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Poster Spotlights 3 - Uncertainty in Artificial Intelligence

Offered By: Uncertainty in Artificial Intelligence via YouTube

Tags

Machine Learning Courses Kernel Methods Courses Causal Inference Courses Text Classification Courses Differential Privacy Courses Uncertainty Quantification Courses Representation Learning Courses

Course Description

Overview

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Explore cutting-edge research in artificial intelligence and machine learning through this 17-minute conference session from the Uncertainty in Artificial Intelligence (UAI) 2023 conference. Dive into poster spotlights covering diverse topics such as causal representation learning, policy selection, independence criteria, Bayesian model approximation, causal inference with missing data, uncertainty interpretation in text classifiers, kernel calibration tests, and private prediction methods. Gain insights from leading researchers as they present their work on BISCUIT, efficient policy selection tests, Nyström M-Hilbert-Schmidt Independence Criterion, JANA for complex Bayesian models, causal inference with outcome-dependent missingness, CUE framework for text classifier uncertainty, fast score-based kernel calibration tests, and private kernelized nearest neighbors. Chaired by Jakob Runge, this session offers a concise overview of innovative approaches addressing uncertainty in AI across various domains.

Syllabus

UAI 2023 Poster Spotlights 3


Taught by

Uncertainty in Artificial Intelligence

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