Peter Sunehag

发表

Variable Metric Stochastic Approximation Theory

S. V. N. Vishwanathan, Nicol N. Schraudolph, Peter Sunehag, 2009, AISTATS.

Rationality, optimism and guarantees in general reinforcement learning

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

Emerge and spread models and word burstiness

Peter Sunehag, 2007, AISTATS.

Sparse Kernel-SARSA(λ) with an Eligibility Trace

Scott Sanner, Peter Sunehag, Bhaskara Marthi, 2011, ECML/PKDD.

Asynchronous Brain Computer Interface using Hidden Semi-Markov Models

Tamás D. Gedeon, Peter Sunehag, Gareth Oliver, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

(Non-)Equivalence of Universal Priors

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

On Ensemble Techniques for AIXI Approximation

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

Using Localization and Factorization to Reduce the Complexity of Reinforcement Learning

Marcus Hutter, Peter Sunehag, 2015, AGI.

Principles of Solomonoff Induction and AIXI pdf

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

On Nicod's Condition, Rules of Induction and the Raven Paradox pdf

Peter Sunehag, Hadi Mohasel Afshar, Peter Sunehag, 2013, ArXiv.

Concentration and Confidence for Discrete Bayesian Sequence Predictors

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

Feature Reinforcement Learning using Looping Suffix Trees

Marcus Hutter, Peter Sunehag, Mayank Daswani, 2012, EWRL.

Reinforcement Learning in Large Discrete Action Spaces pdf

Peter Sunehag, Gabriel Dulac-Arnold, Richard Evans, 2015, ArXiv.

Online feature selection for Brain Computer Interfaces

Tamás D. Gedeon, Peter Sunehag, Gareth Oliver, 2013, 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB).

Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward pdf

Guy Lever, Joel Z. Leibo, Max Jaderberg, 2018, AAMAS.

Malthusian Reinforcement Learning

Joel Z. Leibo, Iain Dunning, Peter Sunehag, 2018, AAMAS.

Adaptive Context Tree Weighting

Marcus Hutter, Peter Sunehag, Wen Shao, 2012, 2012 Data Compression Conference.

Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems

Tom Eccles, Guy Lever, Joel Z. Leibo, 2019, Artificial Life Conference Proceedings.

Q-learning for history-based reinforcement learning

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

Context Tree Maximizing

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

Coding of Non-Stationary Sources as a Foundation for Detecting Change Points and Outliers in Binary Time-Series

Marcus Hutter, Peter Sunehag, Wen Shao, 2012, AusDM.

Consistency of Feature Markov Processes

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

Recursive channel selection techniques for brain computer interfaces

Tamás D. Gedeon, Peter Sunehag, Gareth Oliver, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

The Sample-Complexity of General Reinforcement Learning

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

Learning to Incentivize Other Learning Agents pdf

Hongyuan Zha, Mehrdad Farajtabar, Jiachen Yang, 2020, NeurIPS.

Feature Reinforcement Learning in Practice pdf

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

A Dual Process Theory of Optimistic Cognition

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

Wearable sensor activity analysis using semi-Markov models with a grammar

Alexander J. Smola, Peter Sunehag, Gideon Dror, 2010, Pervasive Mob. Comput..

Learning Agents with Evolving Hypothesis Classes

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

Semi-Markov kMeans Clustering and Activity Recognition from Body-Worn Sensors

Peter Sunehag, Matthew W. Robards, 2009, 2009 Ninth IEEE International Conference on Data Mining.

Optimistic AIXI

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

Optimistic Agents Are Asymptotically Optimal

Marcus Hutter, Peter Sunehag, 2012, Australasian Conference on Artificial Intelligence.

Context tree maximizing reinforcement learning

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

Subcouples of codimension one and interpolation of operators that almost agree

Peter Sunehag, Peter Sunehag, 2004, J. Approx. Theory.

Intelligence as Inference or Forcing Occam on the World

Marcus Hutter, Peter Sunehag, 2014, AGI.

Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions pdf

Peter Sunehag, Gabriel Dulac-Arnold, Yori Zwols, 2015, ArXiv.

Context Tree Weighting

Marcus Hutter, Peter Sunehag, Marcus Hutter, 2013 .

Axioms for Rational Reinforcement Learning pdf

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

Feature Reinforcement Learning: State of the Art

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

Deep Reinforcement Learning in Large Discrete Action Spaces

Richard Evans, Peter Sunehag, Timothy A. Mann, 2015, 1512.07679.

Gradient Based Algorithms with Loss Functions and Kernels for Improved On-Policy Control

Peter Sunehag, Matthew W. Robards, 2011, EWRL.

Reinforcement learning with value advice

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

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot pdf

Joel Z. Leibo, Peter Sunehag, Thore Graepel, 2021, ICML.

Induced Graph Semantics: Another Look at the Hammersley-Clifford Theorem

Peter Sunehag, Sears Tim, 2007 .

Using two-stage conditional word frequency models to model word burstiness and motivating TF-IDF

Peter Sunehag, 2007 .