Conferences, Lectures, & Seminars
Events for December
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NL Seminar-What Can We Learn From An Agent that Plays Word-Guessing Games?
Fri, Dec 04, 2015 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Eli Pincus, USC/ICT
Talk Title: What Can We Learn From An Agent that Plays Word-Guessing Games?
Abstract: In this talk I will discuss an agent that can play a simple word-guessing game with a user. The fast-paced, multi-modal, and interactive nature of the dialogue that takes place in word-guessing games are challenging for today's dialogue systems to emulate. The agent serves as a research testbed to explore issues of fast-paced incremental interaction and user satisfaction in such a setting. I will trace how the agent's design was motivated by a human-human corpus as well as discuss two empirical studies involving the agent. The first study was designed to learn an algorithm to automatically select effective clues (clues likely to elicit a correct guess from a human). The second study was an evaluation of several synthetic voices and 1 human voice which showed how participant's subjective perceptions and objective task performances fluctuated based on the voice used and the duration of the participant's exposure to the voice.
Biography: Eli Pincus is a 3rd year USC PhD student and a graduate research assistant in the Natural Dialogue Group at
USC Institute for Creative Technologies. He is advised by Professor David Traum. Eli's main research is in human-computer dialogue. Since joining USC he has been working on improving virtual human dialogue. He won the best computer science department TA award in spring 2015. He was a research intern in the NLP and AI group at Nuance Communications in summer 2015.
Host: Nima Pourdamghani and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 6th Flr Conf Rm # 689, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Iteratively Learning Data Transformation Programs from Examples
Tue, Dec 08, 2015 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Bo Wu, USC/ISI
Talk Title: Iteratively Learning Data Transformation Programs from Examples
Series: AI Seminar
Abstract: Data transformation is an essential preprocessing step in most data analysis applications. It often requires users to write many trivial and task-dependent programs. Recently, programming-by-example (PBE) approaches enable users to generate data transformation programs without coding. To correctly transform these datasets, existing PBE approaches typically require users to provide multiple examples to generate the correct transformation programs. These approaches time complexity grows exponentially with the number of examples and in a high polynomial degree with the length of the examples. Users have to wait a long time to see any response from the systems when they work on moderately complicated datasets. Moreover, existing PBE approaches also lack the support for users to verify the correctness of the transformed results.
To address the challenges, we propose an approach that generates programs iteratively, which exploits the fact that users often provide multiple examples iteratively to refine programs learned from previous iterations. We evaluated IPBE, the implementation of our iterative programming-by-example approach, against several state-of-the-art alternatives on various transformation scenarios. The results show that users of our approach used less time and achieved higher correctnesses compared to other alternative approaches.
Biography: Bo Wu is a newly graduated PhD from University of Southern California. He worked at Information Integration group at Information Science Institute. His research focuses on automatically generating data transformation programs. He received his B.S. in software engineering from Harbin Institute of Technology and his M.S. in computer science from Institute of Computing Technology, Chinese Academy of Sciences.
Host: Craig Knoblock
Location: Information Science Institute (ISI) - 1135 - 11th fl Large CR
Audiences: Everyone Is Invited
Contact: Alma Nava / Information Sciences Institute
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
AI Seminar- Kernel Methods for Unsupervised Domain Adaptation
Fri, Dec 11, 2015 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Boqing Gong , University of Central Florida
Talk Title: Kernel Methods for Unsupervised Domain Adaptation
Series: Artificial Intelligence Seminar
Abstract: In many applications (computer vision, natural language processing, speech recognition, etc.), the curse of domain mismatch arises when the test data (of a target domain) and the training data (of some source domain(s)) come from different distributions. Thus, developing techniques for domain adaptation, i.e., generalizing models from the sources to the target, has been a pressing need. In this talk, I will describe our efforts and results on addressing this challenge.
A key observation is that domain adaptation entails discovering and leveraging latent structures in the source and the target domains. To this end, we develop kernel methods. Concretely, our kernel-based adaptation methods exploit various latent structures in the data. In this talk, I will give 3 examples: subspaces for aligning domains, landmarks for bridging the gaps between domains, and clusters by distribution similarity for identifying unknown domains. We demonstrate their effectiveness on well-benchmarked datasets and tasks. This work is conducted with my Ph.D. adviser Dr. Fei Sha and our collaborator Dr. Kristen Grauman.
Biography: Boqing Gong is an Assistant Professor in the Department of Computer Science and the Center for Research in Computer Vision at University of Central Florida. His research lies at the intersection of machine learning and computer vision, and has been focusing on domain adaptation, zero-shot/transfer learning, and visual analytics of objects, attributes, and human activities. Boqing received his Ph.D. in Computer Science from the University of Southern California in 2015, where his work was partially supported by the Viterbi School of Engineering Doctoral Fellowship. He holds a Master of Philosophy degree from the Chinese University of Hong Kong and a Bachelor of Engineering degree from the University of Science and Technology of China.
Host: Linhong Zhu
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=26f8433757004b14aaf06c222eda19211dLocation: 11th Flr Conf Rm # 1135, Marina Del Rey
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=26f8433757004b14aaf06c222eda19211d
Audiences: Everyone Is Invited
Contact: Peter Zamar
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
