Events Add an event Speakers Talks Collections
 

Talk Videos from UAI 2020

August 3 - 6, 2020. Online, USA
Watch
Add to favorites
110 Speakers
112 Talks
4 Days
14 Hours of content
This page was created based on publicly available data without event host participation.
If you represent Association for Uncertainty in Artificial Intelligence - please contact us

About the Event

The Conference on Uncertainty in Artificial Intelligence (UAI) was one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. UAI was supported by the Association for Uncertainty in Artificial Intelligence (AUAI).
Share

Featured Speakers

Ellen Novoseller

CI Fellow and Postdoctoral Researcher at University of California, Berkeley
I am fortunate to be a postdoctoral scholar at UC Berkeley, working with Professor Ken Goldberg in the AUTOLAB. My research focuses on robot manipulation, learning from human feedback, and human-robot interaction

David Inouye

Assistant Professor at Purdue University
Assistant Professor in Electrical and Computer Engineering (ECE) at Purdue University. My research interests focus on the fundamentals of machine learning, probabilistic models, unconventional deep learning and explainable AI with an emphasis on density estimation. See https://www.davidinouye.com for the most up-to-date information.

Ehsan Amid

Research Scientis at Google
I am a Research Scientist at Google. I received my PhD in Computer Science (with a focus on Machine Learning) from the University of California, Santa Cruz. I hold a MSc degree in Machine Learning and Data Mining from Aalto University, Finland. I work on theory of machine learning, robust learning, optimization, and dimensionality reduction techniques. I was a student researcher at Google Brain in Mountain View, CA.
See all speakers
Watch

Featured Sessions

August 4
August 5
August 6
Honghua Zhang
Computer Science student at UCLA
Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free

On the Relationship Between Probabilistic Circuits and Determinantal Point Processes

Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free
Ondrej Kuzelka
Assistant Professor at Faculty of Electrical Engineering, Czech Technical University in Prague
Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free

Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles

Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free
Pawel Chilinski
UBS, Director at Quantitative Trader
Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free

Neural Likelihoods via Cumulative Distribution Functions

Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free
Diego Agudelo-España
PhD student at Max Planck Institute (MPI)
Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free

Bayesian Online Prediction of Change Points

Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free
Victor Veitch
Machine Learning Researcher at Google
Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free

Adapting Text Embeddings for Causal Inference

Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free
Noam Finkelstein
PhD student at Johns Hopkins University
Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free

Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals

Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free
Jakob Runge
Researcher at German Aerospace Agency
Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free
Xi Wang
Student at East China Normal University
Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free

Relaxed Multivariate Bernoulli Distribution and Its Applications to Deep Generative Models

Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free
Peiyuan Zhu
Student at The University of British Columbia Department of Statistics
Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free

Slice Sampling for General Completely Random Measures

Available
In cart
Free
Free
Free
Free
Free
Free
Free
Free
see all (112)