Current students

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

Chengyao Fu, Master’s candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Alan Huang and Yuying Li

Sentiment analysis has been widely used in the domain of finance. There are two most common textual sentiment analysis methods in finance: \textit{dictionary-based approach} and \textit{machine learning approach}.

Gaurav Gupta, Master’s candidate
David R. Cheriton School of Computer Science

We propose a mechanism for achieving cooperation and communication in Multi-Agent Reinforcement Learning (MARL) settings by intrinsically rewarding agents for obeying the commands of other agents. At every timestep, agents exchange commands through a cheap-talk channel. During the following timestep, agents are rewarded both for taking actions that conform to commands received as well as for giving successful commands. We refer to this approach as obedience-based learning.

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. 

Professor Shai Ben-David has been appointed a University Research Chair in recognition of his outstanding contributions to machine learning theory, logic, the theory of distributed computation and complexity theory. This prestigious title may be held for up to seven years and is conferred to recognize the exceptional achievements of Waterloo faculty members and to acknowledge their pre-eminence in a field of knowledge.

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

Master’s Thesis Presentation: Asking for Help with a Cost in Reinforcement Learning

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. 

Thursday, March 19, 2020 10:30 am - 11:30 am EDT (GMT -04:00)

AI Seminar: Zero-Shot Learning: Generalized Information Transfer Across Classes

Yuhong Guo, School of Computer Science
Carleton University

The need for annotated data is a fundamental bottleneck in developing automated prediction systems. A key strategy for reducing the reliance on human annotation is to exploit generalized information transfer, where a limited data resource is augmented with labeled data collected from related sources. 

Thursday, April 2, 2020 10:30 am - 11:30 am EDT (GMT -04:00)

CANCELLED • AI Seminar: Graph Guided Predictions

Vikas Garg, Electrical Engineering & Computer Science
Massachusetts Institute of Technology

In this talk I will describe our recent work on effectively using graph structured data. Specifically, I will discuss how to compress graphs to facilitate predictions, understand the capacity of algorithms operating on graphs, and how to infer interaction graphs so as to predict deliberative outcomes.