31,870 research outputs found
Bayesian Learning of Sum-Product Networks
Sum-product networks (SPNs) are flexible density estimators and have received
significant attention due to their attractive inference properties. While
parameter learning in SPNs is well developed, structure learning leaves
something to be desired: Even though there is a plethora of SPN structure
learners, most of them are somewhat ad-hoc and based on intuition rather than a
clear learning principle. In this paper, we introduce a well-principled
Bayesian framework for SPN structure learning. First, we decompose the problem
into i) laying out a computational graph, and ii) learning the so-called scope
function over the graph. The first is rather unproblematic and akin to neural
network architecture validation. The second represents the effective structure
of the SPN and needs to respect the usual structural constraints in SPN, i.e.
completeness and decomposability. While representing and learning the scope
function is somewhat involved in general, in this paper, we propose a natural
parametrisation for an important and widely used special case of SPNs. These
structural parameters are incorporated into a Bayesian model, such that
simultaneous structure and parameter learning is cast into monolithic Bayesian
posterior inference. In various experiments, our Bayesian SPNs often improve
test likelihoods over greedy SPN learners. Further, since the Bayesian
framework protects against overfitting, we can evaluate hyper-parameters
directly on the Bayesian model score, waiving the need for a separate
validation set, which is especially beneficial in low data regimes. Bayesian
SPNs can be applied to heterogeneous domains and can easily be extended to
nonparametric formulations. Moreover, our Bayesian approach is the first, which
consistently and robustly learns SPN structures under missing data.Comment: NeurIPS 2019; See conference page for supplemen
Causal Confusion in Imitation Learning
Behavioral cloning reduces policy learning to supervised learning by training
a discriminative model to predict expert actions given observations. Such
discriminative models are non-causal: the training procedure is unaware of the
causal structure of the interaction between the expert and the environment. We
point out that ignoring causality is particularly damaging because of the
distributional shift in imitation learning. In particular, it leads to a
counter-intuitive "causal misidentification" phenomenon: access to more
information can yield worse performance. We investigate how this problem
arises, and propose a solution to combat it through targeted
interventions---either environment interaction or expert queries---to determine
the correct causal model. We show that causal misidentification occurs in
several benchmark control domains as well as realistic driving settings, and
validate our solution against DAgger and other baselines and ablations.Comment: Published at NeurIPS 2019 9 pages, plus references and appendice
Mixed membership stochastic blockmodels
Observations consisting of measurements on relationships for pairs of objects
arise in many settings, such as protein interaction and gene regulatory
networks, collections of author-recipient email, and social networks. Analyzing
such data with probabilisic models can be delicate because the simple
exchangeability assumptions underlying many boilerplate models no longer hold.
In this paper, we describe a latent variable model of such data called the
mixed membership stochastic blockmodel. This model extends blockmodels for
relational data to ones which capture mixed membership latent relational
structure, thus providing an object-specific low-dimensional representation. We
develop a general variational inference algorithm for fast approximate
posterior inference. We explore applications to social and protein interaction
networks.Comment: 46 pages, 14 figures, 3 table
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