Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Hendrik Blockeel,Kristian Kersting,Filip Železný Conference pro

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Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration for this procedure which show the improvement of the order of . for fixed iteration cost over purely sequential versions. Moreover, the multiplicative constants involved have the property of being dimension-free. We also confirm empirically the efficiency of . on real and synthetic problems compared to state-of-the-art competitors.

Collision

0302-9743 dings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers

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Learning from Demonstrations: Is It Worth Estimating a Reward Function?he behavior of the expert. This reward is then optimized to imitate the expert. One can wonder if it is worth estimating such a reward, or if estimating a policy is sufficient. This quite natural question has not really been addressed in the literature right now. We provide partial answers, both from a theoretical and empirical point of view.

crease

Regret Bounds for Reinforcement Learning with Policy Adviceis regret and its computational complexity are independent of the size of the state and action space. Our empirical simulations support our theoretical analysis. This suggests RLPA may offer significant advantages in large domains where some prior good policies are provided.

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Expectation Maximization for Average Reward Decentralized POMDPsommon set of conditions expectation maximization (EM) for average reward Dec-POMDPs is stuck in a local optimum. We introduce a new average reward EM method; it outperforms a state of the art discounted-reward Dec-POMDP method in experiments.

CYN

Iterative Model Refinement of Recommender MDPs Based on Expert Feedbackhe parameters of the model, under these constraints, by partitioning the parameter space and iteratively applying alternating optimization. We demonstrate how the approach can be applied to both flat and factored MDPs and present results based on diagnostic sessions from a manufacturing scenario.

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古代

Spectral Learning of Sequence Taggers over Continuous Sequences to a class where transitions are linear combinations of elementary transitions and the weights of the linear combination are determined by dynamic features of the continuous input sequence. The resulting learning algorithm is efficient and accurate.

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