6 papers · 1 filter
Choice Between Partial Trajectories: Disentangling Goals from Beliefs
Henrik Marklund, Benjamin Van Roy
As AI agents generate increasingly sophisticated behaviors, manually encoding human preferences to guide these agents becomes more challenging. To address this, it has been suggest…
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…
Adaptive Crowdsourcing Via Self-Supervised Learning
Anmol Kagrecha, Henrik Marklund, Benjamin Van Roy +2
Common crowdsourcing systems average estimates of a latent quantity of interest provided by many crowdworkers to produce a group estimate. We develop a new approach -- predict-each…
An Information-Theoretic Analysis of In-Context Learning
Hong Jun Jeon, Jason D. Lee, Qi Lei +1
Previous theoretical results pertaining to meta-learning on sequences build on contrived assumptions and are somewhat convoluted. We introduce new information-theoretic tools that…