Thesis defence

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Friday, April 24, 2020 4:00 pm - 4:00 pm EDT (GMT -04:00)

Master’s Thesis Presentation • Machine Learning — Local Graph Clustering Using l1-regularized PageRank Algorithms

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

Chufeng Hu, Master’s candidate
David R. Cheriton School of Computer Science

Local graph clustering methods are used to find small- and medium-scale clusters without traversing the graph. It has been shown that the combination of the Approximate Personalized PageRank (APPR) algorithm and the sweep method can efficiently detect a small cluster around the starting vertex. 

Friday, May 1, 2020 1:30 pm - 1:30 pm EDT (GMT -04:00)

Master’s Thesis Presentation • Artificial Intelligence — Novel Directions for Multiagent Trust Modeling in Online Social Networks

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

Alexandre Parmentier, Master’s candidate
David R. Cheriton School of Computer Science

This thesis presents two works with the shared goal of improving the capacity of multiagent trust modeling to be applied to social networks. 

Wednesday, April 15, 2020 11:00 am - 11:00 am EDT (GMT -04:00)

Master’s Thesis Presentation • Artificial Intelligence — Asking for Help with a Cost in Reinforcement Learning

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

Colin Vandenhof, Master’s candidate
David R. Cheriton School of Computer Science

Reinforcement learning (RL) is a powerful tool for developing intelligent agents, and the use of neural networks makes RL techniques more scalable to challenging real-world applications, from task-oriented dialogue systems to autonomous driving. However, one of the major bottlenecks to the adoption of RL is efficiency, as it often takes many time steps to learn an acceptable policy.