5 papers
Continual Learning as Computationally Constrained Reinforcement Learning
Saurabh Kumar, Henrik Marklund, Ashish Rao +4
An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills over a long lifetime could advance the frontier of artificial intelligence capabilities…
Information-Theoretic Foundations for Machine Learning
Hong Jun Jeon, Benjamin Van Roy
The progress of machine learning over the past decade is undeniable. In retrospect, it is both remarkable and unsettling that this progress was achievable with little to no rigorou…
Aligning AI Agents via Information-Directed Sampling
Hong Jun Jeon, Benjamin Van Roy
The staggering feats of AI systems have brought to attention the topic of AI Alignment: aligning a "superintelligent" AI agent's actions with humanity's interests. Many existing fr…
The Need for a Big World Simulator: A Scientific Challenge for Continual Learning
Saurabh Kumar, Hong Jun Jeon, Alex Lewandowski +1
The "small agent, big world" frame offers a conceptual view that motivates the need for continual learning. The idea is that a small agent operating in a much bigger world cannot s…
Information-Theoretic Foundations for Neural Scaling Laws
Hong Jun Jeon, Benjamin Van Roy
Neural scaling laws aim to characterize how out-of-sample error behaves as a function of model and training dataset size. Such scaling laws guide allocation of a computational reso…