Faculty

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.

Renjie Liao, Department of Computer Science
University of Toronto

Graphs are ubiquitous in many domains like computer vision, natural language processing, computational chemistry, and computational social science. Although deep learning has achieved tremendous success, effectively handling graphs is still challenging due to their discrete and combinatorial structures. In this talk, I will discuss my recent work which improves deep learning on graphs from both modeling and algorithmic perspectives.

Ke Li, Research Scientist
Google

In this talk, I will present our work on overcoming two long-standing problems in machine learning and computer vision

Taylor Denouden, Master’s candidate
David R. Cheriton School of Computer Science

Recently, much research has been published for detecting when a classification neural network is presented with data that does not fit into one of the class labels the network learned at train time. These so-called out-of-distribution (OOD) detection techniques hold promise for improving safety in systems where unusual or novel inputs may result in errors that endanger human lives.

Han Zhao, Machine Learning Department
Carnegie Mellon University

The success of supervised machine learning in recent years crucially hinges on the availability of large-scale and unbiased data, which is often time-consuming and expensive to collect. Recent advances in deep learning focus on learning invariant representations that have found abundant applications in both domain adaptation and algorithmic fairness. However, it is not clear what price we have to pay in terms of task utility for such universal representations. In this talk, I will discuss my recent work on understanding and learning invariant representations. 

Lifu Huang, Department of Computer Science
University of Illinois at Urbana–Champaign

Who? What? When? Where? Why? are fundamental questions asked when gathering knowledge about and understanding a concept, topic, or event. The answers to these questions underpin the key information conveyed in the overwhelming majority, if not all, of language-based communication. Unfortunately, typical machine learning models and Information Extraction (IE) techniques heavily rely on human annotated data, which is usually very expensive and only available and compiled for very limited types or languages, rendering them incapable of dealing with information across various domains, languages, or other settings.

Steven Y. Feng
David R. Cheriton School of Computer Science

For conversational AI and virtual assistants to communicate with humans in a realistic way, they must exhibit human characteristics such as expression of emotion and personality. Current attempts toward constructing human-like dialogue agents have presented significant difficulties.