Vector researchers take leadership role at Conference on Uncertainty in AI

August 11, 2020

The 2020 edition of the Conference on Uncertainty in AI (UAI) was held last week, bringing together academic and industrial researchers and students from a host of fields including statistics, data science, AI, probabilistic reasoning, and decision making.

One of the “big five” conferences on machine learning, UAI focuses on knowledge representation, learning, and reasoning in the presence of uncertainty. A concept inherent to machine learning, uncertainty refers to the imperfect or incomplete information with which machine learning researchers must work.

Originally slated to be held in Toronto, this year’s conference was held virtually, with a number of Vector Faculty Members taking key leadership positions. Roger Grosse was local arrangements chair, Pascal Poupart sat on the senior program committee, and Faculty Member David Duvenaud served as sponsorship chair with the help of Mona Davies and other members of the Vector professional staff.

“Every year UAI attracts some of the most prestigious researchers in the field,” says Duvenaud, noting that there were more than 400 paper submissions. “I myself have published some of my favourite papers at this conference.”

Several of this year’s talks also included Vector Faculty members:

Next year’s edition is currently scheduled to be held in Toronto.

Related:

Keith Strier and Tony Gaffney speak on stage at the Remarkable 2024 conference.

Remarkable 2024 spotlights Canada’s flourishing ecosystem

Merck and Vector logos
News
Partnership

Merck Canada announces collaboration with Vector Institute

Insights

12 AI Trends to watch for in 2024