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Can we measure the difficulty of an optimization problem?

Author(s)Alpcan, Tansu; Everitt, Tom; Hutter, Marcus
TypeConference paper
Date Published2014
Date CreatedNovember 2-5 2014

Free Lunch for Optimisation under the Universal Distribution

Author(s)Everitt, Tom; Lattimore, Tor; Hutter, Marcus
TypeConference paper
Date Published2014
Date CreatedJuly 6-11 2014

Feature Reinforcement Learning: State of the Art

Author(s)Daswani, Mayank; Sunehag, Peter; Hutter, Marcus
TypeConference paper
Date Published2014
Date CreatedJuly 27-31 2014

Extreme State Aggregation beyond MDPs

Author(s)Hutter, Marcus
TypeConference paper
Date Published2014
Date CreatedOctober 8-10 2014

Bayesian reinforcement learning with exploration

Author(s)Lattimore, Tor; Hutter, Marcus
TypeConference paper
Date Published2014
Date CreatedOctober 8-10 2014

The Sample-Complexity of General Reinforcement Learning

Author(s)Lattimore, Tor; Hutter, Marcus; Sunehag, Peter
TypeConference paper
Date Published2013
Date CreatedJune 16-21 2013

Unifying Probability and Logic for Learning

Author(s)Hutter, Marcus; Lloyd, John; Ng, Kee Siong; Uther, William T.B.
TypeConference paper
Date Published2013
Date CreatedAugust 3-9 2013

Concentration and Confidence for Discrete Bayesian Sequence Predictors

Author(s)Lattimore, Tor; Hutter, Marcus; Sunehag, Peter
TypeConference paper
Date Published2013
Date CreatedOctober 6-9 2013

No free lunch versus Occam's Razor in supervised learning

Author(s)Lattimore, Tor; Hutter, Marcus
TypeConference paper
Date Published2013
Date CreatedNovember 30-December 2 2011

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

Author(s)Sunehag, Peter; Shao, Wen; Hutter, Marcus
TypeConference paper
Date Published2012
Date CreatedDecember 5-7 2012

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