소식

NEWS & EVENTS

세미나

소식

NEWS & EVENTS

세미나

세미나

(11월 23일) 관계형 자동 통계학자를 통한 다중 시계열 데이터 분석

제목

관계형 자동 통계학자를 통한 다중 시계열 데이터 분석

날짜

2016년 11월 23일 (수요일) 오후 4:00-5:00

연사

최재식 교수 (UNIST)

장소

N1 111호

개요:

Recent advances in artificial intelligence have changed our daily lives dramatically. Recently, a management consulting firm, McKinsey, expected that automation of knowledge work could impact $5-7 trillion worth of labor market across a wide range of industry sectors in 2025. In this talk, I will present a state-of-the-art machine learning framework to analysis time series data automatically. Gaussian Processes (GPs) provide a general and analytically tractable way of modeling complex time-varying, nonparametric functions. The Automatic Bayesian Covariance Discovery (ABCD) system constructs natural-language description of time-series data by treating unknown time-series data nonparametrically using GP with a composite covariance kernel function. Unfortunately, learning a composite covariance kernel with a single time-series data set often results in less informative kernel that may not give qualitative, distinctive descriptions of data. We address this challenge by proposing two relational kernel learning methods which can model multiple time-series data sets by finding common, shared causes of changes. We show that the relational kernel learning methods find more accurate models for regression problems on several real-world data sets; US stock data, US house price index data and currency exchange rate data.

연사악력:

Assistant Professor, School of Electrical and Computer Engineering, UNIST, 2013-present
Affiliate Research, in Computational Research Division, Lawrence Berkeley National Lab, 2013-present
Postdoc Fellow, in Computational Research Division, Lawrence Berkeley National Lab, 2013
Ph.D., in Dept. of Computer Science, University of Illinois at Urbana-Champaign, 2012
B.S., in Dept. of Computer Engineering, Seoul National University, 2004