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Colloquia and Seminars

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Upcoming Colloquia & Seminars

event head separator Sketching Streaming Data
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Rana Shahout (Ph.D. Thesis Seminar)
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Wednesday, 07.12.2022, 11:30
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Zoom Lecture: 99794111202 and Taub 301
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Advisor:  Prof. Roy Friedman
Stream monitoring is fundamental in many data stream applications, such as financial data trackers, security, anomaly detection, and load balancing. To cope with high-speed data streams, these applications require algorithms that are both time and space efficient to cope with high-speed data streams. Space efficiency is needed due to the memory hierarchy structure, to enable cache residency and to avoid page swapping. Even if the potential computing cost is low, this residency is critical for good performance (e.g., constant time algorithms may be inefficient if they access the DRAM for each element). To that end, stream processing algorithms often build compact approximate sketches (synopses) of the input streams. This work improves the speed and space requirements for streaming problems.
event head separator Theory Seminar: Streaming Algorithms for Submodular Maximizatio with a Cardinality Constraint
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Moran Feldman (Haifa university)
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Wednesday, 07.12.2022, 12:30
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Taub 201
Motivated by machine learning applications, much research over the last decade was devoted to solving submodular maximization problems under Big Data computational models. Perhaps the most basic such problem is the problem of maximizing a submodular function subject to a cardinality constraint. A recent series of papers has studied this problem in the data stream model, and in particular, fully determined the approximation ratio that can be obtained for it by (semi-)-streaming algorithms both when the objective function is monotone and non-monotone. In this talk we will survey the main ideas behind these tight results. Based on joint works with Naor Alaluf, Alina Ene, Huy Nguyen, Ashkan Norouzi-Fard, Andrew Suh, Ola Svensson and Rico Zenklusen.
event head separator CS Colloquia: Formal Methods for a Robust Network Ecosystem
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George Varghese (UCLA)
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Monday, 12.12.2022, 14:30
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Room 1003, EE Meyer Building
Network verification, applying formal methods to verify properties of router configurations, is already mainstream with startups like Forward and Veriflow Networks, and divisions in established companies such as Amazon’s ARG and Cisco’s Candid. In this talk, I will survey what remains to be done including: extending formal methods to implementations, and to other parts of the network ecosystem besides routing. I will illustrate these points with recent work we have done on improving the robustness of the Domain Name Service (DNS). Notable errors in DNS have rendered popular services such as GitHub, Twitter, and HBO inaccessible for extended periods. First, I will present GRoot (SIGCOMM 2020, Best Student Paper), a new verification tool that performs exhaustive and proactive static analysis of DNS configuration files (zone files) based on an efficient algorithm to determine the equivalence class of all possible queries to guarantee key correctness properties. Next, I will describe a new technique, SCALE (NSDI 2022), for finding RFC compliance errors in DNS nameserver implementations via symbolic execution of a DNS formal model to jointly generate test queries and zone files. Using SCALE, we identified 30 new bugs in 8 popular open-source DNS implementations such as BIND, PowerDNS, KNOT, and NSD, including 3 previously unknown critical security vulnerabilities. This talk is based on Siva Kakarla’s Ph.D. work at UCLA and is joint work with Ryan Beckett, Behnaz Armani at MSR , and Todd Millstein at UCLA. Bio: George Varghese is the Jonathan B. Postel Professor of Networking in the Computer Science department at UCLA. He was elected to the National Academy of Engineering in 2017, to the National Academy of Inventors in 2020, and to the Internet Hall of Fame in 2021, and to the American Academy of Arts and Sciences in 2022.
event head separator CS Colloquia: Notions of Simplicity In Deep Learning: From Time Series to Images
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Amir Globerson (Tel-Aviv university)
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Tuesday, 13.12.2022, 14:30
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Room 337 Taub Bld.
It is standard practice in deep learning to train large models on relatively small datasets. This can potentially lead to severe overfitting, but more often than not, test error is actually good. This phenomenon has prompted research on the so-called "Implicit Bias of Deep Learning Algorithms". Here I will discuss our recent works on multiple novel facets of this bias, and present theoretical and empirical results in different settings. In particular, I will discuss analysis of implicit bias in fine-tuning of large models, in learning temporal models (e.g., RNNs) and in labeling images with very few examples. Bio: Prof. Globerson received his BSc in computer science and physics in 1997 from the Hebrew University, and his PhD in computational neuroscience from the Hebrew University in 2006. After his PhD, he was a postdoctoral fellow at the University of Toronto and a Rothschild postdoctoral fellow at MIT. He joined the Hebrew University school of computer science in 2008, and moved to the Tel Aviv University School of Computer Science in 2015. He was an associate editor for the Journal of Machine Learning Research, and the Associate Editor in Chief for the IEEE Transactions on Pattern Analysis and Machine Intelligence. His work has received several paper awards (at NIPS, UAI, and ICML). He also serves as Research Scientist at Google in Tel Aviv. In 2018 he served as program co-chair for the UAI conference, and in 2019 he was the general co-chair for UAI in Tel Aviv. In 2019 he received the ERC consolidator grant. Host: Nir Rosenfeld.
event head separator Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality
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Guy Ohayon (M.Sc. Thesis Seminar)
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Thursday, 15.12.2022, 11:30
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Zoom Lecture: 2049693728
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Advisor:  Michael Elad (primary), Prof. Tomer Michaeli (secondary)
Stochastic restoration algorithms allow to explore the space of solutions that correspond to the degraded input. In this paper we reveal additional fundamental advantages of stochastic methods over deterministic ones, which further motivate their use. First, we prove that any restoration algorithm that attains perfect perceptual quality and whose outputs are consistent with the input must be a posterior sampler, and is thus required to be stochastic. Second, we illustrate that while deterministic restoration algorithms may attain high perceptual quality, this can be achieved only by filling up the space of all possible source images using an extremely sensitive mapping, which makes them highly vulnerable to adversarial attacks. Indeed, we show that enforcing deterministic models to be robust to such attacks profoundly hinders their perceptual quality, while robustifying stochastic models hardly influences their perceptual quality, and improves their output variability. These findings provide a motivation to foster progress in stochastic restoration methods, paving the way to better recovery algorithms.