- talks - lists.cs.princeton.edu

Reminder: Colloquium Speaker Noam Slonim, tomorrow at 12:30pm
by Emily C. Lawrence 06 Feb '20

06 Feb '20

CS Colloquium Speaker Noam Slonim , IBM Haifa Research Lab Friday, February 7 - 12:30pm Computer Science - Room 105 Host: Yoram Singer and Karthik Narasimhan [ https://www.cs.princeton.edu/events/25915 | https://www.cs.princeton.edu/events/25915 ] Title: Project Debater - How persuasive can a computer be? Project Debater is the first AI system that can meaningfully debate a human opponent. The system, an IBM Grand Challenge, is designed to build coherent, convincing speeches on its own, as well as provide rebuttals to the opponent's main arguments. In February 2019, Project Debater competed against Harish Natarajan, who holds the world record for most debate victories, in an event held in San Francisco that was broadcasted live world-wide. In this talk I will tell the story of Project Debater, from conception to a climatic final event, describe its underlying technology, and discuss how it can be leveraged for advancing decision making and critical thinking. Bio: Noam Slonim is a Distinguished Engineer at IBM Research AI. He received his doctorate from the Interdisciplinary Center for Neural Computation at the Hebrew University and held a post-doc position at the Genomics Institute at Princeton University. During his PhD, Noam received the best paper award in UAI and ECIR, and the best presentation award at SIGIR. Noam joined the IBM Haifa Research Lab in 2007, and in 2011 he proposed to develop Project Debater. He has been serving as the Principal Investigator of the project since then. Noam published around 60 peer reviewed articles, focusing on the last few years on advancing the emerging field of Computational Argumentation. Finally, Noam used to have a secondary career as a TV script writer. Coincidentally, or not, in a sitcom he co-created back in 1998, the last episode was focused on competitive debates.

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Colloquium Speaker: Kevin Ellis, Tuesday, Feb 11 at 12:30pm
by Emily C. Lawrence 05 Feb '20

05 Feb '20

CS Colloquium Speaker Kevin Ellis, Massachusetts Institute of Technology Tuesday, February 11 - 12:30pm Computer Science - Room 105 Host: Tom Griffiths [ https://www.cs.princeton.edu/events/25918 | https://www.cs.princeton.edu/events/25918 ] Title: Building Machines that Discover Generalizable, Interpretable Knowledge Humans can learn to solve an endless range of problems: building, drawing, designing, coding, and cooking, to name a few, and need relatively modest amounts of experience to acquire any one new individual skill. Machines which can similarly master a diverse span of problems are surely far off. Here, however, I will argue that program induction--an emerging AI technique--will play a role in building this more human-like AI. Program induction systems represent knowledge as programs, and learn by synthesizing code. Across three case studies in vision, natural language, and learning-to-learn, this talk will present program induction systems that take a step toward machines that can: acquire new knowledge from modest amounts of experience; strongly generalize that knowledge to extrapolate beyond their training; learn to represent their knowledge in an interpretable format; and are applicable to a broad spread of problems, from drawing pictures to discovering equations. Driving these developments is a new neuro-symbolic algorithm for Bayesian program synthesis. This algorithm integrates maturing program synthesis technologies with several complementary AI traditions (symbolic, probabilistic, and neural). Building a human-like machine learner is a very distant, long-term goal for the field. In the near-term program induction comes with a roadmap of practical problems to push, such as language learning, scene understanding, and programming-by-examples, which this talk explores. But it's worth keeping these long-term goals in mind as well. Bio: Kevin Ellis works across artificial intelligence, program synthesis, and machine learning. He develops learning algorithms that teach machines to write code, and applies these algorithms to problems in artificial intelligence. His work has appeared in machine learning venues ( NeurIPS , ICLR, IJCAI) and cognitive science venues ( CogSci , TOPICS). He has collaborated with researchers at Harvard, Brown, McGill, Siemens, and MIT, where he is a final-year graduate student advised by Josh Tenenbaum and Armando Solar- Lezama .

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TODAY at 4:30pm in B205 - Elaine Shi, Cornell University
by Emily Lawrence 04 Feb '20

04 Feb '20

Speaker: Elaine Shi, Cornell University Title: Practical Oblivious Computation Day: Tuesday, February 4, 2020 Time: 4:30 pm Room: B205 Engineering Quadrangle Host: Prateek Mittal Abstract: Oblivious computation refers to the ability to compute on "encrypted" data, such that neither intermediate results nor the program's runtime behavior reveal anything about secret inputs. Oblivious computation can enable privacy-preserving data mining for sensitive data (e.g., genomic or financial data), and allow businesses and individuals to monetize their data without compromising their privacy. In this talk, I will present a novel, binary-tree based paradigm for constructing Oblivious RAM (ORAM) schemes. ORAM is a generic and powerful primitive that is central to realizing oblivious computation based on either trusted hardware or cryptographic secure computation. Our new ORAM constructions not only solve a thirty-year open theoretical challenge, but also allow ORAM to evolve from a theoretical primitive to a practical building block. Specifically, we made it possible, for the first time, to implement ORAM atop secure processors as well as secure multi-party computation. Additionally, I will present programming language techniques that not only accelerate oblivious computation, but also make it accessible to real-life programmers who are not security experts. Based on these algorithmic and programming language advances, we have built new platforms for oblivious computation and developed various demo applications such as common data structures, data mining, graph algorithms, and streaming algorithms. Evaluation results suggest that our work has led to four to five orders of magnitude performance improvement for moderately large data sizes, and has enabled a dramatic reduction in application development effort. Bio: Elaine Shi is an Associate Professor in Cornell University. Her research interests include security, cryptography, distributed systems, foundations of blockchains, and language-based security. She is a recipient of the Packard Fellowship, the Sloan Fellowship, the ONR YIP award, the NSF CAREER award, the NSA Best Scientific Security paper, and various other best-paper and research awards. Elaine obtained her Ph.D. from Carnegie Mellon University. Before joining Cornell, she was an Assistant Professor in UMD. This seminar is supported by Korhammer Lecture Series Funds

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Colloquium Speaker Noam Slonim, Fri. Feb 7 at 12:30pm
by Emily Lawrence 04 Feb '20

04 Feb '20

CS Colloquium Speaker Noam Slonim, IBM Haifa Research Lab Friday, February 7 - 12:30pm Computer Science - Room 105 Host: Yoram Singer and Karthik Narasimhan https://www.cs.princeton.edu/events/25915 Title: Project Debater - How persuasive can a computer be? Project Debater is the first AI system that can meaningfully debate a human opponent. The system, an IBM Grand Challenge, is designed to build coherent, convincing speeches on its own, as well as provide rebuttals to the opponent's main arguments. In February 2019, Project Debater competed against Harish Natarajan, who holds the world record for most debate victories, in an event held in San Francisco that was broadcasted live world-wide. In this talk I will tell the story of Project Debater, from conception to a climatic final event, describe its underlying technology, and discuss how it can be leveraged for advancing decision making and critical thinking. Bio: Noam Slonim is a Distinguished Engineer at IBM Research AI. He received his doctorate from the Interdisciplinary Center for Neural Computation at the Hebrew University and held a post-doc position at the Genomics Institute at Princeton University. During his PhD, Noam received the best paper award in UAI and ECIR, and the best presentation award at SIGIR. Noam joined the IBM Haifa Research Lab in 2007, and in 2011 he proposed to develop Project Debater. He has been serving as the Principal Investigator of the project since then. Noam published around 60 peer reviewed articles, focusing on the last few years on advancing the emerging field of Computational Argumentation. Finally, Noam used to have a secondary career as a TV script writer. Coincidentally, or not, in a sitcom he co-created back in 1998, the last episode was focused on competitive debates.

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Colloquium Speaker Kira Radinsky, Mon. Feb 10 at 12:30pm
by Emily C. Lawrence 03 Feb '20

03 Feb '20

CS Colloquium Speaker Dr. Kira Radinsky, Diagnostic Robotics Monday, February 10 - 12:30pm Computer Science - Room 105 Host: Elad Hazan [ https://www.cs.princeton.edu/events/25906 | https://www.cs.princeton.edu/events/25906 ] Learning to predict the future in healthcare Our world faces increasingly complex challenges: we destabilized the climate, haven’t beaten all diseases, and haven’t spread the values of democracy and freedom to large parts of the globe, where violence and riots reign supreme. The world must be fixed in our generation - everyone would agree. But in order to take action, build a plan, we need to see the complete picture, and empower decision makers with tools to make those changes. This decade, we have finally reached a critical amount of data to facilitate the creation of such tools. My work is inspired by Mark Twain’s quote, who once said: “The past does not repeat itself, but it rhymes." Although future events have unique circumstances, they typically follow familiar past patterns. Over the past few years, I devoted my life to development of prediction techniques. My system inferred that Cholera outbreaks in land-locked areas are more likely to occur following storms, especially when preceded by a long drought. Another inference is that genocide events tend to occur following events where local opinion makers describe minority groups as pests. These types of patterns are composed of several abstractions, over variable-term temporal extents and selected from a large number of possible causes. The algorithms I developed deal with the complexity of discovering such patterns. Large-scale digital histories, social and real-time media, and human web behavior are harvested and augmented with human knowledge mined from the web to afford real-time estimations of likelihoods of future events. Most recently, these algorithms have accurately predicted the first Cholera outbreak reported in Cuba in fifty years. These types of actionable predictions, that enable preventative measures, have drawn the attention of a UN genocide-prevention organization and the Gates foundations and illustrate the vast potential for real impact on the state of humanity. In the last few years I have been focusing on applying similar techniques for the healthcare and Pharma, leveraging large amount of data obtained from both medical records, EMR and other medical research results data in a quest to create an AI system for automated medical research and breakthroughs. Bio: Dr. Kira Radinsky is the chairman and CTO of Diagnostic Robotics, where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available. Dr. Radinsky has founded SalesPredict , acquired by eBay in 2016 and served as eBay Chief Scientist (IL). She gained international recognition for her work at the Technion and Microsoft Research for developing predictive algorithms that recognized the early warning signs of globally impactful events, as disease epidemics and political unrests. In 2013, she was named one of MIT Technology Review’s 35 Young Innovators Under 35 and in 2015 Forbes included her as "30 Under 30 Rising Stars in Enterprise Tech”. She is a frequent presenter at global tech and industry conferences, including TEDx , Wired, Strata Data Science, Techcrunch and publishes in HBR. Radinsky also serves as a board member in Israel Security Authorities and technology board of HSBC bank. She also holds a visiting professor position at the Technion focusing on the application of predictive data mining in medicine.

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4:30pm Tue Feb 4 Elaine Shi (Cornell) talk in E-Quad B205
by Jennifer Rexford 01 Feb '20

01 Feb '20

> > > > EE SEMINAR SERIES > > Speaker: > Elaine Shi, Cornell University > Title: > Practical Oblivious Computation > Day: > Tuesday, February 4, 2020 > Time: > 4:30 pm > Room: > B205 Engineering Quadrangle > Host: > Prateek Mittal > > Abstract: > Oblivious computation refers to the ability to compute on "encrypted" data, such that neither intermediate results nor the program's runtime behavior reveal anything about secret inputs. Oblivious computation can enable privacy-preserving data mining for sensitive data (e.g., genomic or financial data), and allow businesses and individuals to monetize their data without compromising their privacy. > In this talk, I will present a novel, binary-tree based paradigm for constructing Oblivious RAM (ORAM) schemes. ORAM is a generic and powerful primitive that is central to realizing oblivious computation based on either trusted hardware or cryptographic secure computation. Our new ORAM constructions not only solve a thirty-year open theoretical challenge, but also allow ORAM to evolve from a theoretical primitive to a practical building block. Specifically, we made it possible, for the first time, to implement ORAM atop secure processors as well as secure multi-party computation. > Additionally, I will present programming language techniques that not only accelerate oblivious computation, but also make it accessible to real-life programmers who are not security experts. Based on these algorithmic and programming language advances, we have built new platforms for oblivious computation and developed various demo applications such as common data structures, data mining, graph algorithms, and streaming algorithms. Evaluation results suggest that our work has led to four to five orders of magnitude performance improvement for moderately large data sizes, and has enabled a dramatic reduction in application development effort. > > > Bio: > Elaine Shi is an Associate Professor in Cornell University. Her research interests include security, cryptography, distributed systems, foundations of blockchains, and language-based security. She is a recipient of the Packard Fellowship, the Sloan Fellowship, the ONR YIP award, the NSF CAREER award, the NSA Best Scientific Security paper, and various other best-paper and research awards. Elaine obtained her Ph.D. from Carnegie Mellon University. Before joining Cornell, she was an Assistant Professor in UMD. > > This seminar is supported by Korhammer Lecture Series Funds

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Sotiris Apostolakis will present his Pre FPO "Perspective: A Sensible Approach to Speculative Automatic Parallelization" on Monday, February 10, 2020 at 10am in CS 402.
by Nicki Mahler 31 Jan '20

31 Jan '20

Sotiris Apostolakis will present his Pre FPO "Perspective: A Sensible Approach to Speculative Automatic Parallelization" on Monday, February 10, 2020 at 10am in CS 402. The members of his committee are as follows: David August (adviser/reader), Simone Campanoni (Northwestern University/reader), Andrew Appel (examiner), Brian Kernighan (examiner), and Zachary Kincaid (examiner). Everyone is invited to attend his talk. The talk title and abstract follow below. Abstract: The promise of automatic parallelization, freeing programmers from the error-prone and time-consuming process of making efficient use of parallel processing resources, remains unrealized. For decades, the imprecision of memory analysis limited the applicability of automatic parallelization. The introduction of speculation to automatic parallelization overcame these applicability limitations, but caused profitability problems due to high communication and bookkeeping costs for speculation validation and commit. This talk presents Perspective, a speculative parallelization framewo rk that maintains the applicability of prior speculative systems while minimizing the use of high-overhead speculative techniques. Unlike current approaches that perform analysis and transformations independently and in sequence, Perspective integrates, without loss of modularity, memory analysis, speculative techniques, and enabling transformations. Specifically, Perspective integrates the first speculation-aware dependence analysis framework, new efficient speculative privatization transformations, and a planning phase to select a minimal-cost set of parallelization-enabling transforms. By reducing speculative parallelization overheads in ways not possible with prior parallelization systems, Perspective obtains higher overall program speedup (23.0x for 12 general-purpose C/C++ programs running on a 28-core shared-memory machine) than Privateer (11.5x ), the prior automatic speculative-DOALL system with the highest applicability.

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EE Talk: Tuesday, February 4 in B205 - Elaine Shi, Cornell University
by Emily Lawrence 30 Jan '20

30 Jan '20

Speaker: Elaine Shi, Cornell University Title: Practical Oblivious Computation Day: Tuesday, February 4, 2020 Time: 4:30 pm Room: B205 Engineering Quadrangle Host: Prateek Mittal Abstract: Oblivious computation refers to the ability to compute on "encrypted" data, such that neither intermediate results nor the program's runtime behavior reveal anything about secret inputs. Oblivious computation can enable privacy-preserving data mining for sensitive data (e.g., genomic or financial data), and allow businesses and individuals to monetize their data without compromising their privacy. In this talk, I will present a novel, binary-tree based paradigm for constructing Oblivious RAM (ORAM) schemes. ORAM is a generic and powerful primitive that is central to realizing oblivious computation based on either trusted hardware or cryptographic secure computation. Our new ORAM constructions not only solve a thirty-year open theoretical challenge, but also allow ORAM to evolve from a theoretical primitive to a practical building block. Specifically, we made it possible, for the first time, to implement ORAM atop secure processors as well as secure multi-party computation. Additionally, I will present programming language techniques that not only accelerate oblivious computation, but also make it accessible to real-life programmers who are not security experts. Based on these algorithmic and programming language advances, we have built new platforms for oblivious computation and developed various demo applications such as common data structures, data mining, graph algorithms, and streaming algorithms. Evaluation results suggest that our work has led to four to five orders of magnitude performance improvement for moderately large data sizes, and has enabled a dramatic reduction in application development effort. Bio: Elaine Shi is an Associate Professor in Cornell University. Her research interests include security, cryptography, distributed systems, foundations of blockchains, and language-based security. She is a recipient of the Packard Fellowship, the Sloan Fellowship, the ONR YIP award, the NSF CAREER award, the NSA Best Scientific Security paper, and various other best-paper and research awards. Elaine obtained her Ph.D. from Carnegie Mellon University. Before joining Cornell, she was an Assistant Professor in UMD. This seminar is supported by Korhammer Lecture Series Funds

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Mingzhe Wang will present his General Exam "Structured Matching for Phrase Localization" on Monday, February 3, 2020 at 3pm in CS 302
by Nicki Mahler 24 Jan '20

24 Jan '20

Mingzhe Wang will present his General Exam on Monday, February 3, 2020 at 3pm in CS 302. The members of his committee are as follows : Jia Deng (adviser), Olga Russakovsky, and Danqi Chen. 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. In this paper we introduce a new approach to phrase localization: grounding phrases in sentences to image regions. We propose a structured matching of phrases and regions that encourages the semantic relations between phrases to agree with the visual relations between regions. We formulate structured matching as a discrete optimization problem and relax it to a linear program. We use neural networks to embed regions and phrases into vectors, which then define the similarities (matching weights) between regions and phrases. We integrate structured matching with neural networks to enable end-to-end training. Experiments on Flickr30K Entities demonstrate the empirical effectiveness of our approach. Textbook: Szeliski, R., 2010. Computer vision: algorithms and applications. Springer Science & Business Media. Papers: Kulkarni, G., Premraj, V., Dhar, S., Li, S., Choi, Y., Berg, A.C. and Berg, T.L., 2011. Baby talk: Understanding and generating image descriptions. In Proceedings of the 24th CVPR. Plummer, B.A., Wang, L., Cervantes, C.M., Caicedo, J.C., Hockenmaier, J. and Lazebnik, S., 2015. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In Proceedings of the IEEE international conference on computer vision (pp. 2641-2649). Wang, L., Li, Y. and Lazebnik, S., 2016. Learning deep structure-preserving image-text embeddings. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5005-5013). Rohrbach, A., Rohrbach, M., Hu, R., Darrell, T. and Schiele, B., 2016, October. Grounding of textual phrases in images by reconstruction. In European Conference on Computer Vision (pp. 817-834). Springer, Cham. Karpathy, A., Joulin, A. and Fei-Fei, L.F., 2014. Deep fragment embeddings for bidirectional image sentence mapping. In Advances in neural information processing systems (pp. 1889-1897). Karpathy, A. and Fei-Fei, L., 2015. Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3128-3137). Hu, R., Xu, H., Rohrbach, M., Feng, J., Saenko, K. and Darrell, T., 2016. Natural language object retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4555-4564). Klein, B., Lev, G., Sadeh, G. and Wolf, L., 2014. Fisher vectors derived from hybrid gaussian-laplacian mixture models for image annotation. arXiv preprint arXiv:1411.7399. Vinyals, O., Toshev, A., Bengio, S. and Erhan, D., 2015. Show and tell: A neural image caption generator. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3156-3164). Fukui, A., Park, D.H., Yang, D., Rohrbach, A., Darrell, T. and Rohrbach, M., 2016. Multimodal compact bilinear pooling for visual question answering and visual grounding. arXiv preprint arXiv:1606.01847.

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Ariel Schvartzman will present his Pre FPO "Circumventing Lower Bounds in Mechanism and Tournament Design" on Monday, January 27, 2020 in CS at 3pm in CS 302
by Nicki Mahler 21 Jan '20

21 Jan '20

Ariel Schvartzman will present his Pre FPO "Circumventing Lower Bounds in Mechanism and Tournament Design" on Monday, January 27, 2020 in CS at 3pm in CS 302. The members of his committee are: Matt Weinberg (adviser/reader), Mark Braverman (reader), Gillat Kol (examiner), Ran Raz (examiner) and Elad Hazan (examiner). Everyone is invited to attend his talk. The talk title and abstract follow below: Title: Circumventing Lower Bounds in Mechanism and Tournament Design How should a seller price multiple items in order to maximize their revenue from a single buyer? This question has been key in connecting research done by computer scientists and economists to applications such as online ad auctions and spectrum auctions. It was shown almost 40 years ago that if the buyer only cares for one item it is optimal for the seller to offer the buyer the item at a take-it-or-leave-it price. This elegant solution, however, may be far from optimal even for the case with a single buyer interested in just two items. Optimal auction design for two or more items is intricate for a number of reasons: the auctions may be bizarre, computationally hard to find or simply too complex to present to a bidder. Numerous results suggest that (under reasonable complexity assumptions) efficiently finding optimal auctions, even in some simple settings, is impossible. In my talk, I will present numerous ways in which we try to circumvent these impossibility results in order to recover positive results. These include the study of different multi-item models and beyond-worst case analysis for mechanism design. I will also discuss recent advances in tournament design, a field at the intersection of mechanism design and social choice theory. For problems in this field we get around known impossibility results via approximations in order to find approximately strategy-proof tournament rules.

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