- talks - lists.cs.princeton.edu

TIME CHANGE -- ECE Korhammer Seminar: Meir Feder, Tel Aviv University (Wed., Sept. 20, 2023 @ 12:30pm, B205)
by Emily C. Lawrence 15 Sep '23

15 Sep '23

ECE KORHAMMER SEMINAR SERIES Speaker: Meir Feder, Tel Aviv University Title: Multiple and Hierarchical Universality Day: September 20, 2023 Time: 12 :30pm Location: B205 Engineering Quadrangle Hosts: H. Vincent Poor and Andrea Goldsmith Registration Required: [ https://forms.gle/jEZviiAmp7c1NzvG6 | https://forms.gle/jEZviiAmp7c1NzvG6 ] Abstract: Universal coding, prediction and learning usually consider the case where the data generating mechanism is unknown or non-existent, and the goal of the universal scheme is to compete with the best hypothesis from a given hypothesis class, either on the average or in a worst-case scenario. Multiple universality considers the case where the hypothesis class is also unknown: there are several hypothesis classes with possibly different complexities. In hierarchical universality, the simpler classes are nested within more complex classes. The main challenge is to correctly define the universality criterion so that the extra “regret” for not knowing the class is monitored. We propose several possible definitions and derive their min-max optimal solutions. Interestingly, the proposed approach can be used to obtain Elias codes for universal representation of the integers. We further utilize this approach for variable-memory Markov models (unifilar models), presenting a new interpretation for the bound over the regret of the celebrated context-tree weighting algorithm, and suggest a multiple universality approach for general linear models, including linear regression, and logistic regression and Perceptrons. Finally, we conjecture that multiple universality, with its non-uniform convergence and regret bounds, can be applied to explain and design learning schemes in general “over-parameterized” model classes such as deep neural networks, transformers and so on. Joint work with Yaniv Fogel. Bio: Meir Feder received the B.Sc and M.Sc degrees in Electrical Engineering in 1980 and 1984 from Tel-Aviv University and the Sc.D degree in Electrical Engineering and Ocean Engineering in 1987 from the Massachusetts Institute of Technology (MIT) and the Woods Hole Oceanographic Institution (WHOI). After being a Research Associate and a Lecturer in MIT, he joined the School of Electrical Engineering, Tel-Aviv University, where he is now the Jokel Chaired Professor and the head of the newly established Tel-Aviv university center for Artificial intelligence and Data science (TAD). He is also a Visiting Professor with the Department of EECS, MIT. Parallel to his academic career, he is closely involved with the high-tech industry. He founded 5 companies, among them Peach Networks that developed an interactive TV solution (Acq: MSFT) and Amimon that provided the highest quality, robust, no latency wireless high-definition A/V connectivity (Acq:LON.VTC). Recently, with his renewed interest in machine learning and AI, he cofounded Run:ai, a virtualization, orchestration, and acceleration platform for AI infrastructure. He is also an active angel investor and serves on the board/advisory board of several US and Israeli companies. Prof. Feder received several academic and professional awards including the IEEE Information Theory Society best paper award for his work on universal prediction, the “creative thinking” award of the Israeli Defense Forces and the Research Prize of the Israeli Electronic Industry, awarded by the President of Israel. For the development of Amimon’s chip-set, that uses a unique MIMO implementation of joint source-channel coding for wireless video transmission, he received the 2020 Scientific and Engineering Award of the Academy of Motion Picture Arts and Sciences (Oscar). This seminar is supported with funds from the Korhammer Lecture Series.

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DeCenter Seminar Series: Wednesday, September 20 at 4:30
by Emily C. Lawrence 13 Sep '23

13 Sep '23

[ https://decenter.princeton.edu/ | DeCenter Seminar Series ] Title: An Overview of Mechanism Design for Blockchain Applications Speaker: Matt Weinberg, Princeton University Date: Wednesday, September 20, 2023 - 4:30 pm Location: Friend Center Convocation Room (Friend 113) Reception following talk. [ https://decenter.princeton.edu/events/decenter-seminar-an-overview-of-mecha… | https://decenter.princeton.edu/events/decenter-seminar-an-overview-of-mecha… ] Abstract: Blockchains rely on an underlying consensus protocol to ensure consistency of their ledger. Classically, these protocols are proven secure assuming that an attacker controls at most (say) 33% of the network, and the remaining portion of the network honestly follows the protocol. This analysis is necessary but overlooks an important aspect: participants earn rewards by participating, and all participants, honest or not, will happily deviate from the protocol if it yields greater rewards. In this talk, I'll overview several challenges to designing consensus protocols that correctly incentivize participants to follow them. This talk will not assume any particular technical background, although it will have technical content. Bio: Matt Weinberg is an Associate Professor of Computer Science at Princeton University. His primary research interest is in algorithmic mechanism design: algorithm design in settings where users have their own incentives. He is also interested more broadly in algorithmic game theory, algorithms under uncertainty, and theoretical computer science in general. Before joining the faculty at Princeton, Weinberg spent two years as a postdoc in Princeton’s CS theory group and was a research fellow at the Simons Institute during the Fall 2015 (economics and computation) and Fall 2016 (algorithms and uncertainty) semesters. He completed his PhD in 2014 at MIT, where he was advised by Costis Daskalakis. Prior to that, he graduated from Cornell University with a BA in Math in 2010, where he worked with Bobby Kleinberg. If you need an accommodation for a disability, please contact [ mailto:decenter@princeton.edu | decenter(a)princeton.edu ] at least one week before the event.

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Princeton Precision Health Fall Symposium September 29 at the Friend Center
by Emily C. Lawrence 12 Sep '23

12 Sep '23

Princeton Precision Health (PPH) is holding an all-day symposium on interdisciplinary, date-driven approaches to precision medicine on September 29 at the Friend Center. Provost Jennifer Rexford will give opening remarks and keynote speakers include Hana El-Samad, Senior Vice President and Director of Science Integration, Innovation & Insights, Altos Labs and Garret FitzGerald, MD, Robert L. McNeil, Jr. Professor in Translational Medicine and Therapeutics, Associate Dean for Translational Research, University of Pennsylvania Perelman School of Medicine. Registration is free but required for an accurate head count for breakfast, lunch, and closing reception: [ https://pph.princeton.edu/form/fall-symposium-registration | https://pph.princeton.edu/form/fall-symposium-registration ] The full program can be viewed here: [ https://pph.princeton.edu/fall-symposium-program | https://pph.princeton.edu/fall-symposium-program ] Hope to see you there! Olga *************************************************************** Olga Troyanskaya Director, Princeton Precision Health Professor of Computer Science and the Lewis-Sigler Institute for Integrative Genomics Princeton University

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Princeton Robotics Seminar: Vikas Sindhwani, Friday June 9 at 11am
by Emily C. Lawrence 12 Sep '23

12 Sep '23

Speaker: Vikas Sindhwani, Senior Staff Research Scientist, Google Deepmind Date: Friday, June 9, 2023 Time: 11am ET Location: Virtual, Zoom link: [ https://princeton.zoom.us/my/robotics | https://princeton.zoom.us/my/robotics ] Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Large Language Models with Eyes, Arms and Legs Abstract: To become useful in human-centric environments, robots must demonstrate language comprehension, semantic understanding and logical reasoning capabilities working in concert with low-level physical skills. With the advent of modern "foundation models" trained on massive datasets, the algorithmic path to developing general-purpose “robot brains” is (arguably) becoming clearer, though many challenges remain. In the first part of this talk, I will attempt to give a flavor of how state-of-the-art multimodal foundation models are built, and how they can be bridged with low-level control. In the second part of the talk, I will summarize a few surprising lessons on control synthesis observed while solving a collection of Robotics benchmarks at Google. I will end with some emerging open problems and opportunities at the intersection of dynamics, control and foundation models. Bio: Vikas Sindhwani is Research Scientist at Google Deepmind in New York where he leads a research group focused on solving a range of planning, perception, learning, and control problems arising in Robotics. His interests are broadly in core mathematical foundations of statistical machine learning, and in end-to-end design aspects of building large-scale and robust AI systems. He received the best paper award at Uncertainty in Artificial Intelligence (UAI-2013), the IBM Pat Goldberg Memorial Award in 2014, and was finalist for Outstanding Planning Paper Award at ICRA-2022. He serves on the editorial board of Transactions on Machine Learning Research (TMLR) and IEEE Transactions on Pattern Analysis and Machine Intelligence; he has been area chair and senior program committee member for NeurIPS, International Conference on Learning Representations (ICLR) and Knowledge Discovery and Data Mining (KDD). He previously headed the Machine Learning group at IBM Research, NY. He has a PhD in Computer Science from the University of Chicago and a B.Tech in Engineering Physics from Indian Institute of Technology (IIT) Mumbai. His publications are available at: http://vikas.sindhwani.org/

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ECE Korhammer Seminar: Meir Feder, Tel Aviv University (Wed., Sept. 20, 2023 @ 4:30pm, B205)
by Emily C. Lawrence 11 Sep '23

11 Sep '23

ECE KORHAMMER SEMINAR SERIES Speaker: Meir Feder, Tel Aviv University Title: Multiple and Hierarchical Universality Day: September 20, 2023 Time: 4:30pm Location: B205 Engineering Quadrangle Hosts: H. Vincent Poor and Andrea Goldsmith Abstract: Universal coding, prediction and learning usually consider the case where the data generating mechanism is unknown or non-existent, and the goal of the universal scheme is to compete with the best hypothesis from a given hypothesis class, either on the average or in a worst-case scenario. Multiple universality considers the case where the hypothesis class is also unknown: there are several hypothesis classes with possibly different complexities. In hierarchical universality, the simpler classes are nested within more complex classes. The main challenge is to correctly define the universality criterion so that the extra “regret” for not knowing the class is monitored. We propose several possible definitions and derive their min-max optimal solutions. Interestingly, the proposed approach can be used to obtain Elias codes for universal representation of the integers. We further utilize this approach for variable-memory Markov models (unifilar models), presenting a new interpretation for the bound over the regret of the celebrated context-tree weighting algorithm, and suggest a multiple universality approach for general linear models, including linear regression, and logistic regression and Perceptrons. Finally, we conjecture that multiple universality, with its non-uniform convergence and regret bounds, can be applied to explain and design learning schemes in general “over-parameterized” model classes such as deep neural networks, transformers and so on. Joint work with Yaniv Fogel. Bio: Meir Feder received the B.Sc and M.Sc degrees in Electrical Engineering in 1980 and 1984 from Tel-Aviv University and the Sc.D degree in Electrical Engineering and Ocean Engineering in 1987 from the Massachusetts Institute of Technology (MIT) and the Woods Hole Oceanographic Institution (WHOI). After being a Research Associate and a Lecturer in MIT, he joined the School of Electrical Engineering, Tel-Aviv University, where he is now the Jokel Chaired Professor and the head of the newly established Tel-Aviv university center for Artificial intelligence and Data science (TAD). He is also a Visiting Professor with the Department of EECS, MIT. Parallel to his academic career, he is closely involved with the high-tech industry. He founded 5 companies, among them Peach Networks that developed an interactive TV solution (Acq: MSFT) and Amimon that provided the highest quality, robust, no latency wireless high-definition A/V connectivity (Acq:LON.VTC). Recently, with his renewed interest in machine learning and AI, he cofounded Run:ai, a virtualization, orchestration, and acceleration platform for AI infrastructure. He is also an active angel investor and serves on the board/advisory board of several US and Israeli companies. Prof. Feder received several academic and professional awards including the IEEE Information Theory Society best paper award for his work on universal prediction, the “creative thinking” award of the Israeli Defense Forces and the Research Prize of the Israeli Electronic Industry, awarded by the President of Israel. For the development of Amimon’s chip-set, that uses a unique MIMO implementation of joint source-channel coding for wireless video transmission, he received the 2020 Scientific and Engineering Award of the Academy of Motion Picture Arts and Sciences (Oscar). This seminar is supported with funds from the Korhammer Lecture Series.

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New Seminar Series - ML & Physics
by Elizabeth Wang 06 Sep '23

06 Sep '23

Greetings, Professor Boris Hanin from Operations Research & Financial Engineering would like to share the following seminar series with COS students, postdocs, and faculty. If you have any questions, please contact Boris at [ mailto:bhanin@princeton.edu | bhanin(a)princeton.edu ] . >From Boris: Dear Colleagues, We wanted to bring to your attention a new seminar series, Machine Learning in Physics, focused on interdisciplinary work at the intersection of physics and ML. Here are the details: What ? A seminar series highlighting research on both physics-inspired approaches to understanding ML and the use of ML for physics applications When/Where ? Wednesdays 4:30 - 5:30pm in Jadwin Hall, Room A10 with light refreshments after the presentation. Who ? The speakers for Fall 2023 are as follows: * Sept. 27 : Shirley Ho (Flatiron) * Oct. 4 : Juan Felipe Carrasquilla Alvarez (Vector) * Nov. 1: Javier Duarte (UCSD) * Nov. 15 : Phiala Shananan (MIT) * Nov 29 : Andy Leifer (Princeton) For more details — and to sign up for the seminar mailing list — please see the seminar [ https://ml.physics.princeton.edu/ | website ] . Best, Boris Hanin, Gage DeZoort, Dan Marlow, Mariangela Lisanti, Peter Elmer (seminar co-organizers)

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“Tony” Runzhe Yang will present his FPO "From mind to machine: neural circuits, learning algorithms, and beyond." on Tuesday, September 26, 2023 at 12:00 PM in CS 105 and Zoom.
by CS Grad Department 06 Sep '23

06 Sep '23

“Tony” Runzhe Yang will present his FPO "From mind to machine: neural circuits, learning algorithms, and beyond." on Tuesday, September 26, 2023 at 12:00 PM in CS 105 and Zoom. Location: Zoom Link: [ https://princeton.zoom.us/j/91084214303 | https://princeton.zoom.us/j/91084214303 ] The members of Runzhe’s committee are as follows: Examiners: H. Sebastian Seung (Adviser), Karthik Narasimhan (Adviser), Mala Murthy Readers: Ryan Adams, Dmitri Chklovskii (Flatiron Institute; NYU) A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: This thesis explores diverse topics within computational neuroscience and machine learning. The work begins by examining the organization of biological neuronal circuits re- constructed by electron microscopy. First, our study of neural connectivity patterns in the mouse primary visual cortex illustrates the necessity for refined understanding of the non- random features of cortical connections, challenging conventional perspectives. Second, in the larval zebrafish hindbrain, our novel discovery highlights overrepresented three-cycles of neuron, an observation unprecedented in electron microscopy-reconstructed neuronal wiring diagrams. Additionally, I present an exhaustive compilation of motif statistics and network characteristics for the complete adult Drosophila brain. These efforts collectively enrich our understanding of the intricate wiring diagram of neurons, offering new insights into the organizational principles of biological brains. In the second part of the thesis, I introduce three distinct machine learning algorithms. The first algorithm, a biologically plausible unsupervised learning algorithm, is implemented within artificial neural networks using Hebbian feedforward and anti-Hebbian lateral connections. The theoretical discourse explores the duality and convergence of the learning process, connecting with the generalized concept of the “correlation game” principle. The second algorithm presents a novel multi-objective reinforcement learning approach, adept at managing real-world scenarios where multiple potentially conflicting criteria must be optimized without predefined importance weighting. This innovation allows the trained neural network model to generate policies that align optimally with user- specified preferences across the entire space of preference. The third algorithm employs a cognitive science-inspired learning principle for dialog systems. The designed system engages in negotiation with others, skillfully inferring the intent of the other party and predicting how its responses may influence the opponent’s mental state. Collectively, these contributions shed light on the complexities of neural circuit organization and offer new methodologies in machine learning. By examining intelligence from both biological and computational perspectives, the thesis presents insights and reference points for future research, contributing to our growing understanding of intelligence.

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CS seminar speaker Sriram Sankararaman: Tuesday, September 5 at 4:30pm
by Emily C. Lawrence 29 Aug '23

29 Aug '23

Speaker: Sriram Sankararaman, from UCLA Date: Tuesday, September 5, 2023 Time: 4:30pm ET Location: CS 105 Host: Ben Raphael Event page: [ https://www.cs.princeton.edu/events/26490 | https://www.cs.princeton.edu/events/26490 ] Title: The Role of Archaic Admixture in Human Evolution Abstract: Over the past decade, the ability to sequence genomes from both present-day and archaic humans (including our closest evolutionary relatives, the Neanderthals) has transformed our understanding of human history. Analyzing these genome sequences paints a picture of human history in which present-day humans migrated out of Africa but exchanged genes with multiple archaic human populations. I will describe statistical methods that identify segments of DNA inherited from archaic humans that are surviving in our genomes today and how these maps of introgressed archaic DNA are providing insights into human migration and biology. Despite this progress, our understanding of the contribution of archaic introgression to populations in Africa remains limited, in part due to the challenges in obtaining ancient DNA in Africa. Leveraging recently developed approaches that enable inferences about archaic populations without access to their genome sequences, we show that west African populations today inherit substantial genetic ancestry from an as-yet-unidentified archaic ghost population that diverged prior to the split of modern humans and Neanderthals. Finally, we combine maps of introgressed Neanderthal DNA with phenotypic datasets collected in hundreds of thousands of individuals to assess the contribution of introgressed Neanderthal DNA to complex traits. I will discuss the implications of these results for our understanding of human evolution as well as the statistical challenges that need to be solved in this endeavor. Bio: Sriram Sankararaman is a professor in the Departments of Computer Science, Human Genetics, and Computational Medicine at UCLA. His research interests lie at the interface of computer science, statistics and biology. His lab develops machine learning algorithms to analyze genomic data and biomedical data with the broad goal of understanding the interplay between evolution, genomes and traits. He received a B.Tech. in Computer Science from the Indian Institute of Technology, Madras, a Ph.D. in Computer Science from UC Berkeley and was a postdoctoral fellow in Harvard Medical School before joining UCLA. He is a recipient of a NSF Career Award, NIH Pathway to Independence Award, and fellowships from Microsoft Research, the Sloan Foundation, the Okawa Foundation and the Simons Institute.

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Princeton Symposium on Biological & Artificial Intelligence - October 19-20, 2023 - Free Registration
by Elizabeth Wang 17 Aug '23

17 Aug '23

Please see attached and below regarding an upcoming symposium.

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Mingzhe Wang will present his FPO "Deep Learning for Automated Theorem Proving" on Monday, August 21, 2023 at 3:00 PM in CS 301 and Zoom.
by CS Grad Department 07 Aug '23

07 Aug '23

Mingzhe Wang will present his FPO "Deep Learning for Automated Theorem Proving" on Monday, August 21, 2023 at 3:00 PM in CS 301 and Zoom. Location: Zoom link: [ https://princeton.zoom.us/j/3294749300 | https://princeton.zoom.us/j/3294749300 ] The members of Mingzhe’s committee are as follows: Examiners: Jia Deng (Adviser), Danqi Chen, Karthik Narasimhan Readers: Andrew Appel, Sanjeev Arora A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Automated theorem proving is a fundamental AI task with broad applications to the formalization of mathematics, software and hardware verification, and program synthesis. Deep learning has emerged as a promising approach for guiding theorem provers. In this dissertation, we present our work on developing deep learning approaches for automated theorem proving. First, we propose FormulaNet, a deep learning-based approach to the problem of premise selection. FormulaNet represents a logical formula as a graph that is invariant to variable renaming and then embeds the graph into a vector via a novel graph embedding method that preserves the information of edge ordering. Our approach achieves state-of-the-art results on the HolStep dataset, improving the classification accuracy from 83% to 90.3%. Next, we propose MetaGen, a neural generator that automatically synthesizes theorems and proofs for the purpose of training a theorem prover. On real-world tasks, we demonstrate that synthetic data from our approach improves the theorem prover and advances the state of the art of automated theorem proving in Metamath. Finally, we extend our theorem generator to the interactive theorem prover Lean. We propose TermGen, a neural generator that automatically synthesizes theorems and proofs in Lean by directly constructing proof terms. We also propose a method of expert iteration for training TermGen to synthesize short theorems. At last, we present a case study of generating formal specifications of while-loop programs through a rule-based theorem generator.

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