1,708 research outputs found
Storage Codes with Flexible Number of Nodes
This paper presents flexible storage codes, a class of error-correcting codes
that can recover information from a flexible number of storage nodes. As a
result, one can make a better use of the available storage nodes in the
presence of unpredictable node failures and reduce the data access latency. Let
us assume a storage system encodes information symbols over a finite
field into nodes, each of size symbols. The code is
parameterized by a set of tuples ,
satisfying and , such that the information symbols can be reconstructed from any
nodes, each node accessing symbols. In other words, the code
allows a flexible number of nodes for decoding to accommodate the variance in
the data access time of the nodes. Code constructions are presented for
different storage scenarios, including LRC (locally recoverable) codes, PMDS
(partial MDS) codes, and MSR (minimum storage regenerating) codes. We analyze
the latency of accessing information and perform simulations on Amazon clusters
to show the efficiency of presented codes
Residual Weighted Learning for Estimating Individualized Treatment Rules
Personalized medicine has received increasing attention among statisticians,
computer scientists, and clinical practitioners. A major component of
personalized medicine is the estimation of individualized treatment rules
(ITRs). Recently, Zhao et al. (2012) proposed outcome weighted learning (OWL)
to construct ITRs that directly optimize the clinical outcome. Although OWL
opens the door to introducing machine learning techniques to optimal treatment
regimes, it still has some problems in performance. In this article, we propose
a general framework, called Residual Weighted Learning (RWL), to improve finite
sample performance. Unlike OWL which weights misclassification errors by
clinical outcomes, RWL weights these errors by residuals of the outcome from a
regression fit on clinical covariates excluding treatment assignment. We
utilize the smoothed ramp loss function in RWL, and provide a difference of
convex (d.c.) algorithm to solve the corresponding non-convex optimization
problem. By estimating residuals with linear models or generalized linear
models, RWL can effectively deal with different types of outcomes, such as
continuous, binary and count outcomes. We also propose variable selection
methods for linear and nonlinear rules, respectively, to further improve the
performance. We show that the resulting estimator of the treatment rule is
consistent. We further obtain a rate of convergence for the difference between
the expected outcome using the estimated ITR and that of the optimal treatment
rule. The performance of the proposed RWL methods is illustrated in simulation
studies and in an analysis of cystic fibrosis clinical trial data.Comment: 48 pages, 3 figure
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