(The talk will be given in English)

Speaker:     Dr. Roi Livni
                   Department of Computer Science, Princeton University

Wednesday, December 20^th, 2017
15:00 - 16:00

Room 011, Kitot Bldg., Faculty of Engineering

Overcoming Intractability in Learning

Abstract

Machine learning has recently been revolutionized by the introduction of Deep Neural Networks. However, from a theoretical viewpoint these methods are still poorly understood. Indeed the key challenge in Machine Learning today is to derive rigorous results for optimization and generalization in deep learning. In this talk I will present several tractable approaches to training neural networks. At the second part I will discuss a new sequential algorithm for decision making that can take into account metric structure in the action space and avoids erratic behavior which often makes MAB algorithm impractical.

I will present our work that provides some of the first positive results and yield new, provably efficient, and practical algorithms for training certain types of neural networks. In a second work I will present a new online algorithm that learns by sequentially sampling random networks and asymptotically converges, in performance, to the optimal network. Our approach improves on previous random features based learning in terms of sample/computational complexity, and expressiveness. In a more recent work we take a different perspective on this problem. I will provide sufficient conditions that guarantee tractable learning, using the notion of refutation complexity. I will then discuss how this new idea can lead to new interesting generalization bounds that can potentially explain generalization in settings that are not always captured by classical theory.

In the setting of reinforcement learning I will present a recently developed new algorithm for decision making in a metrical action space. As an application, we consider a dynamic pricing problem in which a seller is faced with a stream of patient buyers. Existing MAB algorithms often ignore the structure in the action space, and they are led to an erratic behavior that leads to sub-optimal regret in face of a strategic environment. Our algorithm achieves an optimal regret, and improves on previously known regret bound.

Bio
Roi Livni is a research instructor at the computer science department in Princeton University. He is a recipient of the Eric and Wendy Schmidt fellowship for strategic innovation, and a Yad Hanadiv fellow for postdoctoral position.
He completed his PhD at the Hebrew University, during which he was a recipient of a Google Europe fellowship in learning theory. He also served, during his graduate studies, as a long term intern at Microsoft Research. His graduate work won a Best paper award in ICML'13, and a best student paper award in COLT'13.

School of Mechanical Engineering Seminar
Monday, January 8, 2018 at 14:00
Wolfson Building of Mechanical Engineering, Room 206

Suspension-Colloidal Transport in Porous Media: Petroleum and Environmental Applications

Prof. Pavel Bedrikovetsky

Australian School of Petroleum

University of Adelaide

Flow of suspensions and colloids in porous media with particle capture, detachment and consequent permeability alteration occurs in aquifers and subterranean basins during exploitation of artesian wells, disposal of industrial wastes in aquifers and consequent contamination, fresh and hot water storage in aquifers and geothermal reservoirs, ocean water invasion into aquifers, industrial filtering, as well as exploitation of oil and gas production and injection wells.

We discuss the particulate transport in rocks with fines attachment and detachment accounting for particle- and pore size distributions. The basic equations form a stochastic population-balance system. The system can be upscaled in the cases of mono-sized particles and of small-concentrations. 1D linear and axi-symmetric problems for suspension injection or detachment of natural reservoir fines allow for exact solutions. The solutions allow analysing the propagation of concentration waves of injected colloids or lifted suspensions, its effects on well injectivity and productivity. The exact solutions allow also for downscaling, i.e. restauration of the micro-scale behaviour. Another application of the exact solutions is regularisation of inverse problems, allowing interpreting laboratory experiments and tuning the model parameters. We show simple lab and field devices for complete characterisation of suspension-colloidal system and lab-based reservoir-scale predictions.

The talk is completed by exploration of random-walk and Boltzmann’s models, and of two-phase suspension-colloidal transport.

Bio: Pavel is a Professor of Petroleum Engineering at the University of Adelaide. His research covers suspension and multiphase transport in porous media, including exact integration, upscaling, inverse problems, and technologies of enhanced gas and oil recovery. He authors a seminal book on reservoir engineering and 230 papers in academic journals and Society of Petroleum Engineers (SPE). He is 2008-2009 and 2016-2017 SPE Distinguished Lecturer.

School of Mechanical Engineering Seminar
Monday, December 25, 2017 at 14:00
Wolfson Building of Mechanical Engineering, Room 206

Acoustic-gravity waves, theory & applications

Prof. Usuma Kadri

  Univ. UK           

Abstract: Acoustic–gravity waves (AGWs) are compression-type waves generated as a response to a sudden change in the water pressure, e.g. due to nonlinear interaction of surface waves, submarine earthquakes, landslides, falling meteorites and objects impacting the sea surface. AGWs can travel at near the speed of sound in water (ca. 1500 m/s), but can also penetrate through the sea-floor surface amplifying their speed, which turns them into excellent precursors. “Acoustic–gravity waves”

is an emerging field that is rapidly gaining popularity among the scientific community, as it finds broad utility in physical oceanography, marine biology, geophysics, water engineering, and quantum analogues. This talk is an overview on AGWs, with emphasis on recent developments, current challenges, and future directions.

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