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Events for October 31, 2019
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Theory Lunch
Thu, Oct 31, 2019 @ 12:15 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Federico Echenique, Professor of Economics at CalTech
Talk Title: The Edgeworth Conjecture with Small Coalitions and Approximate Equilibria in Large Economies
Abstract: A talk about the paper Federico Echenique worked on with Siddharth Barman entitled "The Edgeworth Conjecture with Small Coalitions and Approximate Equilibria in Large Economies." The paper shows that deciding if an allocation is approximately Walrasian can be done in polynomial time, even if finding the market equilibrium in intractable.
It will be a fun time, so make sure not to miss out!
Host: Shaddin Dughmi
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Cherie Carter
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CS Colloquium: Masahiro Ono (NASA JPL) - Robots in Space: How AI and Machine Learning are Revolutionizing Space Exploration
Thu, Oct 31, 2019 @ 02:00 PM - 03:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Masahiro Ono, NASA JPL
Talk Title: Robots in Space: How AI and Machine Learning are Revolutionizing Space Exploration
Series: Computer Science Colloquium
Abstract: On the third planet of the Solar System, there is an ongoing revolution caused by a group of technologies collectively called as "AI," including, but not limited to, machine learning, optimal decision making, situation awareness, and autonomous navigation. The same revolution is taking off elsewhere in the Solar System, triggered by an advent of HPSC (high-performance spacecraft computing). This talk introduces ongoing research efforts to adapt and enhance latest AI technologies for future exploration missions to Mars, Europa, and Enceladus, such as resource-aware autonomous rover driving, on-board science interpretation, and autonomous descent into Enceladus's vents, which are believed to be connected to the subsurface ocean that may harbor extraterrestrial life.
If you would like to take a peek of the talk, take a look at the following YouTube movies:
https://www.youtube.com/watch?v=-AYvgTlDKQM&t=5s
https://www.youtube.com/watch?v=LJXQ0-a9IJE
https://www.youtube.com/watch?v=7bdS_xpYz7A
This lecture satisfies requirements for CSCI 591: Research Colloquium. *Note: Rescheduled to 10/31/19, 2:00-3:20PM*
Biography: Dr. Masahiro (Hiro) Ono is a Research Technologist at NASA Jet Propulsion Laboratory, California Institute of Technology. His broad interest is centered around the application of robotic autonomy to space exploration, with an emphasis on machine learning applications to perception, data interpretation, and decision making. Before joining JPL in 2013, he was an assistant professor at Keio University in Japan. He graduated from MIT with PhD in Aeronautics and Astronautics in 2012. Since 2017 he is the PI of a JPL-funded Strategic Research and Development task on the machine learning-based analytics for automated rover systems (MAARS). From 2015 he has led the development of machine learning- based Martian terrain classifier, which is used by MSL and Mars 2020 Rover missions. He was also the PI of a JPL-Caltech joint project on imitation learning-based planning from 2016 to 2018. He was awarded two NIAC Phase I studies: Comet Hitchhiker (2014) and Journey to the Center of Icy Moon (2016). Since 2019, he has been the Autonomy Lead of the EELS (Exobiology Extant Life Surveyor) project.
Host: Stefanos Nikolaidis
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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MASCLE Machine Learning Seminar: Joan Bruna (NYU) - On (Provably) Learning with Large Neural Networks
Thu, Oct 31, 2019 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Joan Bruna, New York University
Talk Title: On (Provably) Learning with Large Neural Networks
Series: Machine Learning Seminar Series hosted by USC Machine Learning Center
Abstract: Virtually all modern deep learning systems are trained with some form of local descent algorithm over a high-dimensional parameter space. Despite its apparent simplicity, the mathematical picture of the resulting setup contains several mysteries that combine statistics, approximation theory and optimization, all intertwined in a curse of dimensionality.
In order to make progress, authors have focused in the so-called 'overparametrised' regime, which studies asymptotic properties of the algorithm as the number of neurons grows. In particular, neural networks with a large number of parameters admit a mean-field description, which has recently served as a theoretical explanation for its favorable training properties. In this regime, gradient descent obeys a deterministic partial differential equation (PDE) that converges to a globally optimal solution for networks with a single hidden layer under appropriate assumptions.
In this talk, we will review recent progress on this problem, and argue that such framework might provide crucial robustness against the curse of dimensionality. First, we will describe a non-local mass transport dynamics that leads to a modified PDE with the same minimizer, that can be implemented as a stochastic neuronal birth-death process, and such that it provably accelerates the rate of convergence in the mean-field limit. Next, such dynamics fit naturally within the framework of total-variation regularization, which following [Bach'17] have fundamental advantages in the high-dimensional regime. We will discuss a unified framework that controls both optimization, approximation and generalisation errors using large deviation principles, and discuss current open problems in this research direction.
Joint work with G. Rotskoff (NYU), Z. Chen (NYU), S. Jelassi (NYU) and E. Vanden-Eijnden (NYU).
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Joan Bruna is an Assistant Professor at Courant Institute, New York University (NYU), in the Department of Computer Science, Department of Mathematics (affiliated) and the Center for Data Science, since Fall 2016. He belongs to the CILVR group and to the Math and Data groups. From 2015 to 2016, he was Assistant Professor of Statistics at UC Berkeley and part of BAIR (Berkeley AI Research). Before that, he worked at FAIR (Facebook AI Research) in New York. Prior to that, he was a postdoctoral researcher at Courant Institute, NYU. He completed his PhD in 2013 at Ecole Polytechnique, France. Before his PhD he was a Research Engineer at a semi-conductor company, developing real-time video processing algorithms. Even before that, he did a MsC at Ecole Normale Superieure de Cachan in Applied Mathematics (MVA) and a BA and MS at UPC (Universitat Politecnica de Catalunya, Barcelona) in both Mathematics and Telecommunication Engineering. For his research contributions, he has been awarded a Sloan Research Fellowship (2018), a NSF CAREER Award (2019) and a best paper award at ICMLA (2018).
Host: Yan Liu
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
