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Scalable Bayesian Nonparametric Mixtures for Computer Vision and Deep Learning
By: Or Dinari
(Host: Oren Freifeld)
​​​May 31, 2022
Bldg. 37, Room 201

>>Zoom Link

In the unsupervised-learning task of clustering, Bayesian Nonparametric (BNP) mixtures provide a principled approach for inferring the model complexity (i.e., the number of clusters) together with the other parameters. Inference in such models, however, is more difficult than in the parametric case (namely, when the number of clusters is known). In particular, existing BNP methods usually scale poorly. This is a key obstacle in using such models in real-world problems in general and, more especially, in Computer Vision, where both the number of samples and the dimensions are usually high. In this talk, I will present a few of our recent works that aim to bridge this practicality gap, via new models, new inference algorithms, and efficient distributed implementations.

1) [Or Dinari and Oren Freifeld, AISTATS 2022]: Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data.
2) [Or Dinari and Oren Freifeld, UAI 2022]: Revisiting DP-Means: Fast Scalable Algorithms via Parallelism and Delayed Cluster Creation.
3) [Or Dinari*, Angel Yu, Oren Freifeld, and John Fisher III. HPML Workshop, 2019]: Distributed MCMC Inference in Dirichlet Process Mixture Models Using Julia.
4) [Or Dinari*, Raz Zamir*, John Fisher III, and Oren Freifeld, Currently Under Review]: CPU- and GPU-based Distributed Sampling in Dirichlet Process Mixtures for Large-scale Analysis.

About the speaker:
Or Dinari is a PhD student in the Department of Computer Science at Ben-Gurion University, working in the areas of computer vision and machine learning under the supervision of Dr. Oren Freifeld. His current research focus is on unsupervised/semi-supervised learning, clustering, Bayesian nonparametrics, deep learning, and computer-vision applications.
Matching vertices of correlated random graphs
By: Mark Rudelson
(Host: Aryeh Kontrovich)
​​May 24, 2022
Bldg. 37, Room 201

>>Zoom Link

Consider two copies of the same graph with a large number of vertices.
In each copy, we independently delete a few edges. Our aim is to match
exactly the vertices of the first and the second copy. This problem
originates in particular in social networks, where you know the
identities of users in one network and want to recover the identities
in another one. This problem is computationally hard in a
deterministic setting, so it is often considered for typical, i.e.,
random graphs. We will discuss a computationally efficient
probabilistic algorithm which allows an exact recovery with high
probability even if one deletes a small constant proportion of edges.

Joint work with Cheng Mao and Konstantin Tikhomirov.

Quantum Pseudorandomness and Classical Complexity
By: William Kretschmer
(Host: Dr. Or Sattath)

May 24, 2022
Bldg. 37, Room 201

>>Zoom Link

Pseudorandom quantum states (PRSs) are a recently-introduced primitive
that have found exciting new applications in quantum cryptography and
black hole physics. In this talk, I will explore some connections
between PRSs and structural complexity theory. I will present a
surprising and counterintuitive result: a quantum oracle relative to
which BQP = QMA but PRSs exist. On the other hand, I will show that
PRSs do not exist if BQP = PP, a result which holds relative to all
oracles. I will discuss some implications of these results, including
a new impossibility result for shadow tomography.

Based on


>> Meeting ​Record​​ing​​

Access strategies for network caching
By: ​Itamar Cohen
(Host: Dr. Gil Einziger)
Apr. 12, 2022
Bldg. 37, Room 201

>> ​Zoom Link

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.


Projection Methods, Superiorization and Applications

By: ​Aviv Gibali
(Host: Dr. E​ran Treister)

Mar. 31, 2022
Bldg. 37, Room 201

>> ​Zoom Link

Projection methods are iterative algorithms that use projections onto sets
while relying on the general principle that when a family of sets is present,
then projections onto the given individual sets are easier to perform than
projections onto other sets that are derived from the given individual sets.
Their robustness, low computational effort and their ability to handle
huge-size problems make them very useful for many convex and non-convex real-world problems
such as Image Reconstruction, Intensity-Modulated Radiation Therapy
(IMRT) Treatment Planning as well as Sudoku and 8 Queens Puzzle.
The Superiorization Methodology is a heuristic tool and its goal is to find certain good, or superior, solutions to feasibility and optimization problems. In many scenarios, solving the full problem can be rather demanding from the computational point of view, but solving part of it, say the feasibility part is, often, less demanding.
In recent years "superiorized" projection methods and other iterative methods have been developed and applied successfully to feasibility, single and multi-objective optimization.
In this talk I will provide an overview on the above concepts, present several theoretical and practical results and also potential direction for future research.

Generalization in Overparameterized Machine Learning

By: Yehuda Dar
(Host: Eran Treister)
Mar. 23, 2022
Bldg. 37, Room 201

>> ​Zoom Link
Deep neural networks are highly overparameterized models, i.e., they are highly complex with typically many more parameters than the number of training data examples. Such overparameterized models are usually learned to perfectly fit their training data; yet, in sharp contrast to conventional machine learning guidelines, they still generalize extremely well to inputs outside of their training dataset. This practical generalization performance motivates numerous foundational questions that have not yet been addressed even for much simpler models like linear regression.
This talk presents new analyses of the fundamental factors that affect generalization in overparameterized machine learning. Our results characterize the so-called “double descent phenomenon," which extends the classical bias-variance tradeoff, in several overparameterized learning settings. First, we consider a transfer learning process between source and target linear regression problems that are related and overparameterized. Our theory characterizes the generalization performance and hence the cases where transfer learning is beneficial. Second, we consider linear subspace learning problems for dimensionality reduction and data generation. We define semi- and fully supervised extensions to the common unsupervised forms of these subspace learning problems and demonstrate that overparameterization in conjunction with supervision can improve generalization performance.

Yehuda Dar is a postdoctoral research associate in the Electrical and Computer Engineering Department at Rice University, working on topics in the foundational understanding of modern machine learning. Before that, he was a postdoctoral fellow in the Computer Science Department of the Technion — Israel Institute of Technology where he also received his PhD in 2018. Yehuda earned his MSc in Electrical Engineering and a BSc in Computer Engineering, both also from the Technion. His main research interests are in the fields of machine learning, signal and image processing, optimization, and data compression.

>> Li​​​n​k ​to ​L​ecture​​​​​​

Information geometry of Markov kernels

By: Shun Watanabe
(Host: Aryeh Kontorovich)
Mar. 22, 2022

>> ​Zoom Link
In this talk, we first review basic concepts on the information geometry,
such as the e-family , m-family, and the Pythagorean identity, in the context
of probability distribution. We will also discuss an application of information
geometry to the problem of statistical hypothesis testing. Then, we will
discuss how the concepts of information geometry are extended to Markov
kernels. Finally, we will present our recent result that the set of reversible
Markov kernels constitute both e-family and m-family of Markov kernels.
This talk is based on a joint work with Geoffrey Wolfer.

>> Li​​​n​k ​to​ ​L​ecture​​​

Seeing the Unseen - a Generalized Scheme for Missing Mass Estimation

By: Amichai Painsky
(Host: Aryeh Kontorovich)
Mar. 21, 2022
Bldg. 37, Room 201

>> ​Zoom Link
Consider a finite sample from an unknown distribution over a countable alphabet. The missing mass refers to the probability of symbols that do not appear in the sample. Estimating the missing mass is a basic problem in statistics, machine learning and related fields, which dates back to the early work of Laplace, and the more recent seminal contribution of Good and Turing. The missing mass problem has many applications in a variety of domains. For example, given the observed population of Covid mutations, what is the probability that we encounter a new variant that has not been seen before? In this work we introduce a generalized framework for missing mass estimation. Our framework provides novel risk bounds and improves upon currently known estimation schemes. Importantly, it is easy to apply and does not require additional modeling assumptions. This makes it a favorable choice for many practical setups. Furthermore, we show that by utilizing our scheme, we improve the estimation accuracy of large alphabet probability distributions.

Amichai Painsky is a Senior Lecturer (Assistant Professor) at Tel Aviv University. Amichai received his B.Sc. in Electrical Engineering from Tel Aviv University (2007), his Masters in Electrical Engineering from Princeton University (2009) and his Ph.D. in Statistics from Tel Aviv University (2016). Following his graduation, Amichai joined Massachusetts Institute of Technology (MIT) as a postdoctoral fellow (2019). Amichai's research focuses on statistical inference and learning, and their connection to information theory.

>> Li​​​n​k ​to L​ecture​​​​​

Rank Aggregation with Proportionate Fairness

By: Baruch Schieber
(Host: Michael Elkin)
Mar. 15, 2022
Bldg. 37, Room 202

>> ​Zoom Link
Given multiple individual rank orders over a set of candidates or items, where the candidates belong to multiple (non-binary) protected groups, we study the classical rank aggregation problem under proportionate or p-fairness (RAPF in short), considering Kemeny distance.
We first study the problem of producing a closest p-fair ranking for an individual ranked order (IPF in short) considering Kendall-Tau distance, and present multiple solutions for IPF. We then present a deterministic algorithm and a randomized algorithm to solve RAF that leverage the solutions of IPF as a subroutine.
We make several algorithmic contributions: (i) we prove that when the group protected attribute is binary, IPF could be solved exactly using a greedy technique; (ii) when the group protected attribute is multi-valued, solving IPF is non-trivial: we present two different solutions for it, one is exact under the assumption that the number of attribute values is constant, and the other admits a 2 approximation factor; (iii) we present the guiding principle of designing the solution for RAPF with an approximation factor that is 2+ the approximation factor of the IPF solution, resulting in 3 and 4 approximation algorithms.
We run extensive experiments using multiple real world and large scale synthetic datasets and compare our proposed solutions against multiple state-of-the-art related works to demonstrate the effectiveness and efficiency of our studied problem and proposed solution.
This is joint work with Senjuti Basu Roy, Mouinul Md Islam, and Dong Wei

>> Li​​​n​k ​to L​ecture​​​​

Sequential and distributed clustering algorithms

By: Tom Hess
(Supervisor: Prof. Sivan Sabato)
Mar. 1, 2022

>> ​Zoom Link
This seminar is about sequential and distributed clustering.
First, I introduce and study the setting of no-substitution sequential k-median clustering. In this setting, an i.i.d. sequenceof examples is observed. An example can be selected as a center only immediately after it is observed, and it cannot be substituted later.
The goal is to select a set of centers with a good k-median cost on the distribution generated the sequence.
I provide algorithms for this setting in a general bounded metric space and show that these algorithms obtain a similar cost guarantee for the best offline algorithm. In addition, I study the k-median clustering under the sequential no-substitution setting, without any structural assumption on the metric space. I give the first algorithm for this setting that obtains a constant approximation factor on the optimal cost, an exponential improvement over previous work. Moreover, the number of selected centers is only quasi-linear in k.
Second, I provide a new k-means algorithm for the distributed setting, where the data is distributed across many machines, and a coordinator communicates with these machines to calculate the output clustering. This algorithm guarantees a cost approximation factor and a number of communication rounds that depend only on the computational capacity of the coordinator. Moreover, the algorithm includes a built-in stopping mechanism, which allows it to use fewer communication rounds whenever possible. I show both theoretically and empirically that in many natural cases, indeed 1-4 rounds suffice.

>> Lin​k ​to L​ecture​​​

Optimal Linear Numeric Planning

By: Dr. Alexander Shleyfman
(Host: Prof. Ronen Brafman​)
Feb. 1, 2022

>> Zoom Link
Forward search, such as A*, equipped with an informative heuristic and a strong pruning technique, proved to be one of the most efficient methods to solve planning problems. Most of these heuristics and pruning techniques, however, are currently limited by an assumption that all variables involved have finite domains. While numeric variables play an important, sometimes central, role in many planning problems arising from real world scenarios, most of the currently available heuristic search planners either do not support such variables or impose heavy restrictions on them. In particular, most current planners do not support heuristics and pruning techniques that account for linear numeric effects. The contribution of our work is twofold.
First, we extend the symmetry breaking pruning methods such as DKS and Orbit Space Search, proposed by Domshlak et al. (2012, 2015) to support actions with linear conditions and effects.  Our experiments further show that symmetry reduction can yield a substantial reduction in search effort even if the algorithm is equipped with a strong heuristics.
Second, we adapt the well-known classical planning heuristic LM-cut (Helmert and Domshlak, 2009) to account for both additive constant and linear effects, where for linear effect we show a way to efficiently exploit specific variable structures. Empirical comparison shows that the proposed LM-cut heuristic favorably competes with the currently available state-of-the-art heuristics and achieves significant improvement in coverage.

Alex did his Master's and PhD degrees in the field of  Artificial Intelligence at the Faculty of Industrial Engineering and Management at the Technion under the supervision of Prof. Carmel Domshlak. After that, he moved to Canada for a postdoctoral fellowship at the University of Toronto, where he was hosted by Prof. J. Christopher Beck, and worked on resource management for cloud services and numeric planning. Today, Alex is back in Israel. He is a postdoctoral fellow at the Technion, at the Cognitive Robotics lab, hosted by Prof. Erez Karpas.

>> Link ​to L​ecture​​

On the Role of Data in Algorithm Design

By: Tal Wagner
(Host: Prof. Sivan Sabato)
Jan. 11, 2022
Bldg. 37, Room 202

>> Zoom Link
​Recently, there has been a growing interest in harnessing the power of big datasets and modern machine learning for designing new scalable algorithms. This invites us to rethink the role of data in algorithm design: not just as the input to pre-designed algorithms, but also a factor that enters the algorithm design process itself, driving it in a strong and possibly automated manner. This talk will show how to leverage data and learning for better algorithm design in two fundamental areas: high-dimensional similarity search and efficient linear algebra.
In particular, I will show the following:
1. Using data-dependent compression, we obtain optimal compressed representations of high-dimensional Euclidean distances. This result marks the first improvement over classical data-oblivious compression schemes, which provably cannot match its performance.
2. Using neural networks, we show how to learn to construct better data structures for high-dimensional similarity search.
3. We then show how those techniques also give rise to fast algorithms for low rank approximation of matrices.
Our algorithms are both proven analytically and implemented and validated empirically, showing improvements over previous methods on standard benchmark datasets.

Tal Wagner is a postdoctoral researcher in the Machine Learning Foundations group at Microsoft Research Redmond. His research interests are in designing algorithms for massive high-dimensional datasets and large-scale machine learning. He received his PhD from the EECS department at MIT in September 2020. Previously, he earned his MSc from the Weizmann Institute and BSc from the Technion. During his PhD, he spent time as a research intern in Microsoft, Amazon and VMware.​

Information in Mechanism Design

By: Alon Eden
(Host: Dr. Sigal Oren)
Jan. 4, 2022
Bldg. 37, Room 202

>> Zoom Link
The modern economy is becoming highly digital, demanding a combination of both economic and computational techniques. The abundance of data and the unique nature of digital markets highlight the special role of information in today's economic systems. In this talk, I will discuss two domains of interest. The first is auction design in the interdependent value (IDV) setting. In IDV settings, buyers hold partial information regarding the quality of the good being sold, but their actual values also depend on information that other buyers have about the good. The second is indirect mechanism design, in which the economic system can only observe partial information regarding the buyers' preferences, if any. In both domains, impossibilities are known for very basic settings. I will show how my research has pushed the boundary of what is possible via a computational approach. For IDV settings, I utilize techniques from approximation algorithms to obtain the first general positive results for combinatorial auctions. For indirect mechanisms, I initiate the use of reinforcement learning for the design of such mechanisms.

>> Link ​to Lecture​​​​

Learning with Less Data and Labels for Language Acquisition and Understanding

By: Elior Sulem
(Host: Prof. Michael Elhadad)
Jan. 2, 2022
Bldg. 37, Room 202

>> Zoom Link
Natural Language Processing has attracted a lot of interest in recent years and has seen large improvements with the use of contextualized neural language models. However, the progress is limited to specific tasks where large datasets are available and models are often brittle outside of these datasets. Also, current models are usually pretrained on extremely large unlabeled datasets, which limits our understanding of low-resource scenarios and makes their use unfeasible for many in the academia and industry. On the other hand, children learn with much less data, which makes the study of child language acquisition an appealing research direction to address these issues. In the first part of the talk, I will focus on pretraining on unlabeled data and show what can be learned with an input both quantitatively and qualitatively comparable to that of an average English-speaking 6 year old. In the second part of the talk I will focus on Natural Language Understanding and show how zero-shot and transfer learning strategies can be used to go beyond task-specific training. In particular, I will present a zero-shot approach to Event extraction, which is usually based on the annotation of large domain-specific corpora, using Textual Entailments (TE) and Question-Answering (QA) tasks. I will show the challenge of missing arguments in this context, presenting new models that identify unanswerable questions, leveraging different QA and TE tasks.

Elior Sulem is a Postdoctoral Researcher at the Department of Computer and Information Science at the University of Pennsylvania, working with Prof. Dan Roth on computational semantics, event extraction and computational psycholinguistics. He completed his PhD. in the School of Computer Science and
Engineering at the Hebrew University of Jerusalem under the supervision of Prof. Ari Rappoport in 2019, working on the integration of semantic information into text simplification and machine translation. Before that, he graduated with a master's degree (magna cum laude) in Cognitive Science at the Hebrew University of Jerusalem, under the supervision of Prof. Ari Rappoport (Cognitive Sciences Department's prize for outstanding thesis). He previously completed a B.Sc. degree in Mathematics (extended program) and
Cognitive Sciences at the Hebrew University. He was a fellow of the Language, Logic and Cognition Center from 2012 to 2016. He received the Best Paper Award Runner Up at CoNLL 2021.​

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Links to related papers:

Informed Data Science

By: Amir Gilad
(Host: Prof. Ehud Gudes)
Dec. 28, 2021
Bldg. 37, Room 202

>> Zoom Link
​Data science has become prevalent in various fields that affect day-to-day lives, such as healthcare, banking, and the job market. The process of developing data science applications usually consists of several automatic systems that manipulate and prepare the data in different manners. Examples of automatic data manipulations and preparations include generating synthetic data, exploring it, repairing the data, and labeling it for machine learning. These systems can be highly complex and even data scientists can find it difficult to understand and verify their output. Moreover, uninformed use of these systems can lead to errors that may affect the quality of the results of such applications.

In the talk, I will highlight prominent challenges in the data science process and present three approaches for addressing them. In particular, I will present a solution that generates natural language explanations for query results, a tool for generating synthetic linked data, and a solution that explains complex queries using abstract database instances.


Amir Gilad is a postdoctoral researcher in the Database group at Duke University. He received his Ph.D. in Computer Science from Tel Aviv university under the supervision of Prof. Daniel Deutch. His work focuses on developing tools and algorithms that assist users in understanding and gaining insights into data and the systems that manipulate it. His research relates to classic database tools such as data provenance, as well as natural language processing, causal inference, and privacy.

Amir is the recipient of the VLDB best paper award, the SIGMOD research highlight award, and the Google Ph.D. fellowship for Structured Data and Database Management.

Sublinear-time graph algorithms: motif counting and uniform sampling

By: Talya Eden
(Host: ​​Prof. Michael Elkin)
Dec. 21, 2021
Bldg. 37, Room 202

>> Zoom Link
​In this talk I will survey recent developments in approximate subgraph-counting and subgraph-sampling in sublinear-time. Counting and sampling small subgraphs (aka motifs) are two of the most basic primitives in graph analysis, and have been studied extensively, both in theory and in practice. In my talk, I will present the sublinear-time computational model, where access to the input graph is given only through queries, and will explain some of the concepts that underlie my results. I will then explain how we can get improved bounds for a very natural graph family: the family of bounded arboricity graphs. Finally, I'll present a recent application, where again we take advantage of the unique structure of social networks in order to get improved bounds for a very basic and well-studied problem.


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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>> Link ​to Lecture​​​

Harnessing Scientific Literature for Boosting Discovery and Innovation

By: Tom Hope
(Host:​ Dr. Yuval Pinter)
Dec. 14, 2021
Bldg. 37, Room 202

>> Zoom Link
With millions of scientific papers coming out every year, researchers are forced to allocate their attention to increasingly narrow areas, creating isolated “research bubbles” that limit knowledge discovery. This fragmentation of science slows down progress and prevents the formation of cross-domain links that catalyze breakthroughs. Toward addressing these large-scale challenges for the future of science, my work develops new paradigms for searching, recommending and discovering scholarly knowledge.
In this talk, I will present methods and systems for recommending research inspirations and creating links across areas and ideas. My approach is based on extracting novel structural representations of scholarly literature, and leveraging them for "matchmaking" between problems and solutions and between scientists. I will also present a new task we introduced for jointly addressing diversity, ambiguity and hierarchy of language in scientific texts. In this new setting, the goal is to learn to construct cross-document coreference clusters along with a hierarchy defined over the clusters. As I will demonstrate, being able to automatically induce this graph structure can help unlock important applications in scientific discovery, but this remains a highly difficult open problem.

Tom Hope is a postdoctoral researcher at The Allen Institute for AI (AI2) and The University of Washington, working with Daniel Weld on accelerating scientific discovery and closely collaborating with Eric Horvitz, CSO at Microsoft. Tom completed his PhD with Dafna Shahaf at the Hebrew University of Jerusalem in January 2020. His work has received four best paper awards, appeared in top venues (PNAS, KDD, EMNLP, NAACL, WSDM, CHI, AKBC, IEEE), and received media attention from Nature and Science on his systems for COVID-19 researchers. In parallel to his PhD studies Tom created and led an applied AI research team at Intel that published award-winning work. Tom was selected for the 2021 Global Young Scientists Summit and 2019 Heidelberg Laureate Forum, and was a member of the KDD 2020 Best Paper Selection Committee.

>> Link to Lecture​​
Large-scale multi-robot systems: From algorithmic foundations to smart-mobility applications

By: Kiril Solovey
(Host: Prof. Ronen Brafman)​​
Dec. 7, 2021
Bldg. 37, Room 202

>> Zoom Link
​Multi-robot systems are already playing a crucial role in various domains such as manufacturing and warehouse automation. In the future, these systems will be incorporated into our daily lives through drone-delivery services and smart-mobility systems that comprise thousands of autonomous vehicles, to give a few examples. The anticipated benefits of multi-robot systems are numerous, ranging from increased safety and efficiency, to broader societal facets such as sustainability. However, to reap those rewards we must develop algorithms that can adapt rapidly to unexpected changes on a massive scale. Importantly, these algorithms must capture (i) dynamical and collision-avoidance constraints of individual robots, (ii) interactions between multiple robots, and (iii), more broadly, the interaction of those systems with their environment. These considerations give rise to extremely complex and high-dimensional optimization problems that need to be solved in real-time. In this talk, I will present progress on the design of systematic control and decision-making mechanisms to allow the safe, effective, and societally-equitable deployment of multi-robot systems. I will highlight results on fundamental capabilities for multi-robot systems (e.g., motion planning and task allocation), as well as applications in smart-mobility systems. I will also discuss challenges and opportunities for smart mobility in addressing societal issues, including traffic congestion and fairness.

BIO: Kiril Solovey is a roboticist specializing in multi-robot systems and their applications to smart mobility. He is currently a Postdoctoral Scholar in the Faculty of Computer Science at the Technion. Prior to that, he was a postdoc in the Department of Aeronautics and Astronautics, Stanford University. He obtained a PhD in Computer Science from Tel Aviv University, where he was advised by Dan Halperin.

Kiril's research focuses on the design of scalable algorithmic approaches for multi-robot systems. His work draws upon ideas across the disciplines of computer science, engineering, and transportation science, to develop methods with substantial guarantees on solution quality and robustness. He received multiple awards, including the Clore Scholars and Fulbright Postdoctoral Fellowships, best paper awards and nominations (at Robotics: Science and Systems, International Conference on Robotics and Automation, International Symposium on Multi-Robot and Multi-Agent System, and European Control Conference), and teaching awards.

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* Rahul Shome, Kiril Solovey, Andrew Dobson, Dan Halperin, Kostas E.
Bekris: dRRT*: Scalable and informed asymptotically-optimal multi-robot motion planning. Auton. Robots 44(3-4): 443-467 (2020)
* Kiril Solovey, Mauro Salazar, Marco Pavone: Scalable and Congestion-Aware Routing for Autonomous Mobility-On-Demand Via Frank-Wolfe Optimization. Robotics: Science and Systems 2019​
* Devansh Jalota, Kiril Solovey, Karthik Gopalakrishnan, Stephen Zoepf, Hamsa Balakrishnan, Marco Pavone: When Efficiency meets Equity in Congestion Pricing and Revenue Refunding Schemes. EAAMO 2021: 9:1-9:11

>> Link to Lecture​

Efficient Secure Multi-Party Computation: How Low Can We Go?

By: Ariel Nof
(Host: Prof. Amos Beimel)
Nov. 30, 2021
Bldg. 37, Room 202

>> Zoom Link
In the setting of secure multi-party computation, a set of parties with private inputs wish to compute a joint function of their inputs, without revealing anything but the output. This computation is carried-out in the presence of an adversary controlling a subset of the parties, who may try to learn private information or disrupt the computation. The problem of constructing efficient protocols for secure computation has gained significant interest in the last decade or so and progress has been extraordinarily fast, transforming the topic from a notion of theoretical interest only, into a technology that is even being commercialized by multiple companies. A useful metric for measuring efficiency of secure protocols is the ratio between the communication cost of the protocol, and that of the best known protocol with a “minimal" level of security, namely security against semi-honest (passive) adversaries, who act as prescribed by the protocol but try to learn additional information from messages they receive. In this talk, I will describe recent efforts to minimize this ratio and show, in the honest majority setting, new results that close (in the amortized sense) the communication gap between secure protocols against adversaries who act in any arbitrary manner, and protocols against semi-honest adversaries.​

A Theoretical Analysis of Generalization in Graph Convolutional Neural Networks

By: Ron Levie (LMU Munich)
(Host: Dr. Eran Treister)
Nov. 23, 2021
Bldg. 37, Room 202

>> Zoom Link
In recent years, the need to accommodate non-Euclidean structures in data science has brought a boom in deep learning methods on graphs, leading to many practical applications with commercial impact. In this talk, we will review the mathematical foundations of the generalization capabilities of graph convolutional neural networks (GNNs). We will focus mainly on spectral GNNs, where convolution is defined as element-wise multiplication in the frequency domain of the graph.

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:

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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Towards Revealing Visual Relations Between Fixations: Modeling the Center Bias During Free-Viewing

By: Rotem Mairon
(Adviser: Prof. Ohad Ben Shahar)
Nov. 16, 2021
Bldg. 37, Room 202

>> Zoom Link
Understanding eye movements is essential for comprehending visual selection and has numerous applications in computational vision and human-computer interfaces. Eye-movement research has revealed a variety of behavioral biases that guide the eye regardless of visual content.
However, computational models of eye-movement behavior only partially account for these biases. The most well-known of these biases is the center bias, which refers to the tendency of observers to fixate on the stimulus's center. In this study, we investigate two aspects of the center bias to help distinguish it from visual content-driven behavior. One aspect concerns standard procedures for collecting eye-movement data during free viewing. Specifically, the requirement to fixate on the center of the display prior to the appearance of the stimulus, as well as the presentation of the stimulus in the display's center. Our findings support a fundamental shift in data collection and analysis procedures, all in the name of obtaining data that reflects more content-driven and less bias-related eye-movement behavior. The second aspect is how the center bias manifests during viewing, as opposed to how it is reflected in the spatial distribution of aggregated fixations, which is used in almost all computational models of eye-movement behavior. To that end, this work proposes an eye-movement data representation based on saccades rather than fixations. This representation not only demonstrates the dynamic nature of the center bias throughout viewing, but it also holistically demonstrates other eye-movement phenomena that were previously investigated exclusively. Finally, we demonstrate that the proposed representation allows for more accurate modeling of eye-movement biases, which is critical for establishing a baseline for computational models. Such a baseline paves the way for researchers to learn how, if at all, visual content guides the human eye from one fixation to the next while freely viewing natural images.

Mind Reading: Decoding Visual Experience from Brain Activity

By: Guy Gaziv (WIS)
(Host: Dr. Eran Treister)
Nov. 9, 2021
Bldg. 37, Room 202

Reconstructing and semantically classifying observed natural images from novel (unknown) fMRI brain recordings is a milestone for developing brain-machine interfaces and for the study of consciousness. Unfortunately, acquiring sufficient “paired” training examples (images with their corresponding fMRI recordings) to span the huge space of natural images and their semantic classes is prohibitive, even in the largest image-fMRI datasets. We present a self-supervised deep learning approach that overcomes this barrier, giving rise to better generalizing fMRI-to-image decoders. This is obtained by enriching the scarce paired training data with additional easily accessible “unpaired” data from both domains (i.e., images without fMRI, and fMRI without images). Our approach achieves state-of-the-art results in image reconstruction from fMRI responses, as well as unprecedented large-scale (1000-way) semantic classification of never-before-seen classes.

Guy Gaziv is a postdoctoral fellow in the Michal Irani Computer Vision group at The Weizmann Institute of Science. This spring he will be starting his postdoc at the DiCarlo Lab at MIT. His PhD research focused on the intersection between machine and human vision, and specifically on decoding visual experience from brain activity. Guy earned his BSc in Electrical and Computer Engineering from The Hebrew University of Jerusalem, and his MSc in Physics and PhD in Computer Science from The Weizmann Institute of Science.

Research-oriented gathering of students and faculty members​​

By: Various Faculty Members
(Host: Dr. Eran Treister)
Nov. 2, 2021
Bldg. 37, Room 202

You are invited for a small research-oriented gathering of students and faculty members on Nov 2nd, at 12:00, room 202/37 (invitation attached). The gathering is mostly intended for new students who did not find an adviser yet, but everyone is welcome.

There will be six faculty members presenting their research briefly, two of them are new in the department. These faculty members and others that will also attend the meeting are looking for students, and this is a good opportunity to talk to them. We will also encourage faculty members to be available for personal meetings with you.​

So - if you haven't done so already, we encourage you to visit our department's research page

and follow the links according to your field(s) of interest. Try to read about the research fields and visit the web-pages of the relevant faculty members. Once you find a faculty member that looks relevant - email him/her and try to schedule a meeting (optionally, but not necessarily on that day).

Scaling laws for deep learning

By: Jonathan Rosenfeld​
(Host: Dr. 
Eran Treister)
Oct. 26, 2021

>> Zoom Link
​Running faster will only get you so far -- it is generally advisable to first understand where the roads lead, then get a car ...

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.​

Expanding the hub and spoke model

By: Clara Shikhelman (Chaincode Labs)​
(Host: Dr. Or Sattath)
Oct. 19, 2021
Bldg. 37, Room 202

>> Zoom Link
When two parties transact often using banks, Bitcoin, or any other financial device that entails fees, they might choose to turn to second-layer solutions. In the​se solutions, both parties lock a certain amount as escrow in a channel and transact freely with reduced fees. This gives rise to a network of channels in which any two nodes can transact with each other.

A downside of these solutions, especially if they are decentralized, is the tendency of the network toward the hub and spoke model. Most nodes in this model, the “spokes", are connected to a single “hub", a high degree node that the spokes believe to be well connected. This graph topology is known to be prone to various attacks and privacy issues.

In this work, we present a solution that benefits the spokes, by saving on costs, and the network, by giving it higher connectivity and good expansion properties. We use probabilistic and spectral techniques for the proofs.​

Short Bio: Clara Shikhelman holds a PhD in mathematics from Tel Aviv University. Following a year at Simons institute in Berkeley and CalTech, she is currently a PostDoc at Chaincode Labs.