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​​​​Abstract​

Bio: Moshe Babaioff is a Senior Principal Researcher at Microsoft Research, located in Israel. His research focuses on establishing rigorous theoretical foundations of solutions to real-world problems in the intersection of Economics and Computation (EC), using tools and approaches from Computer Science, Game Theory, and Microeconomic Theory. Moshe has served on multiple leadership positions at the EC community, including serving as Program co-Chair of ACM-EC 2017, as General Chair of ACM-EC 2014, and as co-chair of the Economics, Monetization, and Online Markets Track of The Web Conference (2023).

Moshe has been at Microsoft Research (MSR) for 15 years, first at MSR Silicon Valley lab (2007-2014) and then at MSR in Israel (since 2014). Before joining MSR he was a postdoctoral scholar at the University of California, Berkeley. He received his PhD in Computer Science from the Hebrew University in Jerusalem.

To begin, I describe how NLP researchers currently conduct experiments and measure model performance, highlighting some important limitations. I will present our work, in which we propose statistical analyses for comparing models that allow researchers to make credible and statistically valid claims in many experimental settings in NLP, such as experimenting with deep neural network models or reporting model performance across multiple languages.

Then, I will discuss challenges related to evaluating text-generation tasks, such as machine translation or automatic summarization. Evaluation of text-generation models is not straightforward because different texts can convey the same meaning. Therefore, comparing a model's output with a human-written reference may not reflect the true quality of the output. Researchers have designed metrics to automatically evaluate text-generation systems. However, none of them measures all of the aspects we wish to evaluate. Therefore, I will propose methods for estimating the quality of these evaluation metrics and introduce limitations for a certain type of evaluation technique known as reference-free. Finally, I will discuss future research directions and challenges in estimating the true performance of NLP models.

This talk is based on papers published in TACL (2017, 2021), ACL (2018, 2019), NAACL 2022, EMNLP 2022, and a book published by Morgan and Claypool Publishers in 2020.   ​

Bio: Rotem Dror is a postdoctoral researcher at the University of Pennsylvania Cognitive Computation Group, mentored by Prof. Dan Roth. She received her Ph.D. from the Technion - Israel Institute of Technology, where she was advised by Prof. Roi Reichart. With a focus on Natural Language Processing applications, her research involves developing statistically sound methodologies for empirical investigation and evaluation.​

By: Amos Korman, The French National Centre for Scientific Research (CNRS)

Host: Natan Rubin

Bio: Amos Korman received his Ph.D. in computer science at the Weizmann Institute of Science in May 2006, under the guidance of David Peleg. His Ph.D. thesis won Dean's prize for Ph.D. students. He then continued for two years of postdoc at the Technion and has been a researcher at CNRS, France, since Nov. 2007. In 2015 Amos received his Habilitation and in 2017 he was promoted to the rank "Directeur de Recherche". Until 2014 Amos worked primarily in the areas of distributed computing and graph algorithms. Around that time, he made a shift in his research focus, aiming to study biological phenomena through an algorithmic perspective. To support this pioneering initiative Amos received an ERC Consolidator Grant that lasted during the years 2015 - 2021. In 2020 Amos received the SIROCCO Prize for Innovation in Distributed Computing. As mentioned in the laudatio, the award was given for "pioneering contributions to distributed computing methods for system biology''.

A fundamental problem in robotics is to localize a robot in the real-world using only a monocular 2D RGB camera. Here, localization means 6 degrees of freedom: x,y,z-axes and rotations around them. Our group suggested the first solution with provable guarantees that can be applied in real-time on a very weak and lightweight micro-computer (RPI0, <5 grams, <$6). 

The motivation was to provide the first autonomous nano-drone of weight <100 grams, but we also used it for smart carts, and flying ads in the supermarket of Rami-Levi (near the lab). The main theoretical challenge was to provide the first provable approximation for the Perspective-n-Point problem: Compute a rotation and translation of a set of n points (in the 3d world) that minimize their sum of distances to n paired lines (2D pixels with missing depth). This is by proving that there is always a small subset of pairs (called core-set) that yields such an approximation and can be computed in O(n) time.

Based on papers with my awesome students:

- International Conference on Intelligence Robots and Systems (IROS'22), with Ibrahim Jubran, Fares Fares, Yuval        Alfassi, and Firas Ayoub.
- International Conference on Computer Vision (ICCV'21), with Ibrahim Jubran and Alaa Maalouf.

Bio: https://www.rbd-labs.com

 Bio: Michael Schapira is professor of Computer Science at Hebrew University. His research focuses on the design and analysis of (Inter)network architectures and protocols and, in particular, on the interface of networking and machine learning. Prior to joining Hebrew U, he worked at Google NYC's Infrastructure Networking Group and was a postdoctoral researcher at UC Berkeley, Yale University, and Princeton University. He is a recipient of the Allon Fellowship, the Wolf Foundation's Krill Prize, an ERC Starting Grant, faculty awards from Microsoft, Google, and Facebook, IETF/IRTF Applied Networking Research Prizes, and the IEEE Communications Society William R. Bennett Prize.

​Proactive and Online Distributed Load Balancing

By: Manish Kumar
Host: Shlomi Dolev

​A study of privacy and compression in learning theory.

By: Meni Sadigurschi

(Host: Aryeh Kontrovich)

​Federated Learning: Collaborative Learning on Private Data

By: Friedman Award - Amit Portnoy

(Host: Danny Hendler)

We introduce federated learning, discuss the communication challenges it poses, and present EDEN—a robust lossy compression technique tailored for its specific communication patterns and constraints. We discuss EDEN's appealing theoretical guarantees and present empirical results that demonstrate that it consistently improves over the state-of-the-art. EDEN will be presented at ICML '22. It is a generalization of our previous compression technique named DRIVE, presented at NeurIPS '21.

This talk is for a general CS audience.

Amit Portnoy is a Ph.D. student in the CS department, advised by Prof.

Danny Hendler. This is joint work with Ran Ben Basat, Yaniv Ben-Itzhak, Gal Mendelson, Michael Mitzenmacher, and Shay Vargaftik.

​Caching systems commonly advertise the cached content, thus allowing users to select which cache to query for the desired datum. The data structure used to advertise the cached content is called indicator. Due to bandwidth and energy constraints, a practical indicator is typically not a fresh, accurate list of all the items currently stored in the cache. Instead, the indicator is a stale approximation of the list of the currently cached items. As a result, indicators experience false indications. These false indications significantly increase costs, and may even result in a scenario where using indicators does more harm than good.

Our work introduces the cache selection problem, namely, the problem of selecting which cache to access in the face of such false indications. We formally model this problem as a cost minimization problem and introduce practical algorithms with guaranteed approximation ratios. We further perform an extensive simulation study with multiple real system traces. We show that our algorithms incur a significantly lower access cost than existing approaches or match the cost of these approaches while requiring an order of magnitude fewer resources (e.g., caching capacity or bandwidth).

Itamar Cohen received his B.Sc. degree in computer engineering and M.Sc. in electrical engineering from the Technion – I.I.T, and Ph.D. from the Ben-Gurion University of the Negev. He worked as a Chip Designer with EZchip Technologies, as a Test Engineer at Marvell, and as a Research Assistant at REMON—Israel 4G Mobile Consortium. He is currently a Post-Doctoral Fellow with the Politecnico di Torino, Italy. His research interests include network algorithms and resource allocation problems, and specifically issues related to scheduling, cooperative caching, and decision making under uncertainty.

Bio: Talya Eden is a postdoctoral fellow jointly affiliated with MIT and Boston University. She works on sublinear-time graph algorithms and randomized algorithms for huge data sets. Her work focuses on theoretical and applied graph parameter estimation in sublinear-time, as well as graph algorithms in other related areas, such as the streaming model, the massively parallel computation model, and learning-augmented algorithms.

 Talya completed her PhD at the School of Electrical Engineering at Tel Aviv University under the supervision of Prof. Dana Ron, and then continued on to a postdoctoral fellowship with the Foundations of Data Science Institute at MIT.  Talya has won several awards for her work, including the EATCS Distinguished Dissertation Award in Theoretical Computer Science, a Rothschild Postdoctoral Fellowship, a Schmidt Postdoctoral Award, a Ben Gurion University postdoctoral fellowship, a Weinstein Graduate Studies Prize, and the Azrieli Fellows Scholarship.

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In machine learning settings where the dataset consists of signals defined on many different graphs, the trained GNN should generalize to graphs outside the training set. A GNN is called transferable if, whenever two graphs represent the same underlying phenomenon, the GNN has similar repercussions on both graphs. Transferability ensures that GNNs generalize if the graphs in the test set represent the same phenomena as the graphs in the training set. We will discuss the different approaches to mathematically model the notions of transferability, and derive corresponding transferability error bounds, proving that GNNs have good generalization capabilities.

Links to related papers:
>> https://arxiv.org/pdf/1907.12972.pdf
>> https://arxiv.org/pdf/1705.07664.pdf

Bio:
Ron Levie received the Ph.D. degree in applied mathematics in 2018, from Tel Aviv University, Israel. During 2018-2020, he was a postdoctoral researcher with the Research Group Applied Functional Analysis, Institute of Mathematics, TU Berlin, Germany. Since 2021 he is a researcher in the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence, Department of Mathematics, LMU Munich, Germany. Since 2021, he is also a consultant at the Huawei project Radio-Map Assisted Pathloss Prediction, at the Communications and Information Theory Chair, TU Berlin. He won excellence awards for his MSc and PhD studies, and a Postdoc Minerva Fellowship. He is a guest editor at Sampling Theory, Signal Processing, and Data Analysis (SaSiDa), and was a conference chair of the Online International Conference on Computational Harmonic Analysis (Online-ICCHA 2021).
His current research interests are in the theory of deep learning, geometric deep learning, interpretability of deep learning, deep learning in wireless communication, harmonic analysis, signal processing, wavelet theory, uncertainty principles, continuous frames, and randomized methods.
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The renaissance of machine learning (ML) and deep learning (DL) over the last decade is accompanied by an unscalable computational cost, limiting its advancement and weighing on the field in practice. In this talk, we take a systematic approach to address the algorithmic and methodological limitations at the root of these costs. We first demonstrate that DL training and pruning are predictable and governed by scaling laws -- for state of the art models and tasks, spanning image classification and language modeling, as well as for state of the art model compression via iterative pruning. Predictability, via the establishment of these scaling laws, provides the path for the principled design and trade-off reasoning, currently largely lacking in the field. We then continue to analyze the sources of the scaling laws, offering an approximation-theoretic view and showing through the exploration of a noiseless realizable case that DL is in fact dominated by error sources very far from the lower error limit. We conclude by building on the gained theoretical understanding of the scaling laws' origins. We present a conjectural path to eliminate one of the current dominant error sources -- through a data bandwidth limiting hypothesis and the introduction of Nyquist learners -- which can, in principle, reach the generalization error lower limit (e.g. 0 in the noiseless case), at finite dataset size.​