Learning types
Here we present different Learning types.
- Abstract
- Active Learning
- Reinforcement Learning (RL)
- On-policy and Off-policy learning
- Policy-based methods
- Multi-task Learning (MTL)
- Meta-Learning
- Continual/Life-long Learning
- Online/Offline Learning
- Network Architecture Search (NAS)
- Bayesian Learning (BL)
- Bayes topics
- Bayes in Deep Learning
- Uncertainties
- Optimization under uncertainty
- Gaussian Process and Kernels
- Summary
Abstract
Active Learning
Reinforcement Learning (RL)
On-policy and Off-policy learning
Policy-based methods
- Note that RL methods split into two types: value-based (which we learned about in STATE SPACE slides in here and here) and policy-based which we learn here. The difference between these two types can be seen here.
- About policy-gradient and AC in here.
Multi-task Learning (MTL)
Meta-Learning
Continual/Life-long Learning
Online/Offline Learning
Network Architecture Search (NAS)
Bayesian Learning (BL)
Bayes topics
Bayes in Deep Learning
Uncertainties
Optimization under uncertainty
Gaussian Process and Kernels
Gaussian Process
- So how to draw the plot of mean function + uncertainty? Simply by calculating the mean and covariance in the train+test points.
- Note that it is actually $\mathbf{f}^* \vert \mathbf{y}$ and not $\mathbf{f}^* \vert \mathbf{X}^*$ GP posterior (see in black box).
- See different GP distributions in here.
- More on GP here, here, and a python implementation here.
- Great (with detailed math) GP explanation, following sparse/var GP versions: here and here. Sparse GP is to reduce K dimensions, and variational as a way to avoid original K huge calculations.
- More on BLR here and here.
- See “The automatic statistician” in here at time ~ 1:34:00.