Welcome to MINDS!

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 | Eren Mehmet Kıral (Keio University, Japan) - Lie Group updates for Learning Distributions on Machine Learning Parameters

MINDS SEMINAR

period : 2023-11-14 ~ 2023-11-14

time : 17:00:00 ~ 18:00:00

개최 장소 : Math Bldg 404 & Online streaming (Zoom)

Topic : Lie Group updates for Learning Distributions on Machine Learning Parameters

개요

Date	2023-11-14 ~ 2023-11-14	Time	17:00:00 ~ 18:00:00
Speaker	Eren Mehmet Kıral	Affiliation	Keio University, Japan
Place	Math Bldg 404 & Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Lie Group updates for Learning Distributions on Machine Learning Parameters
Contents	I will talk about our recent paper https://arxiv.org/abs/2303.04397 with Thomas Möllenhoff and Emtiyaz Khan, and other related results. Bayesian Learning learns a distribution over the model parameters, allowing for different descriptions of the same data. This is (contrary to classical learning which "bets-it-all" on a single set of parameters in describing a given dataset and making predictions. We focus on classes of distributions which have a transitive Lie group action on them given by pushforwards of an action on the parameter space. I will also specialize to a few concrete Lie groups and show distinct learning behavior.