Probabilistic Methods for Increased Robustness in Machine Learning

Explore probabilistic methods for enhancing machine learning robustness in this conference talk from the Alan Turing Institute's Innovation Symposium. Delve into the speaker's insights on improving AI reliability through latent variables, molecular design optimization, and real-world problem-solving. Gain valuable knowledge about cutting-edge techniques in machine learning, including applications in colormnist and other practical scenarios. Learn how these advanced probabilistic approaches can be applied to increase the safety and effectiveness of AI systems across various domains.

Introduction
Overview
Previous Features
Latent Variables
Colormnist
Real World Problem
Molecular Design
Optimization
Conclusion
Questions