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Established in 2020, POSTECH Mathematical Institute for Data Science (MINDS) is the community of researchers in the areas of fundamental data science, machine learning, artificial intelligence, scientific computing, and humanitarian data science. MINDS mission is to provide a platform for collaboration among researchers and to provide various opportunities for students in data science. MINDS also aims to use our data science research to serve our local and global communities pursuing humanitarian data science.

News

🌟 DACON Ranker Special Lecture: Winning Strategies for AI Competitions 🌟 2023.11.30

3nd POSTECH&Peking SIAM Student Chapter Joint Conference 2023.11.01

2023 PSSC Summer Camp 2023.10.04

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고]-상시모집 2023.07.25

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.07.14

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구교수 채용 공고] 2023.06.12

Seminar | Joint seminar for probability and mathematical biology 2023.05.02

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.02.15

Upcoming Events

Schedule

MINDS SEMINAR

MINDS Seminar Series | Jakwang Kim (University of Wisconsin-Madison) - Adversarial robustness vie optimal transport theory

MINDS SEMINAR

period : 2023-05-30 ~ 2023-05-30

time : 10:00:00 ~ 11:00:00

개최 장소 : Online streaming (Zoom)

Topic : Adversarial robustness vie optimal transport theory

개요

Date	2023-05-30 ~ 2023-05-30	Time	10:00:00 ~ 11:00:00
Speaker	Jakwang Kim	Affiliation	University of Wisconsin-Madison
Place	Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Adversarial robustness vie optimal transport theory
Contents	We study a family of adversarial multiclass classification problems and provide equivalent reformulations in terms of: 1) a family of generalized barycenter problems introduced in the paper and 2) a family of multimarginal optimal transport problems where the number of marginals is equal to the number of classes in the original classification problem. These new theoretical results reveal a rich geometric structure of adversarial learning problems in multiclass classification and extend recent results restricted to the binary classification setting. A direct computational implication of our results is that by solving either the barycenter problem and its dual, or the MOT problem and its dual, we can recover the optimal robust classification rule and the optimal adversarial strategy for the original adversarial problem. Furthermore, we prove the existence of Borel measurable robust classifiers while previous works only argue universally measurable robust classifiers only in the binary setting. Also, based on our theoretical results, we propose two new numerics relying on the truncation of the order of interactions which are faster than the state-of-art MOT algorithm in real data sets by providing empirical results using MNIST and CIFAR-10 data.