6,720 research outputs found
Automatic Differentiation Variational Inference
Probabilistic modeling is iterative. A scientist posits a simple model, fits
it to her data, refines it according to her analysis, and repeats. However,
fitting complex models to large data is a bottleneck in this process. Deriving
algorithms for new models can be both mathematically and computationally
challenging, which makes it difficult to efficiently cycle through the steps.
To this end, we develop automatic differentiation variational inference (ADVI).
Using our method, the scientist only provides a probabilistic model and a
dataset, nothing else. ADVI automatically derives an efficient variational
inference algorithm, freeing the scientist to refine and explore many models.
ADVI supports a broad class of models-no conjugacy assumptions are required. We
study ADVI across ten different models and apply it to a dataset with millions
of observations. ADVI is integrated into Stan, a probabilistic programming
system; it is available for immediate use
A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
The benefits of automating design cycles for Bayesian inference-based
algorithms are becoming increasingly recognized by the machine learning
community. As a result, interest in probabilistic programming frameworks has
much increased over the past few years. This paper explores a specific
probabilistic programming paradigm, namely message passing in Forney-style
factor graphs (FFGs), in the context of automated design of efficient Bayesian
signal processing algorithms. To this end, we developed "ForneyLab"
(https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message
passing-based inference in FFGs. We show by example how ForneyLab enables
automatic derivation of Bayesian signal processing algorithms, including
algorithms for parameter estimation and model comparison. Crucially, due to the
modular makeup of the FFG framework, both the model specification and inference
methods are readily extensible in ForneyLab. In order to test this framework,
we compared variational message passing as implemented by ForneyLab with
automatic differentiation variational inference (ADVI) and Monte Carlo methods
as implemented by state-of-the-art tools "Edward" and "Stan". In terms of
performance, extensibility and stability issues, ForneyLab appears to enjoy an
edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate
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OBOE: Collaborative Filtering for AutoML Model Selection
Algorithm selection and hyperparameter tuning remain two of the most
challenging tasks in machine learning. Automated machine learning (AutoML)
seeks to automate these tasks to enable widespread use of machine learning by
non-experts. This paper introduces OBOE, a collaborative filtering method for
time-constrained model selection and hyperparameter tuning. OBOE forms a matrix
of the cross-validated errors of a large number of supervised learning models
(algorithms together with hyperparameters) on a large number of datasets, and
fits a low rank model to learn the low-dimensional feature vectors for the
models and datasets that best predict the cross-validated errors. To find
promising models for a new dataset, OBOE runs a set of fast but informative
algorithms on the new dataset and uses their cross-validated errors to infer
the feature vector for the new dataset. OBOE can find good models under
constraints on the number of models fit or the total time budget. To this end,
this paper develops a new heuristic for active learning in time-constrained
matrix completion based on optimal experiment design. Our experiments
demonstrate that OBOE delivers state-of-the-art performance faster than
competing approaches on a test bed of supervised learning problems. Moreover,
the success of the bilinear model used by OBOE suggests that AutoML may be
simpler than was previously understood
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