By Luc De Raedt (auth.), Jean-Gabriel Ganascia, Philippe Lenca, Jean-Marc Petit (eds.)

This ebook constitutes the refereed lawsuits of the fifteenth overseas convention on Discovery technology, DS 2012, held in Lyon, France, in October 2012. The 22 papers offered during this quantity have been rigorously reviewed and chosen from forty six submissions. the sector of discovery technology goals at inducing and validating new medical hypotheses from info. The scope of this convention comprises the improvement and research of equipment for computerized clinical wisdom discovery, computer studying, clever facts research, conception of studying, instruments for helping the human means of discovery in technological know-how, in addition to their program to wisdom discovery.

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The 2 quantity set LNCS 7431 and 7432 constitutes the refereed lawsuits of the eighth overseas Symposium on visible Computing, ISVC 2012, held in Rethymnon, Crete, Greece, in July 2012. The sixty eight revised complete papers and 35 poster papers provided including forty five designated song papers have been rigorously reviewed and chosen from greater than two hundred submissions.

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For a complete proof in the general case, we refer the reader for example to [25,13]. We are now ready to state the following key result. Lemma 4. Let us assume that Assumptions LipLoss, WeakDep and Lip are satisfied. Let us fix λ > 0. Let k be defined as in Lemma 2. Then, ⎫ ⎧ ∀ρ ∈ M1+ (Θ), ⎪ ⎪ ⎪ ⎪ ⎪ 2 K(ρ,π)+log( 2ε ) ⎪ ⎪ ⎪ ⎪ ⎪ ⎬ ⎨ Rdρ ≤ rn dρ + λκ k 2 + λ n(1− n ) ≥ 1 − ε. (4) P and ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 2 K(ρ,π)+log( 2ε ) ⎪ ⎪ ⎪ ⎭ ⎩ rn dρ ≤ Rdρ + λκ k 2 + λ n(1− n ) Prediction of Quantiles and GDP Forecasting 33 Proof of Lemma 4.

What RL algorithms do to overcome this diﬃculty is to consider a subset of policies from Π that can be compactly represented, such as parametric policies or value function-based policies. In this paper, we additionally expect the policies from such a subset to be interpretable by humans. We use the ideas of Kolmogorov complexity theory to express the interpretability of a policy π relative to a given description language. We say that a policy is interpretable, in the selected description language, if it can be described in this language by using few symbols.

1. 5 by θˆ∗ . Interestingly, both are quite similar but θB,λ is a bit more precise. We remind that mean abs. pred. error = mean quad. pred. 5 [t] (Xt−1 , Xt−2 ) [t] (Xt−1 , Xt−2 ) B,λ(t) 2 . 30 P. Alquier and X. Li Fig. 2. 08178 θ We also report the frequency of realizations of the GDP falling above the predicted τ -quantile for each τ . Note that this quantity should be close to τ . 978 θB,λ As the INSEE did, we miss the value of the 2008 crisis. However, it is interesting to note that our conﬁdence interval shows that our prediction at this date is less reliable than the previous ones: so, at this time, the forecaster could have been aware of some problems in their predictions.