22,000 research outputs found
High-Dimensional Screening Using Multiple Grouping of Variables
Screening is the problem of finding a superset of the set of non-zero entries
in an unknown p-dimensional vector \beta* given n noisy observations.
Naturally, we want this superset to be as small as possible. We propose a novel
framework for screening, which we refer to as Multiple Grouping (MuG), that
groups variables, performs variable selection over the groups, and repeats this
process multiple number of times to estimate a sequence of sets that contains
the non-zero entries in \beta*. Screening is done by taking an intersection of
all these estimated sets. The MuG framework can be used in conjunction with any
group based variable selection algorithm. In the high-dimensional setting,
where p >> n, we show that when MuG is used with the group Lasso estimator,
screening can be consistently performed without using any tuning parameter. Our
numerical simulations clearly show the merits of using the MuG framework in
practice.Comment: This paper will appear in the IEEE Transactions on Signal Processing.
See http://www.ima.umn.edu/~dvats/MuGScreening.html for more detail
Simulation-Based Inference for Global Health Decisions
The COVID-19 pandemic has highlighted the importance of in-silico
epidemiological modelling in predicting the dynamics of infectious diseases to
inform health policy and decision makers about suitable prevention and
containment strategies. Work in this setting involves solving challenging
inference and control problems in individual-based models of ever increasing
complexity. Here we discuss recent breakthroughs in machine learning,
specifically in simulation-based inference, and explore its potential as a
novel venue for model calibration to support the design and evaluation of
public health interventions. To further stimulate research, we are developing
software interfaces that turn two cornerstone COVID-19 and malaria epidemiology
models COVID-sim, (https://github.com/mrc-ide/covid-sim/) and OpenMalaria
(https://github.com/SwissTPH/openmalaria) into probabilistic programs, enabling
efficient interpretable Bayesian inference within those simulators
- …