451 citations · 1.7k across the 73 of their papers we have counts for
13 papers · 2 filters
Inclusive Artificial Intelligence
Dilip Arumugam, Shi Dong, Benjamin Van Roy
Prevailing methods for assessing and comparing generative AIs incentivize responses that serve a hypothetical representative individual. Evaluating models in these terms presumes h…
An Information-Theoretic Analysis of Compute-Optimal Neural Scaling Laws
Hong Jun Jeon, Benjamin Van Roy
We study the compute-optimal trade-off between model and training data set sizes for large neural networks. Our result suggests a linear relation similar to that supported by the e…
Posterior Sampling for Continuing Environments
Wanqiao Xu, Shi Dong, Benjamin Van Roy
We develop an extension of posterior sampling for reinforcement learning (PSRL) that is suited for a continuing agent-environment interface and integrates naturally into agent desi…
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning
Dilip Arumugam, Mark K. Ho, Noah D. Goodman +1
Throughout the cognitive-science literature, there is widespread agreement that decision-making agents operating in the real world do so under limited information-processing capabi…
Is Stochastic Gradient Descent Near Optimal?
Yifan Zhu, Hong Jun Jeon, Benjamin Van Roy
The success of neural networks over the past decade has established them as effective models for many relevant data generating processes. Statistical theory on neural networks indi…
Robustness of Epinets against Distributional Shifts
Xiuyuan Lu, Ian Osband, Seyed Mohammad Asghari +4
Recent work introduced the epinet as a new approach to uncertainty modeling in deep learning. An epinet is a small neural network added to traditional neural networks, which, toget…