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Events for November 16, 2023
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PhD Thesis Defense - Iordanis Fostiropoulos
Thu, Nov 16, 2023 @ 01:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Iordanis Fostiropoulos
Committee Members: Laurent Itti, Mohammad Soleymani, Stefanos Nikolaidis, Nicolas Schweighofer
Title: Towards Efficient Task Generalization
Abstract: Current practices in Machine Learning (ML) require a model to be trained iteratively on novel examples and tasks. The same model generalizes poorly on previously learned data, where we empirically observe 'Catastrophic Forgetting'. Generalizing across tasks can be trivially solved when there is no restriction on the computational resources. We find that current state-of-the-art fails catastrophically to perform robustly when presented with a large sequence of tasks with large domain gaps. Additionally, simpler methods have improved generalization compared to state-of-the-art methods. While current methods suffer in computational performance. In this talk, we present our work that introduces a framework for efficiently learning a large sequence of tasks by utilizing several experts under strict computational constraints. Last, we discuss future improvements of our method and industrial applications, for example, to self-driving carsLocation: Hughes Aircraft Electrical Engineering Center (EEB) - 110
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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CAIS Webinar: Sidestepping the Black-Box: A New Paradigm for Explainable AI
Thu, Nov 16, 2023 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Amulya Yadav, PNC Technologies Career Development Assistant Professor (Penn State University)
Talk Title: Sidestepping the Black-Box: A New Paradigm for Explainable AI
Abstract: Existing work in Explainable Artificial Intelligence (XAI) has been focused on developing techniques to interpret decisions made by pre trained and black box machine learning (ML) models. This black box assumption is reasonable in a lot of settings, e.g., explaining Amazons recommender systems requires assuming a black box model because it is infeasible to assume glass box access to Amazons proprietary models, etc. However, I argue that in many real world settings (especially those that pertain to low resource domains), the black box assumption is unnecessary, undesirable, and often, overly limiting. In this talk, I motivate the need to move away from the black box assumption of XAI by discussing two deployed use cases of responsible AI research i. automated tele triage for poor pregnant women in Kenya, and ii. raising awareness of HIV among homeless youth in Los Angeles. Through my experiences with the deployment of AI in these domains, we will argue the need for a new paradigm in explainable AI. Next, I will discuss two new frameworks i. CounterNet, a novel end to end learning framework which integrates Machine Learning (ML) model training and the generation of corresponding counterfactual (CF) explanations into a single end to end pipeline and ii. RoCourseNet, a training framework that jointly optimizes predictions and recourses that are robust to future data shifts.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Amulya Yadav is the PNC Technologies Career Development Assistant Professor in the College of Information Sciences and Technology at Penn State University, where he serves as Director of the RAISE Research Lab. He is also the Associate Director (Programs) at the Center for Socially Responsible AI at Penn State. Amulyas research work in the field of Responsible AI and Artificial Intelligence for Social Good focuses on developing theoretically grounded approaches to real world problems that can have an impact in the field. His algorithms have been deployed in the real world, particularly in the field of public health and wildlife protection. Amulya is a recipient of the AAMAS 2016 Best Student Paper Award, the AAAI 2017 Best Video and Best Student Video Award, the IDEAS 2016 Most Visionary Paper Award, and the AAMAS 2017 Best Paper Award nomination. His work has also been highlighted by Mashable.com as one of 26 incredible innovations that improved the world in 2015.
Amulya holds a Ph.D. in Computer Science from the University of Southern California, and a B. Tech. in Computer Science and Engineering from Indian Institute of Technology (IIT), Patna.
Register for the Zoom webinar here: https://usc.zoom.us/webinar/register/WN_nPykyeAAQH-B3R6p5-kezg
Host: CAIS
More Info: https://usc.zoom.us/webinar/register/WN_nPykyeAAQH-B3R6p5-kezg
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/webinar/register/WN_nPykyeAAQH-B3R6p5-kezg
