159 research outputs found
Hashing-Based Approximate Probabilistic Inference in Hybrid Domains
In recent years, there has been considerable progress on fast randomized algorithms that ap-proximate probabilistic inference with tight toler-ance and confidence guarantees. The idea here is to formulate inference as a counting task over an annotated propositional theory, called weighted model counting (WMC), which can be parti-tioned into smaller tasks using universal hashing. An inherent limitation of this approach, how-ever, is that it only admits the inference of dis-crete probability distributions. In this work, we consider the problem of approximating inference tasks for a probability distribution defined over discrete and continuous random variables. Build-ing on a notion called weighted model integra-tion, which is a strict generalization of WMC and is based on annotating Boolean and arithmetic constraints, we show how probabilistic inference in hybrid domains can be put within reach of hashing-based WMC solvers. Empirical evalu-ations demonstrate the applicability and promise of the proposal.
Causal Abstraction with Soft Interventions
Causal abstraction provides a theory describing how several causal models can
represent the same system at different levels of detail. Existing theoretical
proposals limit the analysis of abstract models to "hard" interventions fixing
causal variables to be constant values. In this work, we extend causal
abstraction to "soft" interventions, which assign possibly non-constant
functions to variables without adding new causal connections. Specifically, (i)
we generalize -abstraction from Beckers and Halpern (2019) to soft
interventions, (ii) we propose a further definition of soft abstraction to
ensure a unique map between soft interventions, and (iii) we prove
that our constructive definition of soft abstraction guarantees the
intervention map has a specific and necessary explicit form
Learning Likelihoods with Conditional Normalizing Flows
Normalizing Flows (NFs) are able to model complicated distributions p(y) with
strong inter-dimensional correlations and high multimodality by transforming a
simple base density p(z) through an invertible neural network under the change
of variables formula. Such behavior is desirable in multivariate structured
prediction tasks, where handcrafted per-pixel loss-based methods inadequately
capture strong correlations between output dimensions. We present a study of
conditional normalizing flows (CNFs), a class of NFs where the base density to
output space mapping is conditioned on an input x, to model conditional
densities p(y|x). CNFs are efficient in sampling and inference, they can be
trained with a likelihood-based objective, and CNFs, being generative flows, do
not suffer from mode collapse or training instabilities. We provide an
effective method to train continuous CNFs for binary problems and in
particular, we apply these CNFs to super-resolution and vessel segmentation
tasks demonstrating competitive performance on standard benchmark datasets in
terms of likelihood and conventional metrics.Comment: 18 pages, 8 Tables, 9 Figures, Preprin
- …