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Events for April 12, 2018
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CS Colloquium: Mikael Henaff (New York University) - Learning Models of the Environment for Sample-Efficient Planning
Thu, Apr 12, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Mikael Henaff, New York University
Talk Title: Learning Models of the Environment for Sample-Efficient Planning
Series: CS Colloquium
Abstract: Learning to predict how an environment will evolve and the consequences of one's actions is an important ability for autonomous agents, and can enable planning with relatively few interactions with the environment which may be slow or costly. However, learning an accurate predictive model is made difficult due to several challenges, such as partial observability, long-term dependencies and inherent uncertainty in the environment. In this talk, I will present my work on architectures designed to address some of these challenges, as well as work focused on better understanding recurrent network memory over long timescales. I will then present some recent work applying learned environment models for planning, using a simple gradient-based approach which can be used in both discrete and continuous action spaces. This approach is able to match or outperform model-free methods while requiring fewer environment interactions and still enabling real-time performance.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.
Biography: Mikael Henaff is a fifth-year PhD student in computer science at New York University, advised by Yann LeCun. His current research interests are centered around learning predictive models of the environment, model-based reinforcement learning and memory-augmented neural networks. Prior to his Ph.D studies, he worked at the NYU Langone Medical Center and has interned several times at Facebook AI Research. He holds a B.S in mathematics from the University of Texas at Austin and an M.S in mathematics from New York University.
Host: Fei Sha
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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PhD Defense - Amulya Yavdav
Thu, Apr 12, 2018 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Amulya Yadav
Committee: Milind Tambe (Chair), Kristina Lerman, Aram Galstyan, Eric Rice, Dana Goldman
Title: Artificial Intelligence for Low Resource Communities: Influence Maximization in an Uncertain World
Time: April 12 (Thursday) 1:00-3:00 PM
Location: KAP 209
Abstract:
The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict society is enormous, particularly in the areas of healthcare, conservation and public safety and security. Many problems in these domains involve harnessing social networks of under-served communities to enable positive change, e.g., using social networks of homeless youth to raise awareness about Human Immunodeficiency Virus (HIV) and other STDs. Unfortunately, most of these real-world problems are characterized by uncertainties about social network structure and influence models, and previous research in AI fails to sufficiently address these uncertainties, as they make several unrealistic simplifying assumptions for these domains.
This thesis addresses these shortcomings by advancing the state-of-the-art to a new generation of algorithms for interventions in social networks. In particular, this thesis describes the design and development of new influence maximization algorithms which can handle various uncertainties that commonly exist in real-world social networks (e.g., uncertainty in social network structure, evolving network state, and availability of nodes to get influenced). These algorithms utilize techniques from sequential planning problems and social network theory to develop new kinds of AI algorithms. Further, this thesis also demonstrates the real-world impact of these algorithms by describing their deployment in three pilot studies to spread awareness about HIV among actual homeless youth in Los Angeles. This represents one of the first-ever deployments of computer science based influence maximization algorithms in this domain. Our results show that our AI algorithms improved upon the state-of-the-art by 160% in the real-world. We discuss research and implementation challenges faced in deploying these algorithms, and lessons that can be gleaned for future deployment of such algorithms. The positive results from these deployments illustrate the enormous potential of AI in addressing societally relevant problems.
Location: Kaprielian Hall (KAP) - 209
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
