BEGIN:VCALENDAR
METHOD:PUBLISH
VERSION:2.0
PRODID:-//Cornell U. Department of Computer Science//Brown Bag Seminar//EN
BEGIN:VEVENT
SUMMARY:Brown bag: Ziv Goldfeld
DESCRIPTION:Title: Smoothing probability distributions for high
	 dimensional learning and inference\nSpeaker: Ziv Goldfeld\nAbstract: The
	 talk will explore the benefits of smoothing probability distribution (by
	 convolving them with a chosen kernel) for high dimensional learning and
	 inference. Machine learning tasks often involve optimization of
	 evaluation of a certain functional of the underlying data distribution\,
	 e.g.\, loss function\, information measure\, statistical distance\, etc.
	 In practice\, we rarely have access to the actual distribution and only
	 get data from it. This necessitates estimating the distribution or the
	 functional of interest from samples. A central issue is that such
	 estimators suffer from the curse of dimensionality\, i.e.\, their sample
	 complexity grows exponentially fast with dimension. This makes it
	 impossible to obtain meaningful accuracy guarantees\, considering the
	 dimensionality of real-world data. As we shall see\, smoothing
	 alleviates the curse of dimensionally while preserving the capability to
	 perform inference by leveling out local irregularities in the considered
	 distributions. This enables constructing estimators with scalable (in
	 dimension) sample complexity guarantees and opens the door for various
	 applications. The talk will cover two such applications: measuring
	 information flows in deep neural network classifiers and implicit
	 generative modeling via minimum distance estimation. We will discuss the
	 original challenges\, how smoothing helps to deal with them\, remaining
	 gaps\, and ongoing/future research trajectories. 
LOCATION:Zoom
UID:2020-12-01
STATUS:TENTATIVE
DTSTART:20201201T182500Z
DTEND:20201201T194000Z
LAST-MODIFIED:20201130T164344Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T153540Z
END:VEVENT
END:VCALENDAR