4,409 research outputs found
A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling
Randomized experiments have been critical tools of decision making for
decades. However, subjects can show significant heterogeneity in response to
treatments in many important applications. Therefore it is not enough to simply
know which treatment is optimal for the entire population. What we need is a
model that correctly customize treatment assignment base on subject
characteristics. The problem of constructing such models from randomized
experiments data is known as Uplift Modeling in the literature. Many algorithms
have been proposed for uplift modeling and some have generated promising
results on various data sets. Yet little is known about the theoretical
properties of these algorithms. In this paper, we propose a new tree-based
ensemble algorithm for uplift modeling. Experiments show that our algorithm can
achieve competitive results on both synthetic and industry-provided data. In
addition, by properly tuning the "node size" parameter, our algorithm is proved
to be consistent under mild regularity conditions. This is the first consistent
algorithm for uplift modeling that we are aware of.Comment: Accepted by 2017 IEEE International Conference on Data Minin
Altimetry, gravimetry, GPS and viscoelastic modeling data for the joint inversion for glacial isostatic adjustment in Antarctica (ESA STSE Project REGINA)
The poorly known correction for the ongoing deformation of the solid Earth caused by glacial isostatic adjustment (GIA) is a major uncertainty in determining the mass balance of the Antarctic ice sheet from measurements of satellite gravimetry and to a lesser extent satellite altimetry. In the past decade, much progress has been made in consistently modeling ice sheet and solid Earth interactions; however, forward-modeling solutions of GIA in Antarctica remain uncertain due to the sparsity of constraints on the ice sheet evolution, as well as the Earth's rheological properties. An alternative approach towards estimating GIA is the joint inversion of multiple satellite data â namely, satellite gravimetry, satellite altimetry and GPS, which reflect, with different sensitivities, trends in recent glacial changes and GIA. Crucial to the success of this approach is the accuracy of the space-geodetic data sets. Here, we present reprocessed rates of surface-ice elevation change (Envisat/Ice, Cloud,and land Elevation Satellite, ICESat; 2003â2009), gravity field change (Gravity Recovery and Climate Experiment, GRACE; 2003â2009) and bedrock uplift (GPS; 1995â2013). The data analysis is complemented by the forward modeling of viscoelastic response functions to disc load forcing, allowing us to relate GIA-induced surface displacements with gravity changes for different rheological parameters of the solid Earth. The data and modeling results presented here are available in the PANGAEA database (https://doi.org/10.1594/PANGAEA.875745). The data sets are the input streams for the joint inversion estimate of present-day ice-mass change and GIA, focusing on Antarctica. However, the methods, code and data provided in this paper can be used to solve other problems, such as volume balances of the Antarctic ice sheet, or can be applied to other geographical regions in the case of the viscoelastic response functions. This paper presents the first of two contributions summarizing the work carried out within a European Space Agency funded study: Regional glacial isostatic adjustment and CryoSat elevation rate corrections in Antarctica (REGINA)
Uplift Modeling with Multiple Treatments and General Response Types
Randomized experiments have been used to assist decision-making in many
areas. They help people select the optimal treatment for the test population
with certain statistical guarantee. However, subjects can show significant
heterogeneity in response to treatments. The problem of customizing treatment
assignment based on subject characteristics is known as uplift modeling,
differential response analysis, or personalized treatment learning in
literature. A key feature for uplift modeling is that the data is unlabeled. It
is impossible to know whether the chosen treatment is optimal for an individual
subject because response under alternative treatments is unobserved. This
presents a challenge to both the training and the evaluation of uplift models.
In this paper we describe how to obtain an unbiased estimate of the key
performance metric of an uplift model, the expected response. We present a new
uplift algorithm which creates a forest of randomized trees. The trees are
built with a splitting criterion designed to directly optimize their uplift
performance based on the proposed evaluation method. Both the evaluation method
and the algorithm apply to arbitrary number of treatments and general response
types. Experimental results on synthetic data and industry-provided data show
that our algorithm leads to significant performance improvement over other
applicable methods
Supersymmetric Vacua in Random Supergravity
We determine the spectrum of scalar masses in a supersymmetric vacuum of a
general N=1 supergravity theory, with the Kahler potential and superpotential
taken to be random functions of N complex scalar fields. We derive a random
matrix model for the Hessian matrix and compute the eigenvalue spectrum.
Tachyons consistent with the Breitenlohner-Freedman bound are generically
present, and although these tachyons cannot destabilize the supersymmetric
vacuum, they do influence the likelihood of the existence of an `uplift' to a
metastable vacuum with positive cosmological constant. We show that the
probability that a supersymmetric AdS vacuum has no tachyons is formally
equivalent to the probability of a large fluctuation of the smallest eigenvalue
of a certain real Wishart matrix. For normally-distributed matrix entries and
any N, this probability is given exactly by P = exp(-2N^2|W|^2/m_{susy}^2),
with W denoting the superpotential and m_{susy} the supersymmetric mass scale;
for more general distributions of the entries, our result is accurate when N >>
1. We conclude that for |W| \gtrsim m_{susy}/N, tachyonic instabilities are
ubiquitous in configurations obtained by uplifting supersymmetric vacua.Comment: 26 pages, 6 figure
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Recommendations can greatly benefit from good representations of the user
state at recommendation time. Recent approaches that leverage Recurrent Neural
Networks (RNNs) for session-based recommendations have shown that Deep Learning
models can provide useful user representations for recommendation. However,
current RNN modeling approaches summarize the user state by only taking into
account the sequence of items that the user has interacted with in the past,
without taking into account other essential types of context information such
as the associated types of user-item interactions, the time gaps between events
and the time of day for each interaction. To address this, we propose a new
class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that
can take into account the contextual information both in the input and output
layers and modifying the behavior of the RNN by combining the context embedding
with the item embedding and more explicitly, in the model dynamics, by
parametrizing the hidden unit transitions as a function of context information.
We compare our CRNNs approach with RNNs and non-sequential baselines and show
good improvements on the next event prediction task
Exhumation, crustal deformation, and thermal structure of the Nepal Himalaya derived from the inversion of thermochronological and thermobarometric data and modeling of the topography
Two endâmember kinematic models of crustal shortening across the Himalaya are
currently debated: one assumes localized thrusting along a single major thrust fault, the
Main Himalayan Thrust (MHT) with nonuniform underplating due to duplexing, and the
other advocates for outâofâsequence (OOS) thrusting in addition to thrusting along the
MHT and underplating. We assess these two models based on the modeling of
thermochronological, thermometric, and thermobarometric data from the central Nepal
Himalaya. We complement a data set compiled from the literature with 114 ^(40)Ar/^(39)Ar,
10 apatite fission track, and 5 zircon (UâTh)/He thermochronological data. The data are
predicted using a thermokinematic model (PECUBE), and the model parameters are
constrained using an inverse approach based on the Neighborhood Algorithm. The model
parameters include geometric characteristics as well as overthrusting rates, radiogenic heat
production in the High Himalayan Crystalline (HHC) sequence, the age of initiation of
the duplex or of out-of-sequence thrusting. Both models can provide a satisfactory fit to the
inverted data. However, the model with out-of-sequence thrusting implies an unrealistic
convergence rate â„30 mm yr^(â1). The out-of-sequence thrust model can be adjusted to fit the
convergence rate and the thermochronological data if the Main Central Thrust zone is
assigned a constant geometry and a dip angle of about 30° and a slip rate of <1 mm yr^(â1). In
the duplex model, the 20 mm yr^(â1) convergence rate is partitioned between an overthrusting
rate of 5.8 ± 1.4 mm yr^(â1) and an underthrusting rate of 14.2 ± 1.8 mm yr^(â1). Modern rock
uplift rates are estimated to increase from about 0.9 ± 0.31 mm yr^(â1) in the Lesser Himalaya to
3.0 ± 0.9 mm yr^(â1) at the front of the high range, 86 ± 13 km from the Main Frontal Thrust.
The effective friction coefficient is estimated to be 0.07 or smaller, and the radiogenic
heat production of HHC units is estimated to be 2.2 ± 0.1 ”Wm^(â3). The midcrustal
duplex initiated at 9.8 ± 1.7 Ma, leading to an increase of uplift rate at front of the High
Himalaya from 0.9 ± 0.31 to 3.05 ± 0.9 mm yr^(â1). We also run 3-D models by coupling
PECUBE with a landscape evolution model (CASCADE). This modeling shows that the
effect of the evolving topography can explain a fraction of the scatter observed in the data but
not all of it, suggesting that lateral variations of the kinematics of crustal deformation and
exhumation are likely. It has been argued that the steep physiographic transition at the foot of
the Greater Himalayan Sequence indicates OOS thrusting, but our results demonstrate
that the best fit duplex model derived from the thermochronological and thermobarometric
data reproduces the present morphology of the Nepal Himalaya equally well
Joint inversion estimate of regional glacial isostatic adjustment in Antarctica considering a lateral varying Earth structure (ESA STSE Project REGINA)
A major uncertainty in determining the mass balance of the Antarctic ice sheet from measurements of satellite gravimetry, and
to a lesser extent satellite altimetry, is the poorly known correction for the ongoing deformation of the solid Earth caused by glacial isostatic adjustment (GIA). Although much progress has been made in consistently modelling the ice-sheet evolution throughout the last glacial cycle, as well as the induced bedrock deformation caused by these load changes, forward models of GIA remain ambiguous due to the lack of observational constraints on the ice sheet's past extent and thickness and mantle rheology beneath the continent. As an alternative to forward modelling GIA, we estimate GIA from multiple space-geodetic observations: GRACE, Envisat/ICESat and GPS. Making use of the different sensitivities of the respective satellite observations to current and past surface mass (ice mass) change and solid Earth processes, we estimate GIA based on viscoelastic response functions to disc load forcing. We calculate and distribute the viscoelastic response functions according to estimates of the variability of lithosphere thickness and mantle viscosity in Antarctica. We compare our GIA estimate with published GIA corrections and evaluate its impact in determining the ice mass balance in Antarctica from GRACE and satellite altimetry. Particular focus is applied to the Amundsen Sea Sector in West Antarctica, where uplift rates of several cm/yr have been measured by GPS. We show that most of this uplift is caused by the rapid viscoelastic response to recent ice-load changes, enabled by the presence of a low-viscosity upper mantle in West Antarctica. This paper presents the second and final contribution summarizing the work carried out within a European Space Agency funded study, REGINA, (www.regina-science.eu)
Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter
We consider the problem of conditioning a geological process-based computer
simulation, which produces basin models by simulating transport and deposition
of sediments, to data. Emphasising uncertainty quantification, we frame this as
a Bayesian inverse problem, and propose to characterize the posterior
probability distribution of the geological quantities of interest by using a
variant of the ensemble Kalman filter, an estimation method which linearly and
sequentially conditions realisations of the system state to data.
A test case involving synthetic data is used to assess the performance of the
proposed estimation method, and to compare it with similar approaches. We
further apply the method to a more realistic test case, involving real well
data from the Colville foreland basin, North Slope, Alaska.Comment: 34 pages, 10 figures, 4 table
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