Meta-Learning on Chen Kai Blog

Reinforcement Learning (11): Hierarchical RL and Meta-Learning

Sat, 20 Sep 2025 09:00:00 +0000

Standard RL treats every problem as a flat sequence of atomic decisions: observe state, pick an action, receive a reward, repeat. That works when the horizon is short and rewards are dense, but it breaks down on the kind of tasks humans solve effortlessly. “Make breakfast” is not one decision; it is a tree of subtasks — brew coffee, fry eggs, toast bread, plate it up — each of which is itself a small policy. Hierarchical RL (HRL) lets agents reason and act at multiple timescales by treating macro-actions as first-class citizens.

Transfer Learning (4): Few-Shot Learning

Mon, 19 May 2025 09:00:00 +0000

Show a child one photograph of a pangolin and they will spot pangolins for life. Show a deep learning model one photograph and it will give you a uniformly random guess. Few-shot learning is the field that closes that gap: building classifiers that work with only one to ten labeled examples per class.

The trick is not to memorize individual classes harder. It is to learn how to learn from very few examples, then carry that ability over to brand-new classes at test time. This article covers the two families that dominate the field today: metric learning, which learns a good distance function, and meta-learning, which learns a good initialization.

Graph Neural Networks for Learning Equivariant Representations of Neural Networks

Sun, 03 Apr 2022 09:00:00 +0000

Shuffling the hidden neurons of a trained MLP yields the exact same function, but the flat parameter vector looks entirely different. This fact ruins most attempts at “learning over neural networks”: naive representations treat two functionally identical models as unrelated points in parameter space, causing the downstream learner to waste capacity rediscovering a symmetry it should have for free. This paper, Graph Neural Networks for Learning Equivariant Representations of Neural Networks (Kofinas et al., ICML 2024), proposes a clean fix: turn the network into a graph and use a GNN whose architecture natively respects the relevant permutation symmetry.