2 citations · 3 across the 12 of their papers we have counts for
7 papers · 1 filter
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 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…
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…
Efficient Exploration for LLMs
Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao +1
We present evidence of substantial benefit from efficient exploration in gathering human feedback to improve large language models. In our experiments, an agent sequentially genera…
RLHF and IIA: Perverse Incentives
Wanqiao Xu, Shi Dong, Xiuyuan Lu +3
Existing algorithms for reinforcement learning from human feedback (RLHF) can incentivize responses at odds with preferences because they are based on models that assume independen…
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…