Dear all,

Our next AI seminar is scheduled to be on May 31st (Friday).

There are 2 seminars on Friday this week: [Please note that they are in different locations]

Talk 1 :

Title: Training Machines to Know What They Don't Know

Time : 10-11 AM

Seminar Location: KEC 1003

Zoom link: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5TEVkdz09

Talk 2 :

Title: Inverse constraint learning and risk averse reinforcement learning for safe AI

Time : 2-3 PM

Seminar Location: BEXL 320

Zoom link: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5TEVkdz09

Speaker Details:

Pascal Poupart

Professor 

David R. Cheriton School of Computer Science 

University of Waterloo

Talk 1 Abstract: Training Machines to Know What They Don't Know

Modern machine learning predictors often suffer from overconfidence.  I will explain why neural predictors with a softmax output layer exhibit arbitrarily high confidence away from the training data.  I will describe a simple modification to the softmax layer to prevent this type of overconfidence. I will also describe how product-of-expert and mixture-of-expert approximations in Bayesian inference can lead to over or under confidence.  Finally, I will describe a simple interpolation technique to enhance the calibration of predictions in distributed machine learning applications including federated learning.

Talk 2 Abstract: Inverse constraint learning and risk averse reinforcement learning for safe AI

In many applications of reinforcement learning (RL) and control, policies need to satisfy constraints to ensure feasibility, safety or thresholds about key performance indicators. However, some constraints may be difficult to specify.  For instance, in autonomous driving, it is relatively easy to specify a reward function to reach a destination, but implicit constraints followed by expert human drivers to ensure a safe, smooth and comfortable ride are much more difficult to specify.  I will present some techniques to learn soft constraints from expert trajectories in autonomous driving and robotics.  I will also present an alternative to variance based on Gini deviation for risk-averse reinforcement learning.

Speaker Bio:

Pascal Poupart is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo (Canada). He is also a Canada CIFAR AI Chair at the Vector Institute and a member of the Waterloo AI Institute. He serves on the advisory board of the NSF AI Institute for Advances in Optimization (2022-present) at Georgia Tech. He served as Research Director and Principal Research Scientist at the Waterloo Borealis AI Research Lab at the Royal Bank of Canada (2018-2020). He also served as scientific advisor for ProNavigator (2017-2019), ElementAI (2017-2018) and DialPad (2017-2018). His research focuses on the development of algorithms for Machine Learning with application to Natural Language Processing and Material Discovery. He is most well-known for his contributions to the development of Reinforcement Learning algorithms. Notable projects that his research team are currently working on include inverse constraint learning, mean field RL, RL foundation models, Bayesian federated learning, uncertainty quantification, probabilistic deep learning, conversational agents, transcription error correction, sport analytics, adaptive satisfiability and material discovery for CO2 recycling.

Please watch this space for future AI Seminars :

https://engineering.oregonstate.edu/EECS/research/AI

Note to AI Seminar course students: Credit will be given for both seminars

Rajesh Mangannavar,

Graduate Student

Oregon State University  

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AI Seminar Important Reminders:
-> For graduate students in the AI program, attendance is strongly encouraged