398 citations · 1.8k across the 163 of their papers we have counts for
6 papers · 1 filter
Model Selection by Loss Rank for Classification and Unsupervised Learning
Minh-Ngoc Tran, Marcus Hutter
Hutter (2007) recently introduced the loss rank principle (LoRP) as a generalpurpose principle for model selection. The LoRP enjoys many attractive properties and deserves further…
Featureless 2D-3D Pose Estimation by Minimising an Illumination-Invariant Loss
Srimal Jayawardena, Marcus Hutter, Nathan Brewer
The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision ranging from robotic vision to image analysis. Our prop…
Consistency of Feature Markov Processes
Peter Sunehag, Marcus Hutter
We are studying long term sequence prediction (forecasting). We approach this by investigating criteria for choosing a compact useful state representation. The state is supposed to…
Reinforcement Learning via AIXI Approximation
Joel Veness, Kee Siong Ng, Marcus Hutter +1
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian…
A Bayesian View of the Poisson-Dirichlet Process
Wray Buntine, Marcus Hutter
The two parameter Poisson-Dirichlet Process (PDP), a generalisation of the Dirichlet Process, is increasingly being used for probabilistic modelling in discrete areas such as langu…
Model Selection with the Loss Rank Principle
Marcus Hutter, Minh-Ngoc Tran
A key issue in statistics and machine learning is to automatically select the "right" model complexity, e.g., the number of neighbors to be averaged over in k nearest neighbor (kNN…