Learning Graphical Models
Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors
The black hole imaging problem is non-convex and highly ill-posed due to severe noise corruption and measurement sparsity.
State-free Reinforcement Learning
Reinforcement learning (RL) studies the problem where an agent interacts with an unknown environment to optimize cumulative rewards/losses [Sutton and Barto, 2018].
Conditional Density Estimation with Histogram Trees
This makes CDE particularly useful in critical application domains. However, interpretable CDE methods are understudied.
Reinforcement Learning with L TL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards Xuan-Bach Le
In this work, we study the relationship between these objectives.
Label Noise: Ignorance Is Bliss
We establish a new theoretical framework for learning under multi-class, instance-dependent label noise. This framework casts learning with label noise as a form of domain adaptation, in particular, domain adaptation under posterior drift.
