9 research outputs found
Dynamics of coordinate ascent variational inference: A case study in 2D Ising models
Variational algorithms have gained prominence over the past two decades as a
scalable computational environment for Bayesian inference. In this article, we
explore tools from the dynamical systems literature to study convergence of
coordinate ascent algorithms for mean field variational inference. Focusing on
the Ising model defined on two nodes, we fully characterize the dynamics of the
sequential coordinate ascent algorithm and its parallel version. We observe
that in the regime where the objective function is convex, both the algorithms
are stable and exhibit convergence to the unique fixed point. Our analyses
reveal interesting {\em discordances} between these two versions of the
algorithm in the region when the objective function is non-convex. In fact, the
parallel version exhibits a periodic oscillatory behavior which is absent in
the sequential version. Drawing intuition from the Markov chain Monte Carlo
literature, we {\em empirically} show that a parameter expansion of the Ising
model, popularly called as the Edward--Sokal coupling, leads to an enlargement
of the regime of convergence to the global optima
Local convexity of the TAP free energy and AMP convergence for Z2-synchronization
We study mean-field variational Bayesian inference using the TAP approach,
for Z2-synchronization as a prototypical example of a high-dimensional Bayesian
model. We show that for any signal strength (the weak-recovery
threshold), there exists a unique local minimizer of the TAP free energy
functional near the mean of the Bayes posterior law. Furthermore, the TAP free
energy in a local neighborhood of this minimizer is strongly convex.
Consequently, a natural-gradient/mirror-descent algorithm achieves linear
convergence to this minimizer from a local initialization, which may be
obtained by a finite number of iterates of Approximate Message Passing (AMP).
This provides a rigorous foundation for variational inference in high
dimensions via minimization of the TAP free energy.
We also analyze the finite-sample convergence of AMP, showing that AMP is
asymptotically stable at the TAP minimizer for any , and is
linearly convergent to this minimizer from a spectral initialization for
sufficiently large . Such a guarantee is stronger than results
obtainable by state evolution analyses, which only describe a fixed number of
AMP iterations in the infinite-sample limit.
Our proofs combine the Kac-Rice formula and Sudakov-Fernique Gaussian
comparison inequality to analyze the complexity of critical points that satisfy
strong convexity and stability conditions within their local neighborhoods.Comment: 56 page
Large-scale variational inference for Bayesian joint regression modelling of high-dimensional genetic data
Genetic association studies have become increasingly important in understanding the molecular bases of complex human traits. The specific analysis of intermediate molecular traits, via quantitative trait locus (QTL) studies, has recently received much attention, prompted by the advance of high-throughput technologies for quantifying gene, protein and metabolite levels. Of great interest is the detection of weak trans-regulatory effects between a genetic variant and a distal gene product. In particular, hotspot genetic variants, which remotely control the levels of many molecular outcomes, may initiate decisive functional mechanisms underlying disease endpoints.
This thesis proposes a Bayesian hierarchical approach for joint analysis of QTL data on a genome-wide scale. We consider a series of parallel sparse regressions combined in a hierarchical manner to flexibly accommodate high-dimensional responses (molecular levels) and predictors (genetic variants), and we present new methods for large-scale inference.
Existing approaches have limitations. Conventional marginal screening does not account for local dependencies and association patterns common to multiple outcomes and genetic variants, whereas joint modelling approaches are restricted to relatively small datasets by computational constraints. Our novel framework allows information-sharing across outcomes and variants, thereby enhancing the detection of weak trans and hotspot effects, and implements tailored variational inference procedures that allow simultaneous analysis of data for an entire QTL study, comprising hundreds of thousands of predictors, and thousands of responses and samples.
The present work also describes extensions to leverage spatial and functional information on the genetic variants, for example, using predictor-level covariates such as epigenomic marks. Moreover, we augment variational inference with simulated annealing and parallel expectation-maximisation schemes in order to enhance exploration of highly multimodal spaces and allow efficient empirical Bayes estimation.
Our methods, publicly available as packages implemented in R and C++, are extensively assessed in realistic simulations. Their advantages are illustrated in several QTL applications, including a large-scale proteomic QTL study on two clinical cohorts that highlights novel candidate biomarkers for metabolic disorders