Ivana Kajić, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Chris Eliasmith
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}.
Neil Budnarain, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Jesse Hoey
Yao Lu, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Olga Vechtomova
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.
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.
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.
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.