[theory students] Theory Seminar @4pm today: Arturs Backurs, TTI Chicago

[Shuchi Chawla <shuchi@xxxxxxxxxxx>]

A reminder that this seminar is happening in 2 hrs in CS 4310... see you all there.

Shuchi

---------- Forwarded message ---------
From: Christos Tzamos via Faculty <faculty@xxxxxxxxxxx>
Date: Mon, Dec 3, 2018 at 10:48 AM
Subject: Theory Seminar this Friday: Arturs Backurs, TTI Chicago
To: theory-seminar@xxxxxxxxxxx <theory-seminar@xxxxxxxxxxx>, Faculty <faculty@xxxxxxxxxxx>

Hi all,

Arturs Backurs is visiting from TTIC and he will talk about "Efficient Density Evaluation for Smooth Kernels" this Friday at 4 pm in CS 4310.Â

Brief Bio: Arturs Backurs is aÂResearch Assistant Professor atÂthe Toyota Technological Institute at Chicago (TTIC). He received his Ph.D. degree from MIT in 2018 and his bachelorâs degree from University of Latvia in 2012.ÂHis research area is theoretical computer science with the main focus on proving conditional lower bounds for problems that are solvable in polynomial time as well as designing faster algorithms inspired by the hardness results.

Further details about the talk below. See you there!

Christos

Theory Seminar: Efficient Density Evaluation for Smooth Kernels

Friday, December 7, 2018 -

4:00pm to 5:00pm

CS 4310

Arturs Backurs

TTI, Chicago

Given a kernel function k and a dataset P (of points from R^d), the kernel density function of P at a point q from R^d is equal to KDF_P(q) := 1/|P| sum_{p in P} k(p,q). Kernel density evaluation has numerous applications, in scientific computing, statistics, computer vision, machine learning and other fields. In all of them it is necessary to evaluate KDF_P(q) quickly, often for many inputs q and large point-sets P.

In this paper we present a collection of algorithms for efficient KDF evaluation under the assumptions that the kernel k is "smooth", i.e., the value changes at most polynomially with the distance...ÂRead More