Bayesian Learning
Probabilistic size-and-shape functional mixed models
It appears that individuals experience different numbers of small and large growth spurts that differ in magnitude and timing.
Overleaf Example
Large transformer-based foundation models have been commonly used as pre-trained models that can be adapted to different challenging datasets and settings with state-of-the-art generalization performance.
Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data
When training deep neural networks, a model's generalization error is often observed to follow a power scaling law dependent both on the model size and the
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions Rui Y ang
These Q values are often objectives that offline algorithms aim to approximate.
Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
We draw parallels between the behavior of LLMs and the evolution of human culture, as the latter has been extensively studied by cognitive scientists for decades.
A Bayesian Approach to Data Point Selection
Thus, we propose a novel Bayesian approach to DPS.
Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization
Training machine learning and statistical models often involves optimizing a data-driven risk criterion.