January 2024 - Ai - Engineering Mailing Lists

AI seminar
by Tadepalli, Prasad 30 Jan '24

30 Jan '24

Hi everyone, As some of you know, we had an unfortunate attack on zoom during the AI seminar on Friday. Some person had transmitted racist and inappropriate video and audio during the talk. Thanks to the presence of mind of the speaker, it did not come through in the seminar room, but people on zoom were affected. It took me a few minutes too long to realize what happened and remove the offender. I am very sorry for the incident and apologize for not being better prepared. Please note that I have deactivated the old seminar link. I am trying to keep it open for outsiders in a secure way by making it a Webinar. Please stay tuned. Thank you for your understanding. Prasad

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AI Seminar : Jan 26
by Mangannavar, Rajesh Devaraddi 24 Jan '24

24 Jan '24

Dear all, Our next AI seminar is scheduled to be on January 26th, 2-3 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Value-Based Abstractions for Planning Amy Zhang Assistant Professor Chandra Family Department of Electrical and Computer Engineering University of Texas at Austin Abstract: As the field of robotics continues to advance, the integration of efficient planning algorithms with powerful representation learning becomes crucial for enabling robots to perform complex manipulation tasks. We address key challenges in planning, reward learning, and representation learning through the objective of learning value-based abstractions. We explore this idea via goal-conditioned reinforcement learning, action-free pre-training, and with language. By leveraging self-supervised reinforcement learning and efficient planning algorithms, these approaches collectively contribute to the advancement of robotic systems capable of learning and adapting to diverse tasks in real-world environments. Speaker Bio: I am an assistant professor at UT Austin in the Chandra Family Department of Electrical and Computer Engineering. My work focuses on improving generalization in reinforcement learning through bridging theory and practice in learning and utilizing structure in real world problems. Previously I was a research scientist at Meta AI - FAIR and a postdoctoral fellow at UC Berkeley. I obtained my PhD from McGill University and the Mila Institute, and also previously obtained an M.Eng. in EECS and dual B.Sci. degrees in Mathematics and EECS from MIT. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged

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AI Semin : Jan 19
by Mangannavar, Rajesh Devaraddi 18 Jan '24

18 Jan '24

Dear all, Our next AI seminar is scheduled to be on January 19th, 2-3 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> AI for Software Engineering (AI4SE): Opportunities and Challenges Manish Motwani Assistant Professor School of Electrical Engineering and Computer Science Oregon State University Abstract: Software is ubiquitous but often faulty. Software engineers spend 35–50% of their time debugging software, accounting for 50–75% of the total software development budget. With the transformative power of AI, software engineering researchers have begun integrating AI into various phases of the software development lifecycle to address these issues and enhance software quality. In this talk, I will provide a high-level overview of opportunities and challenges for applying AI in software engineering, specifically focusing on software testing and debugging. I will then present my research on using natural language artifacts to automatically improve software quality, including my technique for automatically generating tests with oracles from software specifications and my method for using developer-written tests in conjunction with bug reports to automatically localize and repair bugs. Speaker Bio: Manish Motwani is an Assistant Professor in the School of Electrical Engineering and Computer Science at Oregon State University. Before joining OSU, he worked as a postdoc at Georgia Tech and has four years of industry work experience as a researcher at the Tata Research Development and Design Center, an industrial research lab in India. His interests lie in improving software engineers’ productivity and aiding software engineers in developing high-quality software by automating software engineering practices. His research involves learning phenomena from large software repositories and using that knowledge to design novel automation techniques, such as requirements elicitation, test synthesis, and program repair. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged

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AI Seminar : Jan 12
by Mangannavar, Rajesh Devaraddi 10 Jan '24

10 Jan '24

Dear all, Our next AI seminar is scheduled to be on January 12th, 2-3 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Reinforcement Learning from a Bayesian perspective Brendan O'Donoghue Research Scientist DeepMind Abstract: Reinforcement learning (RL) involves an agent interacting with an environment over time attempting to maximize its total return. Initially the agent does not know about the environment and must learn about it from experience. As the agent navigates the environment it receives noisy observations which it can use to update its (posterior) beliefs about the environment. Therefore, the RL problem is a statistical inference problem wrapped in a control problem, and the two problems must be tackled simultaneously for good data efficiency. This is because the policy of the agent affects the data it will collect, which in turn affects the policy, and so on. This is in contrast to supervised learning, where the performance of a classifier (for instance) does not influence the data it will later observe. Failure to properly consider the statistical aspect of the RL problem will result in agents that require exponential amounts of experience for good performance. On the other hand, correctly considering the statistical inference problem and the control problem together has the potential to dramatically reduce the compute requirements to solve problems and potentially unlock new domains and capabilities far outside of the range of current agents. In this talk I will introduce these concepts and discuss how Bayesian techniques can provide principled solutions to the problem. Speaker Bio: Brendan O'Donoghue earned his PhD in 2013 from Stanford working with Stephen Boyd on optimization and control theory. Since then he has worked at DeepMind as a research scientist working on deep reinforcement learning, optimization, and (more recently) large language models. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged

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AI Seminar: Brendan O'Donoghue
by Tadepalli, Prasad 10 Jan '24

10 Jan '24

Hi all, The first AI Seminar this term is by Brendan O'Donoghue of Deep Mind on Friday, 1/12 2-3 pm (new time) in KEC 1001, which will be followed by a 30 minutes time for Q&A. Zoom link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ… Title: Reinforcement Learning from a Bayesian Perspective Abstract Reinforcement learning (RL) involves an agent interacting with an environment over time attempting to maximize its total return. Initially the agent does not know about the environment and must learn about it from experience. As the agent navigates the environment it receives noisy observations which it can use to update its (posterior) beliefs about the environment. Therefore, the RL problem is a statistical inference problem wrapped in a control problem, and the two problems must be tackled simultaneously for good data efficiency. This is because the policy of the agent affects the data it will collect, which in turn affects the policy, and so on. This is in contrast to supervised learning, where the performance of a classifier (for instance) does not influence the data it will later observe. Failure to properly consider the statistical aspect of the RL problem will result in agents that require exponential amounts of experience for good performance. On the other hand, correctly considering the statistical inference problem and the control problem together has the potential to dramatically reduce the compute requirements to solve problems and potentially unlock new domains and capabilities far outside of the range of current agents. In this talk I will introduce these concepts and discuss how Bayesian techniques can provide principled solutions to the problem. Speaker Biography Brendan O'Donoghue earned his PhD in 2013 from Stanford working with Stephen Boyd on optimization and control theory. Since then he has worked at DeepMind as a research scientist working on deep reinforcement learning, optimization, and (more recently) large language models. Please check the seminar page https://engineering.oregonstate.edu/EECS/research/AI-seminars<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> for future talks. Prasad Tadepalli

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information theory class
by Nguyen, Thinh 05 Jan '24

05 Jan '24

Hello everyone, I just want to bring to your attention that I will be teaching information theory class (ECE 566) next term. If you are interested in learning the theories and some foundational connections among various disciplines (compression, compression, machine learning, statistics, ...) then you might want to consider taking ECE 566. I am attaching the ECE 566 syllabus. If enough number of student register for ECE 566, I will offer the second course (special topics) in information theory series in the Spring term. Best, Dr. Nguyen

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