"딥러닝기반의 최신음성통신기술의 이해 "
"Theoretical Computer Science for Numerics"
Prof. Martin Ziegler(KAIST)
"4차 산업혁명 시대 트렌드와 공유서비스 방향성 "
"SociaLite: A Declarative Query Language for Large-Scale Graph Analysis"
- 일 자
- 제목 : 2016.12.20(Tue) 'Leveraged Gaussian Process Regression: Learning from Do's and Don'ts' - Songhwai Oh 오성회교수 (Seoul National University 서울대학교)
-Titile:Leveraged Gaussian Process Regression: Learning from Do's and Don'ts
Songhwai Oh received the B.S. (with highest honors), M.S., and Ph.D. degrees in electrical engineering and computer sciences from the University of California, Berkeley, in 1995, 2003, and 2006, respectively. He is currently an Associate Professor in the Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea. Before his Ph.D. studies, he was a Senior Software Engineer at Synopsys, Inc. and a Microprocessor Design Engineer at Intel Corporation. In 2007, he was a Postdoctoral Researcher in the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. From 2007 to 2009, he was an Assistant Professor of electrical engineering and computer science in the School of Engineering, University of California, Merced. His research interests include robotics, computer vision, cyber-physical systems, and machine learning.
-AbstractWith recent advances in hardware, sensing, and algorithms, we are witnessing the emergence of a new robotics industry. I will present a few examples of new services provided by upcoming service robots. With the introduction of new service robots in diverse domains, we can expect
that more service robots will be assisting us in the near future in places, such as offices, malls, and homes. But, for a robot to coexist with humans and operate successfully in crowded and dynamic
environments, a robot must be able to learn from experiences to act safely and harmoniously with human participants in the environment. I will discuss research challenges for service robots and our attempts to address those challenges.
In particular, I will present our recent work on learning with counterexamples to enhance safety and social acceptability of service robots. While existing learning from demonstration (LfD) algorithms
assume that demonstrations are given from skillful experts, the proposed method alleviates such assumption by allowing demonstrations from casual or novice users. To learn from demonstrations of mixed quality, we present a sparse-constrained leveraged optimization algorithm using
proximal linearized minimization. I will also present how the same concept can be applied to inverse reinforcement learning to improve performance.