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Events for March 23, 2023
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PhD Thesis Proposal - Gautam Salhotra
Thu, Mar 23, 2023 @ 09:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
University Calendar
Title: Accelerating Robot Reinforcement Learning Using Demonstrations
Committee: Gaurav Sukhatme (Chair), SK Gupta, Laurent Itti, Stefanos Nikolaidis, Somil Bansal
Date: Thursday March 23, 9am PST
Abstract:
Reinforcement learning is a promising and, recently, popular tool to solve robotic tasks such as object manipulation and locomotion. However, it is also well known for being a very hard problem setting to explore in. In contrast, Learning from demonstrations (LfD) methods train agents to the desired solution using demonstrations from a teacher.
I will explore the role of LfD methods to guide the exploration of RL methods, with the aim of applying it to regular object manipulation tasks. I will talk about work that uses planners and trajectory optimizers to guide RL, and then discuss the role human experts can play in LfD for RL. Finally, I will talk about proposed projects that can extend the current work to get the benefits of demonstrations while avoiding the downsides of obtaining them.
Location: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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CS Colloquium: Benjamin Eysenbach (CMU) - Self-Supervised Reinforcement Learning
Thu, Mar 23, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Benjamin Eysenbach , CMU
Talk Title: Self-Supervised Reinforcement Learning
Series: CS Colloquium
Abstract: Reinforcement learning (RL) promises to harness the power of machine learning to solve sequential decision making problems, with the potential to enable applications ranging from robotics to chemistry. However, what makes the RL paradigm broadly applicable is also what makes it challenging: only limited feedback is provided for learning to select good actions. In this talk, I will discuss how we have made headway of this challenge by designing self-supervised RL methods, ones that can learn representations and skills for acting using unsupervised (reward-free) experience. These skill learning methods are practically-appealing and have since sparked a vibrant area of research. I will also share how we have answered some open theoretical questions in this area.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Benjamin Eysenbach is a final-year PhD student at Carnegie Mellon University. His research has developed machine learning algorithms for sequential decision making. His algorithms not only achieve a high degree of performance, but also carry theoretical guarantees, are typically simpler than prior methods, and draw connections between many areas of ML and CS. Ben is the recipient of the NSF and Hertz graduate fellowships. Prior to the PhD, he was a resident at Google Research and studied math as an undergraduate at MIT.
Host: Jyo Deshmukh
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Dr. Zhou Li (University of California Irvine) - Debugging the Fragmented DNS Infrastructure at Scale
Thu, Mar 23, 2023 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Zhou Li, University of California Irvine
Talk Title: Debugging the Fragmented DNS Infrastructure at Scale
Abstract: Domain Name System (DNS) is a fundamental infrastructure that supports almost all sorts of Internet activities. However, service failures and breach of DNS are not rare, and some even led to the shutdown of large data centers, though DNS was designed under the goals like resiliency from the very beginning. We argue that the root causes are that DNS infrastructure has become too fragmented and its protocols have become much more complex, so new research efforts are needed to harden the DNS infrastructure. In this talk, I'll describe our efforts in this direction. First, I'll talk about two new DNS attacks we identified under the settings of domain revocation and conditional resolution, and their implications. Second, I'll talk about how we measure the operational status of DNS-over-Encryption at a large scale. Finally, I'll conclude the talk with an outlook for DNS-related research.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Zhou Li is an Assistant Professor at UC Irvine, EECS department, leading the Data-driven Security and Privacy Lab. Before joining UC Irvine, he worked as Principal Research Scientist at RSA Labs from 2014 to 2018. His research interests include Domain Name System (DNS), Graph Security analytics, Privacy Enhancement Technologies and Side-channel analysis. He received the NSF CAREER award, Amazon Research Award, Microsoft Security AI award and IRTF Applied Networking Research Prize.
Host: Weihang Wang
More Info: https://usc.zoom.us/j/92035174335?pwd=VzhKZ0xjM3A2SzFwOWsyRG1SQWpqUT09
Location: Seeley G. Mudd Building (SGM) - 124
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/j/92035174335?pwd=VzhKZ0xjM3A2SzFwOWsyRG1SQWpqUT09
