1,178 research outputs found
Sampling Online Social Networks via Heterogeneous Statistics
Most sampling techniques for online social networks (OSNs) are based on a
particular sampling method on a single graph, which is referred to as a
statistics. However, various realizing methods on different graphs could
possibly be used in the same OSN, and they may lead to different sampling
efficiencies, i.e., asymptotic variances. To utilize multiple statistics for
accurate measurements, we formulate a mixture sampling problem, through which
we construct a mixture unbiased estimator which minimizes asymptotic variance.
Given fixed sampling budgets for different statistics, we derive the optimal
weights to combine the individual estimators; given fixed total budget, we show
that a greedy allocation towards the most efficient statistics is optimal. In
practice, the sampling efficiencies of statistics can be quite different for
various targets and are unknown before sampling. To solve this problem, we
design a two-stage framework which adaptively spends a partial budget to test
different statistics and allocates the remaining budget to the inferred best
statistics. We show that our two-stage framework is a generalization of 1)
randomly choosing a statistics and 2) evenly allocating the total budget among
all available statistics, and our adaptive algorithm achieves higher efficiency
than these benchmark strategies in theory and experiment
A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation
Kidney transplantation is the preferred treatment for people suffering from
end-stage renal disease. Successful kidney transplants still fail over time,
known as graft failure; however, the time to graft failure, or graft survival
time, can vary significantly between different recipients. A significant
biological factor affecting graft survival times is the compatibility between
the human leukocyte antigens (HLAs) of the donor and recipient. We propose to
model HLA compatibility using a network, where the nodes denote different HLAs
of the donor and recipient, and edge weights denote compatibilities of the
HLAs, which can be positive or negative. The network is indirectly observed, as
the edge weights are estimated from transplant outcomes rather than directly
observed. We propose a latent space model for such indirectly-observed weighted
and signed networks. We demonstrate that our latent space model can not only
result in more accurate estimates of HLA compatibilities, but can also be
incorporated into survival analysis models to improve accuracy for the
downstream task of predicting graft survival times.Comment: This work has been accepted to BIBM 202
Solving Inverse Problems with Reinforcement Learning
In this paper, we formally introduce, with rigorous derivations, the use of
reinforcement learning to the field of inverse problems by designing an
iterative algorithm, called REINFORCE-IP, for solving a general type of
non-linear inverse problem. By choosing specific probability models for the
action-selection rule, we connect our approach to the conventional
regularization methods of Tikhonov regularization and iterative regularization.
For the numerical implementation of our approach, we parameterize the
solution-searching rule with the help of neural networks and iteratively
improve the parameter using a reinforcement-learning algorithm~-- REINFORCE.
Under standard assumptions we prove the almost sure convergence of the
parameter to a locally optimal value. Our work provides two typical examples
(non-linear integral equations and parameter-identification problems in partial
differential equations) of how reinforcement learning can be applied in solving
non-linear inverse problems. Our numerical experiments show that REINFORCE-IP
is an efficient algorithm that can escape from local minimums and identify
multi-solutions for inverse problems with non-uniqueness.Comment: 33 pages, 10 figure
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