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Events for August 31, 2022
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New & Continuing MS Student Group Advising Session (CSCI/DSCI)Wed, Aug 31, 2022 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Workshops & Infosessions
If you are a New or Continuing MS student in the Computer Science Department or Data Science Program and have any questions or need assistance, please join us for today's optional group advising session via zoom. Access instructions will be sent to students directly. Note: D-clearance is not granted during advisement sessions. All requests for d-clearance must go through the myViterbi portal.
Location: Zoom
Audiences: Graduate
Contact: USC Computer Science
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CS Undergraduate Live Chat Drop-in Advisement
Wed, Aug 31, 2022 @ 01:30 PM - 02:30 PM
Thomas Lord Department of Computer Science
Workshops & Infosessions
CS Advisors will be available on Tuesdays/Wednesdays/Thursdays this fall from 1:30pm to 2:30pm to assist undergraduates in our four majors (CSCI, CSBA, CSGA, and CECS) via Live Chat. Access the live chat through our website at https://cs.usc.edu/chat
Location: Live Chat on Website
Audiences: Undergrad
Contact: USC Computer Science
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PhD Thesis Proposal - Binh Vu
Wed, Aug 31, 2022 @ 03:00 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Binh Vu
Title: Building Semantic Description of Data Sources
Committee: Craig Knoblock, Sven Koenig, Yolanda Gil, Muhao Chen, Daniel O'Leary
Abstract: A semantic description of a data source precisely describes source attributes' types and the relationships between them. Building semantic descriptions is a prerequisite to automatically publish data to knowledge graphs (KGs). Previous work on this task can be placed into two groups: learning-based and value-linked methods. The learning-based methods require manually labeled semantic descriptions to train their systems. The value-linked methods use the linked entities in a data source to discover candidate semantic descriptions by matching the values in the source with values of entities' properties; hence they are unsupervised. However, the value-linked methods need linked entities and do not work well when the source's data is not in KGs. In this thesis proposal, we propose a method to address the limitations of the value-linked methods. We hypothesize that by exploiting knowledge from web tables and KGs, we can learn semantic descriptions of data sources even when there is little overlap between the sources' data and KGs.WebCast Link: https://usc.zoom.us/j/99238519131?pwd=R2ZUYlZoYVNiNXdxTXVFU1JFZXROdz09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Thesis Proposal - Chung-Wei Lee
Wed, Aug 31, 2022 @ 03:00 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Chung-Wei Lee
Title: Online Learning and Its Applications to Games and Partially Observable Systems
Committee: Haipeng Luo (host), David Kempe, Ashutosh Nayyar, Vatsal Sharan, Jiapeng Zhang
Abstract: Online Learning is a general framework for studying sequential decision-making. I will start with its applications in solving games. In particular, Online Learning has been shown as an essential theoretical foundation when building superhuman AI in poker games. We first focus on last-iterate convergence, a favorable property for online learning algorithms in two-player zero-sum games. In normal-form games, we show optimistic multiplicative weight updates (OMWU) and optimistic gradient descent ascent (OGDA) enjoy last-iterate convergence. We then generalize the results to extensive-form games (EFGs), which model sequential actions and incomplete information that appear in card games. We show that a family of regret minimization algorithms have last-iterate convergence, with some of them based on OMWU and OGDA can even converge exponentially fast. We then consider multiplayer games, where our goal becomes minimizing the individual regret of every player. We design two algorithms achieving logarithmic regret in EFGs based on ideas including a reduction from normal-form games and usage of a self-concordant regularizer on a lifted space.
In addition to solving EFGs as an application of Online Learning to partially observable systems, we discuss other examples, including dynamic pricing and recommender systems. Specifically, we formulate the problems as bandits with graph feedback and preference elicitation and discuss our contributions therein. Finally, I will talk about future work in all directions.
WebCast Link: https://usc.zoom.us/j/96191886806?pwd=UExwOXRyaG9ETUhmaW5udEF3TjYzQT09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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New & Continuing MS Student Group Advising Session (CSCI/DSCI)
Wed, Aug 31, 2022 @ 03:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
Workshops & Infosessions
If you are a New or Continuing MS student in the Computer Science Department or Data Science Program and have any questions or need assistance, please join us for today's optional group advising session via zoom. Access instructions will be sent to students directly. Note: D-clearance is not granted during advisement sessions. All requests for d-clearance must go through the myViterbi portal.
Location: Zoom
Audiences: Graduate
Contact: USC Computer Science
