398 citations · 1.8k across the 163 of their papers we have counts for
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Principles of Solomonoff Induction and AIXI
Peter Sunehag, Marcus Hutter
We identify principles characterizing Solomonoff Induction by demands on an agent's external behaviour. Key concepts are rationality, computability, indifference and time consisten…
No Free Lunch versus Occam's Razor in Supervised Learning
Tor Lattimore, Marcus Hutter
The No Free Lunch theorems are often used to argue that domain specific knowledge is required to design successful algorithms. We use algorithmic information theory to argue the ca…
Context Tree Switching
Joel Veness, Kee Siong Ng, Marcus Hutter +1
This paper describes the Context Tree Switching technique, a modification of Context Tree Weighting for the prediction of binary, stationary, n-Markov sources. By modifying Context…
Feature Reinforcement Learning In Practice
Phuong Nguyen, Peter Sunehag, Marcus Hutter
Following a recent surge in using history-based methods for resolving perceptual aliasing in reinforcement learning, we introduce an algorithm based on the feature reinforcement le…
Asymptotically Optimal Agents
Tor Lattimore, Marcus Hutter
Artificial general intelligence aims to create agents capable of learning to solve arbitrary interesting problems. We define two versions of asymptotic optimality and prove that no…
Universal Prediction of Selected Bits
Tor Lattimore, Marcus Hutter, Vaibhav Gavane
Many learning tasks can be viewed as sequence prediction problems. For example, online classification can be converted to sequence prediction with the sequence being pairs of input…