Marcus Hutter

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Universal Artificial Intellegence - Sequential Decisions Based on Algorithmic Probability

Marcus Hutter, Marcus Hutter, 2005, Texts in Theoretical Computer Science. An EATCS Series.

A Gentle Introduction to The Universal Algorithmic Agent AIXI

Marcus Hutter, 2003 .

Universal Intelligence: A Definition of Machine Intelligence

Shane Legg, Marcus Hutter, S. Legg, 2007, Minds and Machines.

Sparse Adaptive Dirichlet-Multinomial-like Processes pdf

Marcus Hutter, Marcus Hutter, 2013, COLT.

Optimality of universal Bayesian prediction for general loss and alphabet

Marcus Hutter, 2003 .

A Bayesian Review of the Poisson-Dirichlet Process pdf

Marcus Hutter, Wray L. Buntine, Marcus Hutter, 2010, ArXiv.

Distribution of mutual information from complete and incomplete data

Marco Zaffalon, Marcus Hutter, Marcus Hutter, 2004, Comput. Stat. Data Anal..

Avoiding Wireheading with Value Reinforcement Learning pdf

Marcus Hutter, Tom Everitt, Marcus Hutter, 2016, AGI.

Multi-task reinforcement learning : shaping and feature selection

Marcus Hutter, S. Sanner, 2011 .

A Monte-Carlo AIXI Approximation pdf

Joel Veness, Marcus Hutter, David Silver, 2009, J. Artif. Intell. Res..

Principles of Solomonoff Induction and AIXI pdf

Marcus Hutter, Peter Sunehag, Marcus Hutter, 2011, Algorithmic Probability and Friends.

(Non-)Equivalence of Universal Priors

Marcus Hutter, Peter Sunehag, Ian Wood, 2013, Algorithmic Probability and Friends.

Context Tree Switching

Joel Veness, Michael H. Bowling, Marcus Hutter, 2011, 2012 Data Compression Conference.

Temporal Difference Updating without a Learning Rate

Shane Legg, Marcus Hutter, S. Legg, 2007, NIPS.

Feature Reinforcement Learning: Part I. Unstructured MDPs

Marcus Hutter, Marcus Hutter, 2009, J. Artif. Gen. Intell..

Universal Algorithmic Intelligence: A Mathematical Top→Down Approach pdf

Marcus Hutter, Marcus Hutter, 2007, Artificial General Intelligence.

On Ensemble Techniques for AIXI Approximation

Joel Veness, Marcus Hutter, Peter Sunehag, 2012, AGI.

Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability (Texts in Theoretical Computer Science. An EATCS Series)

Marcus Hutter, Marcus Hutter, 2006 .

Fitness uniform optimization

Shane Legg, Marcus Hutter, S. Legg, 2006, IEEE Transactions on Evolutionary Computation.

A Collection of Definitions of Intelligence

Shane Legg, Marcus Hutter, S. Legg, 2007, AGI.

Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions

Marcus Hutter, Marcus Hutter, 2000, ECML.

Self-Optimizing and Pareto-Optimal Policies in General Environments based on Bayes-Mixtures

Marcus Hutter, Marcus Hutter, 2002, COLT.

Free Lunch for optimisation under the universal distribution

Tor Lattimore, Marcus Hutter, Tom Everitt, 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

The subjective computable universe

Marcus Hutter, 2012 .

The Alignment Problem for Bayesian History-Based Reinforcement Learners∗

Marcus Hutter, Tom Everitt, Marcus Hutter, 2019 .

The Alignment Problem for History-Based Bayesian Reinforcement Learners ∗ PUBLIC DRAFT

Marcus Hutter, Tom Everitt, Marcus Hutter, 2018 .

Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective

Marcus Hutter, Tom Everitt, 2019, arXiv.org.

Simulation Algorithms for Computational Systems Biology

Marcus Hutter, 2017, Texts in Theoretical Computer Science. An EATCS Series.

Exact Bayesian regression of piecewise constant functions

Marcus Hutter, 2007 .

On the Optimality of General Reinforcement Learners

Marcus Hutter, J. Leike, 2015 .

Treatise of Universal Induction

Marcus Hutter, Samuel Rathmanner, 2011 .

Observer localization in multiverse theories

Marcus Hutter, 2010 .

IDSIA-1106 General Discounting versus Average Reward

Marcus Hutter, 2006 .

Intelligence : Many definitions

S. Legg, Marcus Hutter, 2006 .

Asymptotics of Discrete MDL for Online Prediction. main

Marcus Hutter, J. Poland, 2020 .

of Random Sequences

Marcus Hutter, A. Muchnik, 2007 .

Algorithmic “Kolmogorov” Complexity

Marcus Hutter, 2008 .

MDL convergence speed for Bernoulli sequences

Marcus Hutter, Jan Poland, Marcus Hutter, 2006, Stat. Comput..

Algorithmic probability

Shane Legg, Marcus Hutter, Paul M. B. Vitányi, 2007, Scholarpedia.

Natural Halting Probabilities , Partial Randomness , and Zeta Functions

Marcus Hutter, P. Vitányi, W. Merkle, 2006 .

Agents in Known Probabilistics Environments

Marcus Hutter, Marcus Hutter, 2005 .

Report from Dagstuhl Seminar 13321 Reinforcement Learning

P. Auer, Marcus Hutter, Laurent Orseau, 2013 .

Neural Networks and the Chomsky Hierarchy

Pedro A. Ortega, S. Legg, Marcus Hutter, 2022, ArXiv.

Method For Recognising An Activity In A Sequence Of Images

Marcus Hutter, Stephen Gould, Peter Anderson, 2019 .

A rapid and efficient learning rule for biological neural circuits

D. Budden, J. Veness, Marcus Hutter, 2021, bioRxiv.

Online Learning of k-CNF Boolean Functions

Marc G. Bellemare, J. Veness, Marcus Hutter, 2014, IJCAI.

Kolmogorov Complexity and Applications

Marcus Hutter, Wolfgang Merkle, Paul M. B. Vitányi, 2006 .

Formal Algorithms for Transformers

Marcus Hutter, Mary Phuong, 2022, ArXiv.

Neural Networks and the Chomsky Hierarchy

Pedro A. Ortega, S. Legg, Marcus Hutter, 2022, ArXiv.

Fully General Online Imitation Learning

Michael K Cohen, Marcus Hutter, Neel Nanda, 2021, ArXiv.

Testing Independence of Exchangeable Random Variables

Marcus Hutter, 2022, ArXiv.

Beyond Bayes-optimality: meta-learning what you know you don't know

Pedro A. Ortega, Jane X. Wang, J. Veness, 2022, ArXiv.

Sequential Learning Of Neural Networks for Prequential MDL

Marcus Hutter, Yazhe Li, J. Bornschein, 2022, ICLR.

Instantons and meson correlation functions in QCD

Marcus Hutter, 1995, hep-ph/9501245.

Kolmogorov Complexity and Applications, 29.01. - 03.02.2006

Marcus Hutter, Paul M. B. Vitányi, Wolfgang Merkle, 2006, Kolmogorov Complexity and Applications.

Atari-5: Distilling the Arcade Learning Environment down to Five Games

Marcus Hutter, Matthew Aitchison, Penny Sweetser, 2022, ICML.

Uniqueness and Complexity of Inverse MDP Models

Marcus Hutter, S. Hansen, 2022, ArXiv.

Generalization Bounds for Transfer Learning with Pretrained Classifiers

Marcus Hutter, Tomer Galanti, Andr'as Gyorgy, 2022, 2212.12532.

Proceedings of the Second Conference on Artificial General Intelligence

Ben Goertzel, Pascal Hitzler, Marcus Hutter, 2009 .

Extreme state aggregation beyond Markov decision processes

Marcus Hutter, Marcus Hutter, 2016, Theor. Comput. Sci..

Extreme State Aggregation beyond MDPs

Marcus Hutter, Marcus Hutter, 2014, ALT.

Context tree maximizing reinforcement learning

Marcus Hutter, Peter Sunehag, Phuong Nguyen, 2012, AAAI 2012.

One Decade of Universal Artificial Intelligence pdf

Marcus Hutter, Marcus Hutter, 2012, ArXiv.

Feature Reinforcement Learning in Practice pdf

Marcus Hutter, Peter Sunehag, Phuong Minh Nguyen, 2011, EWRL.

Consistency of Feature Markov Processes

Marcus Hutter, Peter Sunehag, Marcus Hutter, 2010, ALT.

Reinforcement Learning via AIXI Approximation pdf

Joel Veness, Marcus Hutter, David Silver, 2010, AAAI.

Feature Dynamic Bayesian Networks pdf

Marcus Hutter, Marcus Hutter, 2008, ArXiv.

Feature Markov Decision Processes pdf

Marcus Hutter, Marcus Hutter, 2008, ArXiv.

On Q-learning Convergence for Non-Markov Decision Processes

Marcus Hutter, Sultan Javed Majeed, Marcus Hutter, 2018, IJCAI.

Model Selection by Loss Rank for Classification and Unsupervised Learning

Marcus Hutter, Minh-Ngoc Tran, Marcus Hutter, 2010, 1011.1379.

Asymptotics of discrete MDL for online prediction

Marcus Hutter, Jan Poland, Marcus Hutter, 2005, IEEE Transactions on Information Theory.

Prediction with Expert Advice by Following the Perturbed Leader for General Weights

Marcus Hutter, Jan Poland, Marcus Hutter, 2004, ALT.

Practical Agents and Fundamental Challenges

Marcus Hutter, Tom Everitt, 2016 .

Universal Artificial Intelligence-Practical Agents and Fundamental Challenges

Marcus Hutter, Tom Everitt, 2016 .

Can Intelligence Explode? pdf

Marcus Hutter, Marcus Hutter, 2012, ArXiv.

Fast Non-Parametric Bayesian Inference on Infinite Trees pdf

Marcus Hutter, Marcus Hutter, 2004, AISTATS.

Intelligence and Unambitiousness Using Algorithmic Information Theory

Marcus Hutter, Badri Vellambi, Michael K. Cohen, 2021, IEEE Journal on Selected Areas in Information Theory.

A Gentle Introduction to Quantum Computing Algorithms with Applications to Universal Prediction pdf

Marcus Hutter, Elliot Catt, Marcus Hutter, 2020, ArXiv.

Asymptotically Unambitious Artificial General Intelligence pdf

Marcus Hutter, Michael K Cohen, Badri Vellambi, 2019, AAAI.

AGI Safety Literature Review pdf

Marcus Hutter, Tom Everitt, Gary Lea, 2018, IJCAI.

On the computability of Solomonoff induction and AIXI

Marcus Hutter, Jan Leike, Marcus Hutter, 2017, Theor. Comput. Sci..

Loss Bounds and Time Complexity for Speed Priors pdf

Marcus Hutter, Jan Leike, Daniel Filan, 2016, AISTATS.

On the Computability of Solomonoff Induction and Knowledge-Seeking pdf

Marcus Hutter, Jan Leike, Marcus Hutter, 2015, ALT.

Solomonoff Induction Violates Nicod's Criterion

Marcus Hutter, Jan Leike, Marcus Hutter, 2015, ALT.

On the Computability of AIXI pdf

Marcus Hutter, Jan Leike, Marcus Hutter, 2015, UAI.

On Martin-Löf (non-)convergence of Solomonoff's universal mixture

Tor Lattimore, Marcus Hutter, Tor Lattimore, 2015, Theor. Comput. Sci..

Learning Agents with Evolving Hypothesis Classes

Marcus Hutter, Peter Sunehag, Marcus Hutter, 2013, AGI.

On Martin-Löf Convergence of Solomonoff's Mixture

Tor Lattimore, Marcus Hutter, Tor Lattimore, 2013, TAMC.

No Free Lunch versus Occam's Razor in Supervised Learning

Tor Lattimore, Marcus Hutter, Tor Lattimore, 2011, Algorithmic Probability and Friends.

Universal Prediction of Selected Bits pdf

Tor Lattimore, Marcus Hutter, Vaibhav Gavane, 2011, ALT.

Algorithmic Complexity

Marcus Hutter, Marcus Hutter, 1993 .

Algorithmic Information Theory

Marcus Hutter, Marcus Hutter, G. Chaitin, 1977, IBM J. Res. Dev..

Model Growth

Marcus Hutter, Marcus Hutter, Jeroen van Maanen, 2002 .

Unifying probability and logic for learning

Marcus Hutter, John W. Lloyd, William T. B. Uther, 2013, IJCAI 2013.

Probabilities on Sentences in an Expressive Logic

Marcus Hutter, John W. Lloyd, William T. B. Uther, 2012, J. Appl. Log..

Bayesian Treatment of Incomplete Discrete Data applied to Mutual Information and Feature Selection

Marco Zaffalon, Marcus Hutter, Marcus Hutter, 2003, KI.

Reliable Point Correspondences in Scenes Dominated by Highly Reflective and Largely Homogeneous Surfaces

Hongdong Li, Marcus Hutter, Richard I. Hartley, 2014, ACCV Workshops.

Reflective Features Detection and Hierarchical Reflections Separation in Image Sequences

Marcus Hutter, Stephen Gould, Di Yang, 2014, 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

Universal Compression of Piecewise i.i.d. Sources

Marcus Hutter, Badri N. Vellambi, Cameron Owen, 2018, 2018 Data Compression Conference.

Adaptive Online Prediction by Following the Perturbed Leader pdf

Marcus Hutter, Jan Poland, Marcus Hutter, 2005, J. Mach. Learn. Res..

Robust Feature Selection by Mutual Information Distributions

Marco Zaffalon, Marcus Hutter, Marcus Hutter, 2002, UAI.

Analytical Results on the BFS vs. DFS Algorithm Selection Problem. Part I: Tree Search

Marcus Hutter, Tom Everitt, Marcus Hutter, 2015, Australasian Conference on Artificial Intelligence.

A Topological Approach to Meta-heuristics: Analytical Results on the BFS vs. DFS Algorithm Selection Problem pdf

Marcus Hutter, Tom Everitt, Marcus Hutter, 2015, ArXiv.

Analytical Results on the BFS vs. DFS Algorithm Selection Problem: Part II: Graph Search

Marcus Hutter, Tom Everitt, Marcus Hutter, 2015, Australasian Conference on Artificial Intelligence.

Shaking the foundations: delusions in sequence models for interaction and control pdf

Nando de Freitas, Shane Legg, Joel Veness, 2021, ArXiv.

A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data pdf

Ke Zhang, Huidong Jin, Marcus Hutter, 2009, PAKDD.

Open Problems in Universal Induction & Intelligence pdf

Marcus Hutter, Marcus Hutter, 2009, Algorithms.

Count-Based Exploration in Feature Space for Reinforcement Learning

Marcus Hutter, Tom Everitt, Jarryd Martin, 2017, IJCAI.

Gauge Invariant Quark Propagator in the Instanton Background

Marcus Hutter, 1995, hep-ph/9502361.

A Game-Theoretic Analysis of the Off-Switch Game

Marcus Hutter, Tom Everitt, Elliot Catt, 2017, AGI.

Rationality, optimism and guarantees in general reinforcement learning

Marcus Hutter, Peter Sunehag, Marcus Hutter, 2015, J. Mach. Learn. Res..

Optimistic AIXI

Marcus Hutter, Peter Sunehag, Marcus Hutter, 2012, AGI.

Axioms for Rational Reinforcement Learning pdf

Marcus Hutter, Peter Sunehag, Marcus Hutter, 2011, ALT.

Universal Knowledge-Seeking Agents for Stochastic Environments

Laurent Orseau, Tor Lattimore, Marcus Hutter, 2013, ALT.

Exact Reduction of Huge Action Spaces in General Reinforcement Learning pdf

Marcus Hutter, Sultan Javed Majeed, Marcus Hutter, 2020, AAAI.

Reward tampering problems and solutions in reinforcement learning: a causal influence diagram perspective pdf

Marcus Hutter, Tom Everitt, Marcus Hutter, 2019, Synthese.

Self-Modification of Policy and Utility Function in Rational Agents pdf

Marcus Hutter, Tom Everitt, Daniel Filan, 2016, AGI.

Feature Reinforcement Learning: State of the Art

Marcus Hutter, Peter Sunehag, Mayank Daswani, 2014, AAAI 2014.

A Taxonomy for Abstract Environments.

Shane Legg, Marcus Hutter, S. Legg, 2004 .

Market-Based Reinforcement Learning in Partially Observable Worlds

Jürgen Schmidhuber, Marcus Hutter, Ivo Kwee, 2001, ICANN.

A Philosophical Treatise of Universal Induction pdf

Marcus Hutter, Samuel Rathmanner, Marcus Hutter, 2011, Entropy.

Bayesian Regression of Piecewise Constant Functions pdf

Marcus Hutter, Marcus Hutter, 2006, ArXiv.

Tournament versus fitness uniform selection

Shane Legg, Marcus Hutter, Akshat Kumar, 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

Robust inference of trees

Marco Zaffalon, Marcus Hutter, Marcus Hutter, 2005, Annals of Mathematics and Artificial Intelligence.

Neural Networks and the Chomsky Hierarchy

Pedro A. Ortega, S. Legg, Marcus Hutter, 2022, ICLR.

Fitness uniform selection to preserve genetic diversity

Marcus Hutter, Marcus Hutter, 2001, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

Matching 2-D ellipses to 3-D circles with application to vehicle pose identification

Marcus Hutter, Nathan Brewer, Marcus Hutter, 2009, 2009 24th International Conference Image and Vision Computing New Zealand.

Sequential Extensions of Causal and Evidential Decision Theory

Marcus Hutter, Tom Everitt, Jan Leike, 2015, ADT.

Instantons in QCD: Theory and Application of the Instanton Liquid Model

Marcus Hutter, Marcus Hutter, 1996, hep-ph/0107098.

Proton Spin in the Instanton Background

Marcus Hutter, 1995, hep-ph/9509402.

Classification by decomposition: a novel approach to classification of symmetric 2×2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \beg

Marcus Hutter, Tom Everitt, Mikael Böörs, 2021, Theory and Decision.

Distribution of Mutual Information

Marcus Hutter, Marcus Hutter, 2001, NIPS.

Logarithmic Pruning is All You Need

Marcus Hutter, Laurent Orseau, Omar Rivasplata, 2020, Neural Information Processing Systems.

Learning Curve Theory pdf

Marcus Hutter, Marcus Hutter, 2021, ArXiv.

The Sample-Complexity of General Reinforcement Learning

Tor Lattimore, Marcus Hutter, Peter Sunehag, 2013, ICML.

Counterfactual Credit Assignment in Model-Free Reinforcement Learning

Marcus Hutter, Nicolas Heess, Fabio Viola, 2020, ICML.

Advanced Artificial Agents Intervene in the Provision of Reward

Michael K Cohen, Michael A. Osborne, Marcus Hutter, 2022, AI Mag..

On Representing (Anti)Symmetric Functions pdf

Marcus Hutter, Marcus Hutter, 2020, ArXiv.

On Universal Prediction and Bayesian Confirmation pdf

Marcus Hutter, Marcus Hutter, 2007, Theor. Comput. Sci..

A Dual Process Theory of Optimistic Cognition

Marcus Hutter, Peter Sunehag, Marcus Hutter, 2014, CogSci.

A Formal Measure of Machine Intelligence pdf

Shane Legg, Marcus Hutter, S. Legg, 2006, ArXiv.

Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet

Marcus Hutter, Marcus Hutter, 2003, J. Mach. Learn. Res..

Fairness without Regret pdf

Marcus Hutter, Marcus Hutter, 2019, ArXiv.

Universal Reinforcement Learning Algorithms: Survey and Experiments

Marcus Hutter, John Aslanides, Jan Leike, 2017, IJCAI.

Discriminative Hierarchical Rank Pooling for Activity Recognition

Marcus Hutter, Basura Fernando, Stephen Gould, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

General time consistent discounting

Tor Lattimore, Marcus Hutter, Tor Lattimore, 2014, Theor. Comput. Sci..

Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence pdf

Marcus Hutter, Marcus Hutter, 2011, ArXiv.

Levin Tree Search with Context Models

Marcus Hutter, Laurent Orseau, Levi H.S. Leli, 2023, IJCAI.

Isotuning With Applications To Scale-Free Online Learning pdf

Laurent Orseau, Marcus Hutter, Marcus Hutter, 2021, ArXiv.

Gated Linear Networks

D. Budden, J. Veness, Tor Lattimore, 2019, AAAI.

On Thompson Sampling and Asymptotic Optimality

Laurent Orseau, Tor Lattimore, Marcus Hutter, 2017, IJCAI.

Discrete MDL Predicts in Total Variation

Marcus Hutter, Marcus Hutter, 2009, NIPS.

On the Role of Neural Collapse in Transfer Learning pdf

Marcus Hutter, Tomer Galanti, Andr'as Gyorgy, 2021, ICLR.

Statistical Machine Learning

Marcus Hutter, Marcus Hutter, 2009 .

A Strongly Asymptotically Optimal Agent in General Environments

Marcus Hutter, Michael K. Cohen, Elliot Catt, 2019, IJCAI.

Curiosity Killed the Cat and the Asymptotically Optimal Agent pdf

Marcus Hutter, Michael K. Cohen, Michael K Cohen, 2020, ArXiv.

A Combinatorial Perspective on Transfer Learning pdf

Joel Veness, Marcus Hutter, Jianan Wang, 2020, NeurIPS.

A Noise Tolerant Watershed Transformation with Viscous Force for Seeded Image Segmentation

Marcus Hutter, Stephen Gould, Di Yang, 2012, ACCV.

Proceedings of the 18th international conference on Algorithmic Learning Theory

Rocco A. Servedio, Marcus Hutter, Eiji Takimoto, 2007 .

Defensive Universal Learning with Experts

Marcus Hutter, Jan Poland, Marcus Hutter, 2005, ALT.

PAC Bounds for Discounted MDPs pdf

Tor Lattimore, Marcus Hutter, Tor Lattimore, 2012, ALT.

Bayesian DNA copy number analysis

Marcus Hutter, Francesco Bertoni, Ivo Kwee, 2009, BMC Bioinformatics.

Near-optimal PAC bounds for discounted MDPs

Tor Lattimore, Marcus Hutter, Tor Lattimore, 2014, Theor. Comput. Sci..

Memory-Based Meta-Learning on Non-Stationary Distributions

J. Veness, Marcus Hutter, Tim Genewein, 2023, ICML.

Time Consistent Discounting pdf

Tor Lattimore, Marcus Hutter, Tor Lattimore, 2011, ALT.

A Theory of Universal Artificial Intelligence based on Algorithmic Complexity

Marcus Hutter, Marcus Hutter, 2000, ArXiv.

Q-learning for history-based reinforcement learning

Marcus Hutter, Peter Sunehag, Mayank Daswani, 2013, ACML.

How to Predict with Bayes, MDL, and Experts

Marcus Hutter, Marcus Hutter, 2005 .

An integrated Bayesian analysis of LOH and copy number data

Marcus Hutter, Francesco Bertoni, Ivo Kwee, 2010, BMC Bioinformatics.

Context Tree Weighting

Marcus Hutter, Peter Sunehag, Marcus Hutter, 2013 .

Gradient-based Reinforcement Planning in Policy-Search Methods

Jürgen Schmidhuber, Marcus Hutter, Ivo Kwee, 2001, ArXiv.

On the Possibility of Learning in Reactive Environments with Arbitrary Dependence pdf

Marcus Hutter, Daniil Ryabko, D. Ryabko, 2008, Theor. Comput. Sci..

Performance Guarantees for Homomorphisms Beyond Markov Decision Processes

Sultan Javed Majeed, Marcus Hutter, 2018, AAAI.

Recent Advances in Reinforcement Learning

Scott Sanner, Marcus Hutter, Marcus Hutter, 2011, Lecture Notes in Computer Science.

Reducing Planning Complexity of General Reinforcement Learning with Non-Markovian Abstractions pdf

Marcus Hutter, Sultan Javed Majeed, Sultan J. Majeed, 2021, ArXiv.

Thompson Sampling is Asymptotically Optimal in General Environments pdf

Laurent Orseau, Tor Lattimore, Marcus Hutter, 2016, UAI.

Asymptotically Optimal Agents

Tor Lattimore, Marcus Hutter, Tor Lattimore, 2011, ALT.

Fitness uniform deletion: a simple way to preserve diversity

Shane Legg, Marcus Hutter, S. Legg, 2005, GECCO '05.

Conditions on Features for Temporal Difference-Like Methods to Converge pdf

Marcus Hutter, Sultan Javed Majeed, Samuel Yang-Zhao, 2019, IJCAI.

Combining a Meta-Policy and Monte-Carlo Planning for Scalable Type-Based Reasoning in Partially Observable Environments

Marcus Hutter, H. Kurniawati, Jonathon Schwartz, 2023, arXiv.org.

Universal Learning of Repeated Matrix Games pdf

Marcus Hutter, Jan Poland, Marcus Hutter, 2005, ArXiv.

Master Algorithms for Active Experts Problems based on Increasing Loss Values pdf

Marcus Hutter, Jan Poland, Marcus Hutter, 2005, ArXiv.

Algorithmic information theory

Marcus Hutter, Grünwald, 2008, ArXiv.

Bayesian Reinforcement Learning with Exploration

Tor Lattimore, Marcus Hutter, Tor Lattimore, 2014, ALT.

Limits of Learning about a Categorical Latent Variable under Prior Near-Ignorance pdf

Marco Zaffalon, Marcus Hutter, Alberto Piatti, 2007, Int. J. Approx. Reason..

Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot pdf

Jürgen Schmidhuber, Marcus Hutter, Faustino J. Gomez, 2006, IAS.

Dynamic Intrusion Detection Method for Mobile Ad Hoc Network Using CPDOD Algorithm

Marcus Hutter, 2017 .

Isotuning With Applications To Scale-Free Online Learning

Marcus Hutter, Laurent Orseau, 2021, ArXiv.

Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence

Marcus Hutter, Daniil Ryabko, D. Ryabko, 2006, ALT.

Learning in Reactive Environments with Arbitrary Dependence

Marcus Hutter, Daniil Ryabko, D. Ryabko, 2006, Kolmogorov Complexity and Applications.

Language Modeling Is Compression

J. Veness, Marcus Hutter, Tim Genewein, 2023, ICLR.

Algorithmic information theory pdf

Marcus Hutter, Marcus Hutter, 2007, Scholarpedia.

Universal Agent Mixtures and the Geometry of Intelligence

Marcus Hutter, Len Du, S. Alexander, 2023, AISTATS.

Reinforcement Learning with Information-Theoretic Actuation pdf

Joel Veness, Marcus Hutter, Elliot Catt, 2021, AGI.

Foundations of Induction

Marcus Hutter, Marcus Hutter, 2011 .

Algorithmic information theory

Marcus Hutter, 2007, Scholarpedia.

Sequential Decisions based on Algorithmic Probability

Marcus Hutter, Marcus Hutter, 2008 .

N EURAL N ETWORKS AND THE C HOMSKY H IERARCHY

J. Veness, S. Legg, Marcus Hutter, 2023 .

Asymptotics of Continuous Bayes for Non-i.i.d. Sources pdf

Tor Lattimore, Marcus Hutter, Tor Lattimore, 2014, ArXiv.

Algorithmic information theory

Marcus Hutter, 2008, ArXiv.

Classification by decomposition: a novel approach to classification of symmetric 2 · 2 games

Marcus Hutter, Tom Everitt, Mikael Böörs, 2021 .

On the Existence and Convergence of Computable Universal Priors

Marcus Hutter, Marcus Hutter, 2003, ALT.

Convergence and Loss Bounds for

Marcus Hutter, Bayesian Sequence Prediction, Marcus Hutter, 2003 .

Death and Suicide in Universal Artificial Intelligence

Marcus Hutter, Tom Everitt, Jarryd Martin, 2016, AGI.

Family structure from periodic solutions of an improved gap equation

Marcus Hutter, A. Blumhofer, 1996, hep-ph/9605393.

Evaluating Representations with Readout Model Switching

Marcus Hutter, Yazhe Li, J. Bornschein, 2023, ICLR.

Bayesian Joint Estimation of CN and LOH Aberrations

Marcus Hutter, Francesco Bertoni, Ivo Kwee, 2009, IWANN.

Generalization Bounds for Few-Shot Transfer Learning with Pretrained Classifiers

Marcus Hutter, Tomer Galanti, Andr'as Gyorgy, 2022, 2212.12532.

A Complete Theory of Everything (Will Be Subjective) pdf

Marcus Hutter, Marcus Hutter, 2009, Algorithms.

Chances and Risks of Artificial Intelligence—A Concept of Developing and Exploiting Machine Intelligence for Future Societies

Marcus Hutter, Reinhard Hutter, 2021, Applied System Innovation.