4 articles tagged with #gaussian-processes. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers propose SEED-SET, a new Bayesian experimental design framework for ethical testing of autonomous systems like drones in high-stakes environments. The system uses hierarchical Gaussian Processes to model both objective evaluations and subjective stakeholder judgments, generating up to 2x more optimal test candidates than baseline methods.
Researchers developed a Bayesian framework combining particle filters and Gaussian processes for robotic tactile object recognition and pose estimation. The system can identify known objects, detect novel objects, and transfer knowledge to learn new shapes through active touch exploration.
Researchers introduce COMBRL, a new reinforcement learning algorithm designed for continuous-time systems using nonlinear ordinary differential equations. The algorithm achieves sublinear regret and better sample efficiency compared to existing methods by combining probabilistic models with uncertainty-aware exploration.
Researchers established a new theoretical framework connecting Bayesian neural networks to Gaussian processes, developing improved convergence results and identifiability properties. They introduced a scalable computational method using Nyström approximation for training and prediction, demonstrating competitive performance on real-world datasets.
