Hi!
My name is Charlie, and I am a third-year Ph.D. student in the Laboratory for Information and Decision Systems, Department of Electrical Engineering and Computer Science, at Massachusetts Institute of Technology. I am also a member of Machine Learning Group at MIT. I study optimization and machine learning, under joint supervision of Prof. Suvrit Sra and Prof. Ali Jadbabaie. Specifically, I am focusing on the theoretical aspects of deep learning from an optimization point of view. Before joining MIT, I was a master’s student in Electrical Engineering at Stanford University, where I had a great fortune to work with Prof. John Duchi. I finished my undergraduate program in Electrical Engineering at KAIST, South Korea.
Contact
News
[02/2019] Two talks given in Feb: one at The 24th LIDS Student conference and the other at an invited seminar held by KAIST ISysE.
[12/2018] Two theoretical papers were accepted at The Seventh International Conference on Learning Representations (ICLR 2019)!
[09/2018] I’ll be giving a presentation based on “Small nonlinearities in activation functions create bad local minima in neural networks” at The 2018 INFORMS Annual Meeting.
[05/2018] A new paper “Minimax Bounds on Stochastic Batched Convex Optimization” was accepted for presentation at The 31st Annual Conference on Learning Theory (COLT 2018). The project during my master’s is finally to be published!
[01/2018] Our work “Global optimality conditions for deep neural networks” was accepted as a poster presentation at The Sixth International Conference on Learning Representations (ICLR 2018). Very excited!
[01/2018] I will be giving a presentation based on our work “Global optimality conditions for deep neural networks” at The 23rd Annual LIDS Student Conference on Feb 1, 2018.
[12/2017] I will be presenting a short paper “Global optimality conditions for deep neural networks” at Deep Learning: Bridging Theory and Practice Workshop at NIPS 2017.
Publications
Preprints
Finite sample expressive power of small-width ReLU networks [arXiv]
Chulhee Yun, Suvrit Sra, and Ali Jadbabaie (Oct 2018)
Conference/Workshop Papers
Efficiently testing local optimality and escaping saddles for ReLU networks [arXiv] [paper]
Chulhee Yun, Suvrit Sra, and Ali Jadbabaie
International Conference on Learning Representations (ICLR) 2019
Small nonlinearities in activation functions create bad local minima in neural networks [arXiv] [paper]
Chulhee Yun, Suvrit Sra, and Ali Jadbabaie
International Conference on Learning Representations (ICLR) 2019
Minimax Bounds on Stochastic Batched Convex Optimization [paper]
John Duchi*, Feng Ruan*, and Chulhee Yun*
Conference on Learning Theory (COLT) 2018
Global optimality conditions for deep neural networks [arXiv] [paper]
Chulhee Yun, Suvrit Sra, and Ali Jadbabaie
International Conference on Learning Representations (ICLR) 2018
Deep Learning: Bridging Theory and Practice Workshop at NIPS 2017 [short version]
Face detection using local hybrid patterns [paper]
Chulhee Yun, Donghoon Lee, and Chang D. Yoo
International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015
* indicates alphabetical order.
Talks
- On the Global and Local Optimality of Deep Learning. KAIST ISysE Seminar. Feb 2019.
- Small nonlinearities in activation functions create bad local minima in neural networks. The 24th LIDS Student Conference. Feb 2019.
- Spurious Local Minima in Neural Networks: A Critical View. The 2018 INFORMS Annual Meeting. Nov 2018.
- Global optimality conditions for deep linear neural networks. The 23rd LIDS Student Conference. Feb 2018. [slides]
Awards
Gratefully, my study has been supported by numerous scholarships and awards. To name a few:
- Conference Travel Awards: COLT 2018, ICLR 2018
- Doctoral Study Abroad Program by Korea Foundation for Advanced Studies. Sep 2016–Current.
- Samsung Scholarship. Sep 2014–June 2016.
Services
- Co-Chair: The 24th Annual LIDS Student Conference 2019
- Reviewer: ICML 2019, AISTATS 2019, ICLR 2019, NIPS 2018, CDC 2018
Last update: 02/28/2019