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Friday, June 24, 2022 1:00 pm - 2:00 pm EDT (GMT -04:00)

PhD Seminar • Machine Learning • Out-of-distribution Detection with Flow Generative Models

Please note: This PhD seminar will be given online.

Dihong Jiang, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Yaoliang Yu

Out-of-distribution (OOD) data come from a distribution that is different from training data. Detecting OOD data contributes to secure deployment of machine learning models. Currently, deep generative models have been widely used as an unsupervised approach for OOD detection.

Tuesday, July 5, 2022 11:00 am - 12:00 pm EDT (GMT -04:00)

Master’s Thesis Presentation • Cryptography, Security and Privacy (CrySP) • Towards Effective Measurement of Membership Privacy Risk for Machine Learning Models

Please note: This master’s thesis presentation will be given online.

Vasisht Duddu, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor N. Asokan

Thursday, July 7, 2022 1:30 pm - 2:30 pm EDT (GMT -04:00)

Master’s Thesis Presentation • Cryptography, Security and Privacy (CrySP) • Security and Ownership Verification in Deep Reinforcement

Please note: This master’s thesis presentation will be given online.

Ching-wen Wang, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor N. Asokan

Deep reinforcement learning (DRL) has seen many successes in complex tasks such as robot manipulation, autonomous driving, and competitive games. However, there are few studies on the security threats against DRL systems. In this thesis, we focus on two security concerns in DRL.

Friday, July 8, 2022 1:00 pm - 2:00 pm EDT (GMT -04:00)

Master’s Research Paper Presentation • Algorithms and Complexity • Quantum Game Theory and the Complexity of Approximating Quantum Nash Equilibria

Please note: This master’s research paper presentation will be given online.

John Bostanci, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Richard Cleve