https://www.math.snu.ac.kr/board/files/attach/images/701/ff97c54e6e21a4ae39315f9a12b27314.png
Extra Form
강연자 이창한
소속 Northwestern University
date 2021-09-16

 

Abstract: 
While the typical behaviors of stochastic systems are often deceptively oblivious to the tail distributions of the underlying uncertainties, the ways rare events arise are vastly different depending on whether the underlying tail distributions are light-tailed or heavy-tailed. Roughly speaking, in light-tailed settings, a system-wide rare event arises because everything goes wrong a little bit as if the entire system has conspired up to provoke the rare event (conspiracy principle), whereas, in heavy-tailed settings, a system-wide rare event arises because a small number of components fail catastrophically (catastrophe principle). In the first part of this talk, I will introduce the recent developments in the theory of large deviations for heavy-tailed stochastic processes at the sample path level and rigorously characterize the catastrophe principle. In the second part, I will explore an intriguing connection between the catastrophe principle and a central mystery of modern AI—the unreasonably good generalization performance of deep neural networks.
 
This talk is based on the ongoing research in collaboration with Mihail Bazhba, Jose Blanchet, Bohan Chen, Sewoong Oh, Insuk Seo, Zhe Su, Xingyu Wang, and Bert Zwart.
 
Short Bio: 
Chang-Han Rhee is an Assistant Professor in Industrial Engineering and Management Sciences at Northwestern University. Before joining Northwestern University, he was a postdoctoral researcher in the Stochastics Group at Centrum Wiskunde & Informatica and in Industrial & Systems Engineering and Biomedical Engineering at Georgia Tech. He received his Ph.D. in Computational and Mathematical Engineering from Stanford University. His research interests include applied probability, stochastic simulation, and statistical learning. He was a winner of the Outstanding Publication Award from the INFORMS Simulation Society in 2016, a winner of the Best Student Paper Award (MS/OR focused) at the 2012 Winter Simulation Conference, and a finalist of the 2013 INFORMS George Nicholson Student Paper Competition.
Atachment
첨부 '1'
List of Articles
카테고리 제목 소속 강연자
수학강연회 Analysis and computations of stochastic optimal control problems for stochastic PDEs file 아주대 이형천
수학강연회 Mirror symmetry of pairings file 숭실대학교 이상욱
수학강연회 Contact topology of singularities and symplectic fillings file 순천대학교 권명기
BK21 FOUR Rookies Pitch 2021-1 Rookies Pitch: PDE, Regularity Theory (박진완) file 수학연구소 박진완
BK21 FOUR Rookies Pitch 2021-1 Rookies Pitch: Topological Combinatorics (이강주) file 수학연구소 이강주
BK21 FOUR Rookies Pitch 2021-1 Rookies Pitch: Number Theory (김예슬) file 수학연구소 김예슬
BK21 FOUR Rookies Pitch 2022-1 Rookies Pitch: PDE, Emergent Dynamics (안현진) file 수학연구소 안현진
BK21 FOUR Rookies Pitch 2022-1 Rookies Pitch: Symplectic Topology (문지연) file 수학연구소 문지연
BK21 FOUR Rookies Pitch 2022-1 Rookies Pitch: Geometric Group Dynamics (서동균) file 수학연구소 서동균
BK21 FOUR Rookies Pitch 2022-1 Rookies Pitch: Probability, PDE (Ramil Mouad) file 수학연구소 Ramil Mouad
BK21 FOUR Rookies Pitch 2022-1 Rookies Pitch: Functional Analysis (Wang Xumin) file 수학연구소 Wang Xumin
BK21 FOUR Rookies Pitch 2022-2 Rookies Pitch: Harmonic Analysis (함세헌) file 수학연구소 함세헌
BK21 FOUR Rookies Pitch 2023-2 Number Theory (윤종흔) file 수학연구소 윤종흔
BK21 FOUR Rookies Pitch 2023-2 Differential Geometry (서동휘) file 수학연구소 서동휘
BK21 FOUR Rookies Pitch 2021-1 Rookies Pitch: PDE, Dynamical Systems (박한솔) file 수리과학부 박한솔
BK21 FOUR Rookies Pitch 2022-1 Rookies Pitch: Cryptography (이기우) file 수리과학부 이기우
BK21 FOUR Rookies Pitch 2022-2 Rookies Pitch: Representation Theory(이신명) file 수리과학부 이신명
BK21 FOUR Rookies Pitch 2022-2 Rookies Pitch: Probability Theory (이중경) file 수리과학부 이중경
BK21 FOUR Rookies Pitch 2023-2 Optimization Theory (박지선) file 수리과학부 박지선
수학강연회 <학부생을 위한 ɛ 강연> Secure computation: Promise and challenges file 송용수 <학부생을 위한 ɛ 강연> Secure computation: Promise and challenges
Board Pagination Prev 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Next
/ 15