Epstein Institute Seminar - ISE 651
Tue, Nov 27, 2018 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Talk Title: Last Class Session
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Department Only
Contact: Grace Owh
MASCLE Machine Learning Seminar: William Wang (UCSB) – Learning to Generate Language and Actions with Structured Agents
Speaker: William Wang, University of California, Santa Barbara
Talk Title: Learning to Generate Language and Actions with Structured Agents
Series: Machine Learning Seminar Series
Abstract: A major challenge in Natural Language Processing is to teach machines to generate natural language and actions. However, existing deep learning approaches to language generation do not impose structural constraints in the generation process, often producing low-quality results. In this context, we will introduce our attempt of imposing structural constraints for video captioning via hierarchical reinforcement learning. Moreover, we observe that most of the automated metrics for generation could be gamed, and therefore, we propose an adversarial reward learning method to automatically learn the reward via inverse reinforcement learning. Furthermore, I will discuss our recent attempts in connecting language and vision to actions via a language grounding task for robot navigation, and introduce new algorithms on scheduled policy optimization and combining model-free and model-based reinforcement learning. I will conclude by introducing other exciting research projects at UCSB's NLP Group.
Biography: William Wang is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara. He received his PhD from School of Computer Science, Carnegie Mellon University in 2016. He has broad interests in machine learning approaches to data science, including natural language processing, statistical relational learning, information extraction, computational social science, dialogue, and vision. He directs UCSB's NLP Group (nlp.cs.ucsb.edu): in two years, UCSB advanced in the NLP area from an undefined ranking position to top 3 in 2018 according CSRankings.org. He has published more than 60 papers at leading NLP/AI/ML conferences and journals, and received best paper awards (or nominations) at ASRU 2013, CIKM 2013, and EMNLP 2015, a DARPA Young Faculty Award (Class of 2018), two IBM Faculty Awards in 2017 and 2018, a Facebook Research Award in 2018, an Adobe Research Award in 2018, and the Richard King Mellon Presidential Fellowship in 2011. He served as an Area Chair for NAACL, ACL, EMNLP, and AAAI. He is an alumnus of Columbia University, Yahoo! Labs, Microsoft Research Redmond, and University of Southern California. In addition to research, William enjoys writing scientific articles that impact the broader online community: his microblog @王威廉 has 110,000+ followers and more than 2,000,000 views each month. His work and opinions appear at major international tech media outlets such as Wired, VICE, Fast Company, NASDAQ, The Next Web, Law.com, and Mental Floss.
Host: Yan Liu, USC Machine Learning Center
Contact: Computer Science Department
CS Colloquium: Chad Jenkins (University of Michigan) - Semantic Robot Programming... and Making the World a Better Place Too
Speaker: Chad Jenkins, University of Michigan
Talk Title: Semantic Robot Programming...and Making the World a Better Place Too
Series: CS Colloquium
Abstract: The visions of interconnected heterogeneous autonomous robots in widespread use are a coming reality that will reshape our world. Similar to "app stores" for modern computing, people at varying levels of technical background will contribute to "robot app stores" as designers and developers. However, current paradigms to program robots beyond simple cases remains inaccessible to all but the most sophisticated of developers and researchers. In order for people to fluently program autonomous robots, a robot must be able to interpret user instructions that accord with that user's model of the world. The challenge is that many aspects of such a model are difficult or impossible for the robot to sense directly. We posit a critical missing component is the grounding of semantic symbols in a manner that addresses both uncertainty in low-level robot perception and intentionality in high-level reasoning. Such a grounding will enable robots to fluidly work with human collaborators to perform tasks that require extended goal-directed autonomy.
I will present our efforts towards accessible and general methods of robot programming from the demonstrations of human users. Our recent work has focused on Semantic Robot Programming (SRP), a declarative paradigm for robot programming by demonstration that builds on semantic mapping. In contrast to procedural methods for motion imitation in configuration space, SRP is suited to generalize user demonstrations of goal scenes in workspace, such as for manipulation in cluttered environments. SRP extends our efforts to crowdsource robot learning from demonstration at scale through messaging protocols suited to web/cloud robotics. With such scaling or robotics in mind, prospects for cultivating both equal opportunity and technological excellence will be discussed in the context of broadening and strengthening Title IX.
Biography: Odest Chadwicke Jenkins, Ph.D., is an Associate Professor of Computer Science and Engineering at the University of Michigan. Prof. Jenkins earned his B.S. in Computer Science and Mathematics at Alma College (1996), M.S. in Computer Science at Georgia Tech (1998), and Ph.D. in Computer Science at the University of Southern California (2003). He previously served on the faculty of Brown University in Computer Science (2004-15). His research addresses problems in interactive robotics and human-robot interaction, primarily focused on mobile manipulation, robot perception, and robot learning from demonstration. He is a founder of the Robot Web Tools open-source robotics organization. Prof. Jenkins' work has been recognized by a Sloan Research Fellow, a Presidential Early Career Award for Scientists and Engineers (PECASE), and Young Investigator awards from the Office of Naval Research, the Air Force Office of Scientific Research, and the National Science Foundation. Prof. Jenkins is currently serving as the Editor-in-Chief for the ACM Transactions on Human-Robot Interaction