5 papers
Sample-Efficient Optimisation over the Outputs of Generative Models
Samuel Willis, Paul Duckworth, Jack Simons +10
Modern generative AI models, such as diffusion and flow matching models, can sample from rich data distributions. However, many applications, especially in science and engineering,…
System-Level Analysis of Module Uncertainty Quantification in the Autonomy Pipeline
Sampada Deglurkar, Haotian Shen, Anish Muthali +5
Modern autonomous systems with machine learning components often use uncertainty quantification to help produce assurances about system operation. However, there is a lack of conse…
Linear combinations of latents in generative models: subspaces and beyond
Erik Bodin, Alexandru Stere, Dragos D. Margineantu +2
Sampling from generative models has become a crucial tool for applications like data synthesis and augmentation. Diffusion, Flow Matching and Continuous Normalising Flows have show…
Reinforcement Learning-Based Air Traffic Deconfliction
Denis Osipychev, Dragos Margineantu, Girish Chowdhary
Remain Well Clear, keeping the aircraft away from hazards by the appropriate separation distance, is an essential technology for the safe operation of uncrewed aerial vehicles in c…
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI
Daniel J. Fremont, Johnathan Chiu, Dragos D. Margineantu +2
We demonstrate a unified approach to rigorous design of safety-critical autonomous systems using the VerifAI toolkit for formal analysis of AI-based systems. VerifAI provides an in…