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Table 1: Static policy evaluation results. evaluator rmse (±95% C.I.) bias DM 0.0151 ± 0.0002 0.0150 RS 0.0191 ± 0.0021 ...

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Table 1: Static policy evaluation results. evaluator rmse (±95% C.I.) bias DM 0.0151 ± 0.0002 0.0150 RS 0.0191 ± 0.0021

# The Author 2010. Published by Oxford University Press. All rights reserved. doi: 10.1093/chinesejil/jmq018 ..........

Data MNIST, N=100 Bayes Bayes PM Wain Min MLE −2.5 20 40 CLL −3 60 Wain min Wain max MLE Bayes p0 −3.5 Wain

find the fewest securities that are informative for some given events of interest but is constrained to select its secur

state of the world lies in the set Πi (ω ∗ ). iting an expectation is said to be proper if a risk neutral forecaster wh

First we implemented the BCCD approach as a (minimal) independence test. Figure 2 shows a typical example in the form of

do not include full proof of this claim. Intuitively it holds because each agent does not receive any information about

As such, the τ -th value function υτ can be built from a (τ + 1)-th value function υτ +1 as follows: υτ (ητ ) υT (ηT )

Algorithm 1 DR-ns(π, {(xk , ak , rk , pk )}, q, cmax ) 1. h0 ← ∅, t ← 1, c1 ← cmax R ← 0, C ← 0, Q ← ∅ 2. For k = 1, 2,X

the hypothesis that the distribution is truly uniform. The χ2 value can be used to test a null hypothesis stating that t

0 . 0 . 0 6 0 . 0 5 5 U U 0 . 4 S 0 . . i e a f o r r c n i f o r m m h T r e e

Sentence   Top   Event   Weights   Top  Sentences   Chelsea looking for a penalty as Malouda's header hits Koscielny, n

The value function for a policy π gives the expected total reward received under that policy starting from a given state

For simplicity, in the below we’ll assume the utilities Ui are bounded, without loss of generality in [0, 1]. MDP M = (

Selecting Computations: Theory and Applications Nicholas Hay and Stuart Russell Computer Science Division University of

the problem that allows the user to tradeoff between the level of conservativeness (or risk) against the total utility.

4.3 Analysis Hence, we must further analyze whether using good approximate inference will lead us to good approximate

We did not compare to BLM [3] or the algorithm of Della Pietra et al. [4], since those algorithms have been shown to be

Sparse Q-learning with Mirror Descent Sridhar Mahadevan and Bo Liu Computer Science Department University of Massachuse

where L(a, b) is a convex loss function, and β is a sparsitycontrolling parameter. The simplest online convex algorithm

ized by ρi . Our objective is to recover the set of transformation parameters {ρi }N i=1 , such that the aligned data se

By communicating its local summary to every other sensor, each mobile sensor can then construct a globally consistent su

A closed form solution to eq.(1) is used in algorithms such as K2 (Cooper and Herskovits, 1992) and the Greedy Equivalen

3.1 Additional Assumptions occupancy ητ −1 and decentralized Markov decision rule P 0 στ −1 . That is, ητ (s0 ) = p(s,