All CFPs on WikiCFP
Present CFP : 2021
The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to learning and reasoning in the presence of uncertainty.
We invite papers that describe new theory, methodology and/or applications related to machine learning and statistics. We welcome submissions by authors who are new to the UAI conference, or on new and emerging topics. We also encourage submissions on applications, especially those that inspire new methodologies or novel combinations of existing methodologies, provided that some intersection with other UAI topics exists (please see subject areas below).
Submitted papers will be reviewed based on their novelty, technical quality, potential impact and clarity of writing. For papers that rely on empirical evaluations, the experimental methods and results should be clear, well executed, and reproducible. Authors are strongly encouraged to make code and data available.
Paper submission deadline February 19th, 23:59 UTC, 2021
Author response period April 14th - April 20th, 2021
Author notification May 12th, 2021
Tutorials July 26th, 2021
Main Conference July 27th - July 29th, 2021
Workshops July 30th, 2021
When submitting a paper, you will be asked to select one primary subject area, and up to 5 secondary subject areas from the sets of terms below. The terms have been grouped to provide a somewhat systematic overview of topics relevant to the UAI conference. For example, a paper about a new approximate inference algorithm for dynamic Bayesian network with applications to a problem in biology could select the combination primary = Models: (Dynamic) Bayesian networks, secondary = [Application: Computational Biology, Algorithms: Approximate Inference] and so on.
The list of subject areas appears to authors and reviewers in the CMT conference management system. Below you find a list for your reference.
Missing Data Handling
Monte Carlo Methods
Optimization - Combinatorial
Optimization - Convex
Optimization - Discrete
Optimization - Non-Convex
Earth System Science
Natural Language Processing
Planning and Control
Privacy and Security
Sustainability and Climate Science
Text and Web Data
Compressed Sensing and Dictionary Learning
Hashing and Encoding
Multitask and Transfer Learning
Online and Anytime Learning
Policy Optimization and Policy Learning
(Dynamic) Bayesian Networks
Graphical Models - Directed
Graphical Models - Undirected
Graphical Models - Mixed
Markov Decision Processes
Models for Relational Data
Spatial and Spatio-Temporal Models
Temporal and Sequential Models
Topic Models and Latent Variable Models
Computational and Statistical Trade-Offs
Knowledge Representation Languages