd: CS/EE Seminar - Monday, November 6. 2017, 4:30 pm, Computer Science 105 - Prof. Benjamin Lee
by Jennifer Rexford 23 Oct '17

23 Oct '17

> > From: "Lori A. Bailey" <lbailey(a)Princeton.EDU> > Subject: CS/EE Seminar - Monday, November 6. 2017, 4:30 pm, Computer Science 105 - Prof. Benjamin Lee > Date: October 23, 2017 at 11:59:27 AM EDT > To: ee-seminar(a)Princeton.EDU > Reply-To: "Lori A. Bailey" <lbailey(a)Princeton.EDU> > > > > > Department of Computer Science and > Department of Electrical Engineering Seminar > > > > Speaker: Prof. Benjamin Lee > Duke University > > Title: Rethinking Datacenter Management with Game Theory > Date: Monday, November 6, 2017 > Time: 4:30 p.m. > Room: Computer Science, Room 105 > Host: Prof. Margaret Martonosi and Prof. David Wentzlaff > > Abstract: Sharing datacenter hardware improves energy efficiency, but whether strategic users participate in consolidated systems depends on management policies. Users who dislike allocations may refuse to participate and deploy private, less-efficient systems. We rethink systems management, drawing on game theory to model strategic behavior and incentivize participation. We illustrate this perspective for three fundamental challenges in datacenters. For workload colocation, we design matching games to produce stable coalitions in which more users share with preferred co-runners. For resource allocation, we use Cobb-Douglas utility functions to produce fair allocations that incentivize users to share cache and memory. For power delivery, we design sprinting games to produce equilibria in which users selfishly draw power for performance boosts yet avoid oversubscribing the shared supply. Collectively, these varied solution concepts provide a rich toolkit for rigorously managing datacenters shared by strategic and competitive users. > > Biography: Benjamin Lee is an Associate Professor of Electrical and Computer Engineering at Duke University. His research focuses on computer architectures, security / privacy, and energy efficiency with links to statistical inference and algorithmic economics. His research has been recognized by IEEE Micro Top Picks (4x), Communications of the ACM Research Highlights (2x), as well as varied paper honors from ASPLOS, MICRO, and SC. He received the NSF Computing Innovation Fellowship, NSF CAREER Award, and Google Faculty Research Award. He held visiting positions at Microsoft Research, Intel Labs, and Lawrence Livermore National Lab. Dr. Lee received his B.S. from the University of California at Berkeley, his Ph.D. from Harvard University, and his post-doctorate from Stanford University.

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Andrej Risteski will present his FPO, "New techniques for learning and inference in Bayesian models" on Thursday, October 26, 2017 at 1pm in CS 402
by Nicki Gotsis 19 Oct '17

19 Oct '17

Andrej Risteski will present his FPO, "New techniques for learning and inference in Bayesian models" on Thursday, October 26, 2017 at 1pm in CS 402. The members of his committee are: Readers: Sanjeev Arora (adviser), Rong Ge (Duke University), and Elad Hazan Nonreaders: Sanjeev Arora, Elad Hazan, Bernard Chazelle, and Rong Ge (Duke University), and Mark Braverman A copy of his thesis, is available in Room 310. Everyone is invited to attend his talk. The talk abstract follows below. Abstract: A common theme in machine learning is succinct modeling of distributions over large domains. Probabilistic graphical models are one of the most expressive frameworks for doing this. The two major tasks involving graphical models are learning and inference. Learning is the task of calculating the best fit graphical model from raw data, while inference is the task of answering probabilistic queries for a known graphical model (for example what is the marginal distribution of one of the variables or what is the distribution of a subset of variables, after conditioning on the values of some other subset of variables). Learning can be thought of as finding a graphical model that explains the raw data, while the inference queries extract the knowledge the graphical model contains. This thesis introduces new provable techniques for performing both of these tasks, in the context of both latent-variable models – in which a portion of the variables in the graphical model are not observed, as well as fully-observable undirected graphical models (Markov Random Fields). Chapters 2 and 3 will focus on learning latent-variable models, while Chapter 4 will focus on inference in Markov Random Fields. In Chapter 2, I will contribute the first provable results for analyzing variational Bayes: a family of alternatingminimization style algorithms which is very popular in practice for learning latent-variable models. Despite it’s popularity with practitioners, the only theoretical guarantees prior to this work concerned convergence to local minima. We will prove that under reasonable assumptions, in the context of topic models, these algorithms will converge to the global minimum. Subsequently, in Chapter 3, we will use the method-of-moments along with new techniques in tensor decomposition and constrained matrix factorization to derive algorithms for learning noisy-OR networks – the textbook example of a probabilistic model for causal relationships. Importantly, these techniques were only applicable to linear latentvariable models – which noisy-OR is not. In Chapter 4, I will contribute a new understanding of a class of variational methods for calculating partition functions in Markov Random Fields. The key technical ingredient is a connection to convex programming hierarchies – a recent area of interest in combinatorial optimization, along with approximations of the entropy of a distribution based on low-order moment information.

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Sotirios Apostolakis will be presenting his general exam "Securing Commodity Systems with Containment: a Redis Server Case Study" on Monday, October 23, 2017 at 2:30pm in CS 402.
by Nicki Gotsis 18 Oct '17

18 Oct '17

Sotirios Apostolakis will be presenting his general exam " Securing Commodity Systems with Containment: a Redis Server Case Study" on Monday, October 23, 2017 at 2:30pm in CS 402. The members of his committee are David August (adviser), Nick Feamster, and Andrew Appel. Everyone is invited to attend his talk. Abstract: Modern computing systems are too complex to be vulnerability free. Current reactive approaches to security are not effective, and proposed "clean slate" approaches, requiring formal proofs of correctness for whole systems, are impractical. Prior work introduced TrustGuard, an approach that provides proactive protection against malicous and buggy hardware by allowing only the correct execution of signed software to produce output. This work expands TrustGuard’s containment guarantees to additionally protect against flawed software. To demonstrate TrustGuard's expanded capabilities, we implemented a system that secures a commodity Redis server against deviations from the Redis server specification. We show that 4K RTL LOC (one time cost on FPGA network card) and 3K software LOC can secure a modern Redis server consisting of hundreds of millions of LOC of hardware, OS, libraries and applications.

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Raghuvansh Saxena will present his General Exam on Monday, October 16, 2017 in CS 233 at 11am
by Nicki Gotsis 13 Oct '17

13 Oct '17

Raghuvansh Saxena will present his General Exam on Monday, October 16, 2017 in CS 233 at 11am. The members of his committee are: Gillat Kol (adviser), Matt Weinberg, and Mark Braverman. 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. "Interactive Coding Over the Noisy Broadcast Channel" A set of n players, each holding a private input bit, communicate over a noisy broadcast channel. Their mutual goal is for all players to learn all inputs. At each round one of the players broadcasts a bit to all the other players, and the bit received by each player is flipped with a fixed constant probability (independently for each recipient). How many rounds are needed? This problem was first suggested by El Gamal in 1984. In 1988, Gallager gave an elegant noise-resistant protocol requiring only O(n log log n) rounds. The problem got resolved in 2005 by a seminal paper of Goyal, Kindler, and Saks, proving that Gallager’s protocol is essentially optimal. We revisit the above noisy broadcast problem and show that O(n) rounds suffice. This is possible due to a relaxation of the model assumed by the previous works. We no longer demand that exactly one player broadcasts in every round, but rather allow any number of players to broadcast. However, if it is not the case that exactly one player chooses to broadcast, each of the other players gets an adversely chosen bit. We generalized the above result and initiate the study of interactive coding over the noisy broadcast channel. We show that any interactive protocol that works over the noiseless broadcast channel can be simulated over our restrictive noisy broadcast model with constant blowup of the communication. Reading list: Textbook: Computational Complexity: A Modern Approach - By Sanjeev Arora and Boaz Barak. Survey: Coding for Interactive Communication: A Survey - By Ran Gelles. Papers: Finding parity in a simple broadcast network - Robert Gallager Deterministic coding for interactive communication - Leonard Schulman Communication on noisy channels: a coding theorem for computation - Leonard Schulman Lower Bounds for the Noisy Broadcast Problem - Navin Goyal, Guy Kindler, and Michael Saks Towards Coding for Maximum Errors in Interactive Communication - Mark Braverman and Anup Rao A coding theorem for distributed computation - Sridhar Rajagopalan and Leonard Schulman Information Equals Amortized Communication - Mark Braverman and Anup Rao Interactive channel capacity - Gillat Kol and Ran Raz Constant-rate coding for multiparty interactive communication is impossible - Mark Braverman, Klim Efremenko, Ran Gelles, and Bernhard Haeupler Optimal Error Rates for Interactive Coding I: Adaptivity and Other Settings - Mohsen Ghaffari, Bernhard Haeupler, and Madhu Sudan

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Linpeng Tang will present his pre FPO, "Efficient processing and delivery of multimedia data" on Friday, October 20, 2017 at 2pm in CS 402.
by Nicki Gotsis 12 Oct '17

12 Oct '17

Linpeng Tang will present his pre FPO, " Efficient processing and delivery of multimedia data" on Friday, October 20, 2017 at 2pm in CS 402. The members of his committee are as follows: Readers: Prof. Mike Freedman, Prof. Wyatt Lloyd Non-readers: Prof. Jen Rexford, Prof. Kyle Jamieson Advisor: Prof. Kai Li Everyone is invited to attend his talk. The talk title and abstract follow below. Title: Efficient processing and delivery of multimedia data Abstract: With the rise of smart mobile devices and faster networks, multimedia data, such as photos and videos, are becoming a central part of people's digital lives in recent years. Meanwhile, new challenges have also emerged on how to process and delivery such data efficiently. This thesis I will investigate three challenging problems related to this area. The first part is about how to cache photo content in the large-scale content delivery network. We have designed and implemented RIPQ, an advanced photo caching framework on SSD. The Flash Translation Layer on SSD devices doesn't cope well with the random writes generated by advanced caching algorithms, causing lower througput and reduced device lifespan. To solve this issue, RIPQ aggregates small random writes, co-locates similarly prioritized content, and lazily moves updated content to reduce device overhead. Our evaluation on Facebook’s photo trace shows that two advanced caching algorithms running on RIPQ increase hit ratios up to ~20% over the current FIFO system, incur low overhead, and achieve high throughput. It has also since been deployed on the Facebook photo serving stack. The second part is on how to improve the streaming quality of social media video content. I will present CHESS, a scalable and accurate video popularity prediction algorithm. By accuratly predicting the popular videos, CHESS allows us to focus intensive processing on a small amount of videos, while improving user experience for most viewers. CHESS only requires constant space per video while delivering superior predictions using a combination of historical access patterns with social signals, in a unified online learning framework. In evaluation we find that re-encoding popular videos predicted by CHESS enables 80% Facebook user watch time to benefit from a computing intensive encoding, with only one-third computing overhead compared to a recent production heuristic. Finally, deep learning emerges as the most effective machine learning method for analyzing many types of content. Computing is a bottleneck for performing such analytics at scale, and I will present Graphi, a generic and high-performance execution engine for deep learning computation on many-core CPUs. We find that the key is finding the right balance between intra/inter operation parallelization, while minimizing resource interference across the weak cores. We therefore have designed and implemented Graphi using intelligent scheduling algorithms with feedback from a profiler, and a thread management system. Our experiments on show that the various training times with Graphi are 2.1× to 9.5× faster than those with TensorFlow on a 68-core Intel Xeon Phi processor.

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Davit Buniatyan will present his general exam on Friday, October 13, 2017 at 4pm in CS 402.
by Nicki Gotsis 11 Oct '17

11 Oct '17

Davit Buniatyan will present his general exam on Friday, October 13, 2017 at 4pm in CS 402. The members of his committee are as follows: Sebastian Seung (adviser), Yoram Singer, and Szymon Rusinkiewicz. 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: Deep Learning Improves Template Matching by Normalized Cross Correlation Abstract: Template matching by normalized cross correlation (NCC) is widely used for finding image correspondences. We improve the robustness of this algorithm by preprocessing images with "siamese" convolutional networks trained to maximize the contrast between NCC values of true and false matches. The improvement is quantified using patches of brain images from serial section electron microscopy. Relative to a parameter-tuned bandpass filter, siamese convolutional networks significantly reduce false matches. Furthermore, all false matches can be eliminated by removing a tiny fraction of all matches based on NCC values. The improved accuracy of our method could be essential for connectomics, because emerging petascale datasets may require billions of template matches to assemble 2D images of serial sections into a 3D image stack. Our method is also expected to generalize to many other computer vision applications that use NCC template matching to find image correspondences. Reading List * Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, * Brian Kulis et al. Metric learning: A survey. Foundations and TrendsR in Machine Learning, 5(4): 287–364, 2013 * Jane Bromley, James W. Bentz, Léon Bottou, Isabelle Guyon, Yann LeCun, Cliff Moore, Eduard Säckinger, and Roopak Shah. Signature verification using a "siamese" time delay neural network. IJPRAI, 7(4): 669–688, 1993. * Sumit Chopra, Raia Hadsell, and Yann LeCun. Learning a similarity metric discriminatively, with application to face verification. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 539–546. IEEE, 2005. * Han, Xufeng, et al. "Matchnet: Unifying feature and metric learning for patch-based matching." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2015. * Arulkumar Subramaniam, Moitreya Chatterjee, and Anurag Mittal. Deep neural networks with inexact matching for person re-identification. In Advances in Neural Information Processing Systems, pages 2667–2675, 2016. * Stephan Saalfeld, Richard Fetter, Albert Cardona, and Pavel Tomancak. Elastic volume reconstruction from series of ultra-thin microscopy sections. Nature methods, 9(7):717–720, 2012. * Jonathan L Long, Ning Zhang, and Trevor Darrell. Do convnets learn correspondence? In Advances in Neural Information Processing Systems, pages 1601–1609, 2014.

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Xinyi Fan will present her pre-FPO on Tuesday, October 10, 2017 at 2pm in CS 401.
by Nicki Gotsis 09 Oct '17

09 Oct '17

Xinyi Fan will present her pre-FPO on Tuesday (October 10) 2-4pm in CS 401. Advisor: Szymon Rusinkiewicz FPO examiners: Adam Finkelstein, Peter Ramadge (ELE) Thesis readers: Tom Funkhouser, Yuxin Chen (ELE) Title: View and Path Planning for Large-Scale 3D Acquisition Abstract: Demand for high-volume 3D scanning of real objects is rapidly growing in a wide range of applications, including online retailing, quality-control for manufacturing, stop motion capturing for 3D animation, as well as archaeological documentation and reconstruction. Although mature technologies exist for high-fidelity 3D model acquisition, deploying them at scale continues to require non-trivial manual labor. In this talk, I will present a system that allows non-expert users to scan large numbers of physical objects within a reasonable amount of time, and also with greater ease. I will describe the design of a structured-light scanner and positioning system, which are optimized for acquisition of relatively small objects, such as fragments of archaeological artifacts. I will also discuss novel view- and path-planning algorithms that enable automatic 3D acquisition, and demonstrate the ability of our prototype to safely, robustly, and automatically acquire 3D geometry and color models for large collections of small objects, such as toy soldiers and fragments of ancient artifacts.

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