Group Meetings (KR)
From Multiagent Communications and Networking Lab
- Demystifying Black-box Models with Symbolic Metamodels
- 일시: 22.07.26
- https://papers.nips.cc/paper/2019/hash/567b8f5f423af15818a068235807edc0-Abstract.html
- 요약
- Complex black-box model을 interpertable하게 만들 수 있는 metamodel 형성
- Metamodel 형성 시, Meijer G function 활용
- Meatmodel optimization에는 gradient descent 방식 사용
- Offline RL Policies Should be Trained to be Adaptive
- 일시: 22.08.01
- 저자발표: https://icml.cc/virtual/2022/oral/18004
- 요약
- Bayesian approach를 통해 offline RL을 adaptive하게 train하는 방식 제안
- Offline data에서 추론한 Q-function들과 belief를 통해 unseen data에 대해서 적절한 모델 적용 가능
- Patten Recognition and Machine Learning: Kernel Method
- 일시: 22.08.22
- https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
- 요약
- What is kernel? - 왜 feature space mapping의 내적으로 정의 되었는가?
- What is Hilbert space?
- What is Reproducing Kernel Hilbert Space(RKHS)? - ML에서 자주 활용되는 이유
- Dual Representation of SVM(Support Vector Machine)?
- 일시: 22.08.29
- https://en.wikipedia.org/wiki/Support-vector_machine
- 요약
- Optimization with Lagrangian multiplier
- Constrained quadratic programming
- Dual representaion of SVM with KKT condition