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- Fairness without Regret (2019)

[shows how to increase fairness without compromising excellence] - Universal artificial intelligence: Practical agents and fundamental challenges
*(with T. Everitt)*

In Foundations of Trusted Autonomy (2018) 15-46

[advances in the 6 years since the last survey] - Universal Learning Theory

Encyclopedia of Machine Learning (2017) 1295-1304

[an extended encyclopedic entry; dense and technical] - To Create a Super-Intelligent Machine, Start with an Equation
*The Conversation, 28 November (2013) 1-5*

[popular science news article; top 10 of 700+ ANU articles] - One Decade of Universal Artificial Intelligence

In Theoretical Foundations of Artificial General Intelligence (2012) 67-88

[gentle introduction, history, future, and discussion] - Can Intelligence Explode?

Journal of Consciousness Studies, 19:1-2 (2012) 143-166

[some thoughts on the technological singularity] - A Philosophical Treatise of Universal Induction
*(with S. Rathmanner)*

Entropy, 16:6 (2011) 1076-1136

[philosophical, statistical, and computational foundations] - Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence

In Randomness through Computation (2011) 159-169

[a personal account on the past and future for a general audience] - A Complete Theory of Everything (will be subjective)

Algorithms, 3:4 (2010) 329-350

[importance of observer localization] - Universal Intelligence: A Definition of Machine Intelligence
*(with S. Legg)*

Minds and Machines, 17:4 (2007) 391-444

[a gentle non-mathematical introduction; recommended] - A Collection of Definitions of Intelligence
*(with S. Legg)*

Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, 157 (2007) 17-24

[70+ definitions by psychologists, AI researchers, and groups] - Algorithmic Information Theory: a brief non-technical guide to the field

Scholarpedia, 2:3 (2007) 2519

[an extended encyclopedic entry; good coverage]

- Rationality, Optimism and Guarantees in General Reinforcement Learning
*(with P. Sunehag)*

Journal of Machine Learning Research, 16 (2015) 1345-1390

[asymptotic optimality of optimistic agents in very large environment classes] - Probabilities on Sentences in an Expressive Logic
*(with J.W. Lloyd & K.S. Ng & W.T.B. Uther)*

Journal of Applied Logic, 11 (2013) 386-420

[unifies probability and logic for learning] - A Monte Carlo AIXI Approximation
*(with J. Veness & K. S. Ng & W. Uther & D. Silver)*

Journal of Artificial Intelligence Research, 40 (2011) 95-142

[implementation of Universal AI. AIXI learns to play Poker,Pacman,TicTacToe,...] - Feature Reinforcement Learning: Part I: Unstructured MDPs (& Part II: Structured MDPs)

Journal of Artificial General Intelligence, 1 (2009) 3-24

[my currently best bet towards practical universal AI] - On Universal Prediction and Bayesian Confirmation

Theoretical Computer Science, 384:1 (2007) 33-48

[solves the induction problem; recommended] - Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability

EATCS Book, Springer, Berlin (2005)

[first complete and sound theory of AI] - Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet

*Journal of Machine Learning Research 4 (2003) 971-1000*

[consistency and bounds for non-i.i.d. time-series prediction; very readable] - Self-Optimizing and Pareto-Optimal Policies
in General Environments based on Bayes-Mixtures

*Proc. 15th Annual Conf. on Computational Learning Theory (COLT-2002) 364-379*

[the core proof of the optimality of the universal AIξ model] -
The Fastest and Shortest Algorithm for All Well-Defined Problems

*International Journal of Foundations of Computer Science, 13:3 (2002) 431-443*

[asymptotically true; my most popular paper] - Family Structure from Periodic Solutions of an Improved Gap Equation
*(with A. Blumhofer)*

Nuclear Physics B484 (1997) 80-96; B494 (1997) 485

[solves the mystery of quarks coming in families with exponential mass spectrum]

- Learning Curve Theory (2021)

[toy model for explaining/understanding the deep learning scaling laws observed by OpenAI et al.] - Universal reinforcement learning algorithms: Survey and experiments
*(with J. Aslanides & J. Leike)*

Proc. 26th International Joint Conf. on Artificial Intelligence (IJCAI-2017) 1403-1410

[survey and online demo of history-based Bayesian RL agents] - Extreme State Aggregation beyond Markov Decision Processes

Theoretical Computer Science, 650 (2016) 73-91

[any reinforcement learning problem can be reduced to a small MDP] - Offline to Online Conversion

Proc. 25th International Conf. on Algorithmic Learning Theory (ALT-2013) 230-244

[includes combinatorial derivations of famous estimators including Good-Turing's] - General Time Consistent Discounting
*(with T. Lattimore)*

Theoretical Computer Science, 519 (2014) 140-154

[complete characterization for general discount] - Sparse Adaptive Dirichlet-Multinomial-like Processes

Journal of Machine Learning Research 30, W&CP (COLT-2013) 432-459

[optimal concentration parameter esp. for large base alphabet] - Universal Knowledge-Seeking Agents for Stochastic Environments
*(with L. Orseau & T. Lattimore)*

Proc. 24th International Conf. on Algorithmic Learning Theory (ALT-2013) 158-172

[makes AIXI a truly autonomous agent by coupling rewards to information gain] - Adaptive Context Tree Weighting
*(with A. O'Neill & W. Shao & P. Sunehag)*

Proc. Data Compression Conference (DCC-2012) 317-326

[extension of CTW for non-stationary sources] - Discrete MDL Predicts in Total Variation

Advances in Neural Information Processing Systems 22 (NIPS-2009) 817-825

[completely general non-iid predictive consistency of countable MDL] - Practical Robust Estimators under the Imprecise Dirichlet Model

International Journal of Approximate Reasoning, 50:2 (2009) 231-242

[simple, fast, and useful expressions] - The Loss Rank Principle for Model Selection

Proc. 20th Annual Conf. on Learning Theory (COLT-2007) 589-603

[for (non)parametric non-stochastic regression and classification like kNN] - Fitness Uniform Optimization
*(with S. Legg)*

IEEE Transactions on Evolutionary Computation, 10:5 (2006) 568-589

[a simple effective selection scheme with no explicit selection pressure] - Hybrid Rounding Techniques for Knapsack Problems
*(with M. Mastrolilli)*

Discrete Applied Mathematics, 154:4 (2006) 640--649

[a linear!time algorithm for the knapsack problem] - Asymptotics of Discrete MDL for Online Prediction
*(with J. Poland)*

IEEE Transactions on Information Theory, 51:11 (2005) 3780-3795

[the Minimal Description Length principle for discrete model classes] - Adaptive Online Prediction by Following the Perturbed Leader
*(with J. Poland)*

Journal of Machine Learning Research 6 (2005) 639-660

[elegant Sqrt(Loss) regret for general loss and adaptive learning rate] - Distribution of Mutual Information from Complete and Incomplete Data
*(with M. Zaffalon)*

Computational Statistics & Data Analysis 48:3 (2005) 633-657

[simple, fast, and useful expressions] - Towards a Universal Theory of Artificial
Intelligence based on Algorithmic Probability and Sequential Decisions

*Proc. 12th European Conf. on Machine Learning (ECML-2001) 226-238*

[first publication of the Universal AIXI model] - Instantons in QCD: Theory and Application of the
Instanton Liquid Model

*Ph.D. Thesis, LMU (1996), hep-ph/0107098*

[nice introduction with some new results, but nothing remarkable]

- Self-modification of policy and utility function in rational agents
*(with T. Everitt & D. Filan & M. Daswani)*

Proc. 9th Conf. on Artificial General Intelligence (AGI-2016) 1-11

[Kurzweil prize for best AGI paper] - Thompson sampling is asymptotically optimal in general environments
*(with J. Leike & T. Lattimore & L. Orseau)*

Proc. 32nd International Conf. on Uncertainty in Artificial Intelligence (UAI-2016) 417-426

[Best student paper] - A Monte Carlo AIXI Approximation
*(with J. Veness & K. S. Ng & W. Uther & D. Silver)*

Journal of Artificial Intelligence Research, 40 (2011) 95-142

[Honorable mention for the 2014 IJCAI-JAIR best paper prize] - Feature Markov decision processes

Proc. 2nd Conf. on Artificial General Intelligence (AGI-2009) 61-66

[First runner-up for the Kurzweil best paper prize] - Bayesian Regression of Piecewise Constant Functions

Bayesian Statistics 8 (ISBA-2007) 607-612

[Lindley prize for innovative research in Bayesian statistics]

- Long hyper-linked list of publications in my CV (compact)
- BibTeX with abstracts and links to papers, slides, code, latex, publisher, etc. (recommended)
- ArXiv: Publications + LaTeX source (incomplete after 2012)
- DBLP Bibliography Server: list of publications + coauthor information
- Google Scholar: list of publications + citation information
- ORCID: list of publications
- Semantic Scholar: list of publications + citation information
- Microsoft Academic: list of publications + citation statistics
- Research Gate: list of publications + citation statistics
- Projects and Publications sorted w.r.t. topic
- Old publications in particle physics (QCD, Instantons)
- Outdated publications in computer graphics and others

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