Bayesian Learning
Conditional Density Estimation with Histogram Trees
This makes CDE particularly useful in critical application domains. However, interpretable CDE methods are understudied.
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.
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning
Utilizing mutual learning can effectively enhance the performance of peer BNNs.
Meta-Learning Universal Priors Using Non-Injective Change of Variables
Critical in this context is the prior knowledge accumulated from related tasks.
Rehearsal Learning for Avoiding Undesired Future
Machine learning (ML) models have been widely used to make predictions.
Generating Origin-Destination Matrices in Neural Spatial Interaction Models
Agent-based models (ABMs) are proliferating as decision-making tools across policy areas in transportation, economics, and epidemiology.
Amortized Bayesian Experimental Design for Decision-Making
For example, in personalized medical diagnostics, a model is built based on historical data to facilitate an optimal treatment for a new patient (Bica et al., 2021).