probabilistic models
Probabilistic models represent data and relationships between variables in a probabilistic framework, quantifying uncertainty. These models are foundational in AI for reasoning under uncertainty, making predictions, or inferring hidden structures.
- A Principled Approach to Randomized Selection under Uncertainty: Applications to Peer Review and Grant Funding
- Information-Driven Design of Imaging Systems
- Model Reconciliation via Cost-Optimal Explanations in Probabilistic Logic Programming
- Natural Gradient VI: Guarantees for Non-Conjugate Models
- Normalizing Flows are Capable Models for Continuous Control
- Rao-Blackwellised Reparameterisation Gradients
- Statistical Analysis of an Adversarial Bayesian Weak Supervision Method