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Generalization error for decision problems
[article]

2018
*
arXiv
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pre-print

Define the estimated conditional CDF of U n given β n as follows F Un,n v β = P (

arXiv:1812.08696v1
fatcat:b3yhwfll7zdbjh3hwuswtjsaqe
*b*) n K*B*−1 ( β (*b*) n − β) 1 U (*b*) n ≤ v P (*b*) n K*B*−1 ( β (*b*) n − β) , subsequently define u n (β) = F −1 Un,n (1 − α/ ... and Murphy, 2011; Hirano and Porter, 2012;*Laber*et al., 2014) . ...##
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Interpretable Dynamic Treatment Regimes
[article]

2016
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arXiv
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pre-print

*Laber*, E.

*B*., K. A. Linn, and L. A. Stefanski (2014). Interactive model building for Qlearning. Biometrika 101 (4), 831-847.

*Laber*, E.

*B*. and Y. Q. Zhao (2015). ...

*Laber*, and M. Davidian (2013). Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions. Biometrika 100 (3), 681-694. Zhang, Y., E.

*B*.

*Laber*, A. Tsiatis, and M. ... For any f ∈ F δ , we have f ∞ ≤

*B*and E f 2 ≤

*B*2 δ. Thus, by Propositions 1 and 3, where c 2 is some constant that depends on

*B*. ...

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Interactive Q-Learning for Quantiles

2017
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Figshare
*

the main paper, the data are generated using the model X 1 ∼ Norm(1 2 , Σ), A 1 , A 2 ∼ Unif{−1, 1} 2 , H 1 = (1, X 1 ) , η H 1 ,A 1 = exp{ C 2 (H 1 γ 0 + A 1 H 1 γ 1 )}, ξ ∼ Norm(0 2 , I 2 ), X 2 =

doi:10.6084/m9.figshare.3121897.v2
fatcat:kd726axteza2dnb52nykm6mrhi
*B*... The 2 × 2 matrix*B*A 1 equals The remaining parameters are γ 0 = (1, 0.5, 0) , γ 1 = (−1, −0.5, 0) , β 2,0 = (0.25, −1, 0.5) , and β 2,1 = (1, −0.5, −0.25) , which were chosen to ensure that the mean-optimal ...##
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A spatiotemporal recommendation engine for malaria control
[article]

2020
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arXiv
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pre-print

*Laber*et al. (2018) developed a spatiotemporal treatment allocation strategy to slow the spread of white nose syndrome in bats. ... We assume at each time t and each health zone l that η lt − η lt−1 = c 0 +

*b*0 A lt + (c 1 +

*b*1 A lt )η lt−1 + (c 2 +

*b*2 A lt ) 1 m l j∈I l η jt−1 + p k=1 β 1k X kl + p k=1 β 2k X kl A lt + lt , (2) ...

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Thompson Sampling for Pursuit-Evasion Problems
[article]

2018
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arXiv
*
pre-print

Let

arXiv:1811.04471v1
fatcat:xz4dlw2djzcr3gsp7s74rylqb4
*B*L denote the set of distributions over L. A strategy, π E , is an infinite sequence of functions π E,t : dom S t →*B*L such that π E,t (S t ) has support only on the neighbors of E t . ... Similarly, let*B*L K denote the space of distributions over L K and define a strategy, π W , for the pursuers to be a sequence of maps π W,t : dom H t →*B*L K such that π W,t (H t ) has support only on ...##
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Comment

2016
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Journal of the American Statistical Association
*

For binary covariates (e.g., treatment indicators) we take

doi:10.1080/01621459.2016.1200911
pmid:28003709
pmcid:PMC5167518
fatcat:qtoifjrgwnbnjhldgde4xvjiri
*b*l (x l ) = x l β l ; for continuous covariates we model*b*l using M*b*-spline basis functions, , where ψ 1 , …, ψ M are a fixed*b*-splines basis ... Patients who achieved C and subsequently suffered disease progression (P), were given salvage treatment Z 2,2 ∈ {*b*21 ,*b*22 }, using p(Z 2,2 =*b*21 |L i ) = 0.2(L i < 100) + 0.85I(L i ≥ 100). ...##
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Note on Thompson sampling for large decision problems
[article]

2019
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arXiv
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pre-print

ACKNOWLEDGEMENTS

arXiv:1905.04735v1
fatcat:7rx6wxgl25ekbawcz6s7hghuli
*Eric**Laber*acknowledges support from the National Science Foundation (DMS-1555141, DMS-1557733, DMS-1513579) and the National Institutes of Health (1R01 AA023187-01A1, P01 CA142538). ... ., 2011;*Laber*et al., 2016) , and adaptive management of natural resources wherein management decisions are adjusted over time according to current and forecasted resource availability (McCarthy et al ...##
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Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes
[article]

2015
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arXiv
*
pre-print

., 2013;

arXiv:1504.07715v1
fatcat:z4mgtjjsajaplbwwnzd52p426i
*Laber*et al., 2014; Taylor et al., 2014) . ... Then we code the observed values x 1j , . . . , x nj into indices*b*1j , . . . ,*b*nj according to which interval they fall. ...##
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An imputation method for estimating the learning curve in classification problems
[article]

2012
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arXiv
*
pre-print

More specifically, using β * and p X (x) one could generate

arXiv:1203.2879v1
fatcat:zl6dezdv25hd5jc6gf56lndhbq
*B*training sets D (1) m , D (2) τ (m) ≈ 1 BN*B**b*=1 (X,Y )∈D * π(X; β * ) · 1{X β (*b*) m ≤ κ} + (1 − π(X; β * )) · 1{X β (*b*) m > κ} ≈ 1 BN*B**b*... Using (4), the IMPINT estimator is given bŷ τ II (m) 1 BN*B**b*=1 (X,Y )∈D * Y · 1{X β (*b*) m ≤ κ} + (1 − Y ) · 1{X β (*b*) m > κ} , (5) whereβ (*b*) m =β (*b*) m (D (*b*) m ) denotes the maximum likelihood estimator ...##
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Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals
[article]

2018
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arXiv
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pre-print

, Σ

arXiv:1810.04338v1
fatcat:o5cq5asdtveijg7v34pmbucfvm
*b*) , and 1 ∆ l (·) is the indicator function with a point mass at ∆ l . ... ., 012a,*b*; Zhao et al., 2012; Zhang et al., 2013; Zhao et al., 2015; Kosorok and Moodie, 2015; Guan et al., 2016; Zhou and Kosorok, 2017 ). ...##
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iqLearn: Interactive Q-Learning inR

2015
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Journal of Statistical Software
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For a list of SMARTs that have finished or are in the field, see The Methodology Center At Pennsylvania State University (2012) and

doi:10.18637/jss.v064.i01
pmid:26900385
pmcid:PMC4760113
fatcat:p6k6gp7hufblpacctkzuyudsfa
*Eric*Laber's current list*Laber*(2013) . ... Based on empirical experiments (see*Laber*et al., 2013) , we recommend choosing the empirical estimator by default, as not much is lost when the true density is normal. ...##
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Set-valued dynamic treatment regimes for competing outcomes

2014
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Biometrics
*

True preferences' must be expressible as convex combinations Patient preferences cannot change over time For simplicity, we consider the two-stage binary treatment setting, this is not essential (see

doi:10.1111/biom.12132
pmid:24400912
pmcid:PMC3954452
fatcat:lchh4ajdy5fmpbcmm3qqwbubuy
*Laber*...##
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Interactive Q-learning for Probabilities and Quantiles
[article]

2015
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arXiv
*
pre-print

The 2 × 2 matrix

arXiv:1407.3414v2
fatcat:m77s4dplj5dgbbfo5vbqxroxjq
*B*A 1 equals*B*A 1 =1 = −0.1 −0.1 0.1 0.1 ,*B*A 1 =−1 = 0.5, −0.1 −0.1 0.5 . ... If f (y * τ ) = y * τ , π QIQ 1,τ (h 1 ) = Γ(h 1 , y * τ ) is optimal as it attains the quantile y * τ .*b*. If f (y * τ ) < y * τ , lim δ↓0 Γ(h 1 , y * τ − δ) is optimal. ...##
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Set-valued dynamic treatment regimes for competing outcomes
[article]

2012
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arXiv
*
pre-print

*B*.2 CATIE Note that at Phase 1 the regression estimators are non-regular, and that inference in this setting requires additional care as many standard methods are not valid . ... (

*b*) Estimate the parameters indexing the working model for the stage-1 Q-function using least squares. That is, regressỸ on H 1 and A 1 using the working model to obtainβ 1 andψ 2 . ...

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A Robust Method for Estimating Optimal Treatment Regimes

2012
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Biometrics
*

M SM.I.P k0 is the MSMM method using (

doi:10.1111/j.1541-0420.2012.01763.x
pmid:22550953
pmcid:PMC3556998
fatcat:sanm4vou2nbyldk6xr6cr2rlqi
*B*.2)-(*B*.3) with polynomial of degree k0. M SM.AI.P k0 is the MSMM method using (*B*.4)-(*B*.5) with polynomial of degree k0. ... Thus, in this scenario, all MSMMs of this form are incorrectly specified.To implement the MSMM method using (*B*.2)-(*B*.3) and (*B*.4)-(*B*.5), we considered the sequence η j from -2 to 6 with step 0.05 and put ...
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