arXiv research

Study proposes an RBM with multivalued hidden variables to improve generalization.

Defines finite type Multivalued Shape using hyperspaces.

We outline a cohomological treatment for multivalued (classical) action functionals. We point out that an application of Takens' theorem, after Zuckerman, Deligne and Freed, allows to conclude that multivalued functionals yield globally defined variational equations.

Paper constructs multivalued harmonic functions on R^3 using twistor methods.

Bayesian method models multivalued power data from wind farms.

Harmonic functions stable under small changes.

We extend the work of Simon and Wickramasekera, who constructed a large class of $C^{1,μ}$ multivalued solutions to the minimal surface equation, to produce $C^{1,μ}$ multivalued solutions to more general classes of elliptic equations and systems, including the minimal surface system with small boundary data and the La…

This article is a survey of the Novikov problem of the structure of leaves of the foliations induced by a collection of closed 1-forms in a compact manifold $M$. Equivalently, this is to the study of the level sets of multivalued functions on $M$. To date, this problem was thoroughly investigated only for $M=\Bbb T^n$ …

This survey covers in our opinion the most important results in the theory of continuous selections of multivalued mappings (approximately) from 2002 through 2012. It extends and continues our previous such survey which appeared in Recent Progress in General Topology, II, which was published in 2002. In comparison, our…

A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Detecting hidden variables poses two problems: determining the relations to other variables in the model and determining the number of states of …

Two accelerated extragradient methods converge at $O(1/k)$ rate for co-hypomonotone inclusions.

Paper introduces MLRH, a probabilistic model for multilabel classification.

Framework for universal graph function approximators outperforms existing methods.

This work presents entropic constraints from DAGs with hidden variables.

New methods for estimating causal effects in hidden variable DAGs.

We introduce the problem of reconstructing a sequence of multidimensional real vectors where some of the data are missing. This problem contains regression and mapping inversion as particular cases where the pattern of missing data is independent of the sequence index. The problem is hard because it involves possibly m…

We provide a classification of graphical models according to their representation as subfamilies of exponential families. Undirected graphical models with no hidden variables are linear exponential families (LEFs), directed acyclic graphical models and chain graphs with no hidden variables, including Bayesian networks …

This paper extends stable blanket theory to models with hidden variables and causal cycles.

Study optimal adjustment sets for causal policies with hidden variables.

We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select models given large samples of data. The standard BIC as well as our extension punishes the complexity of a model according to …

In this work a local inequality is provided which bounds the distance of an integral varifold from a multivalued plane (height) by its tilt and mean curvature. The bounds obtained for the exponents of the Lebesgue spaces involved are shown to be sharp.

Paper tackles causal effect estimation in observational data with hidden variables.

In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive procedure, which limits most applications to parameter learning. This problem is even more acute when learning networks with hidden variables. We p…

Estimates effects of multiple interventions with hidden confounders using single-variable interventions.

The paper introduces an inference algorithm for graded Bayesian networks.

Identifying causal direction in location-scale noise models with hidden variables

EDML is a recently proposed algorithm for learning MAP parameters in Bayesian networks. In this paper, we present a number of new advances and insights on the EDML algorithm. First, we provide the multivalued extension of EDML, originally proposed for Bayesian networks over binary variables. Next, we identify a simplif…

We first describe the local and global moduli spaces of germs of foliations defined by analytic functions in two variables with p transverse smooth branches, and with integral multiplicities (in the univalued holomorphic case) or complex multiplicities (in the multivalued ''Darboux'' case). We specify normal forms in e…

End-to-end speaker recognition method using neural networks.

Polynomial delay algorithm tests causal models with hidden variables.

This work restricts hidden cardinality in causal models to infer causal relations.

Many widely studied graphical models with latent variables lead to nontrivial constraints on the distribution of the observed variables. Inspired by the Bell inequalities in quantum mechanics, we refer to any linear inequality whose violation rules out some latent variable model as a "hidden variable test" for that mod…

QT improves inference in complex PGMs with hidden variables.

The Gauss Image Measure uniquely identifies dual convex bodies up to dilation.

New methods for estimating causal effects with limited overlap, using Stable Probability Weighting.

The muti-layer information bottleneck (IB) problem, where information is propagated (or successively refined) from layer to layer, is considered. Based on information forwarded by the preceding layer, each stage of the network is required to preserve a certain level of relevance with regards to a specific hidden variab…

We use the score function for causal discovery, tackling challenges with hidden variables.

Algorithm BGLM-OFU minimizes regret in combinatorial causal bandits with binary models.

RBMs model binary interactions with hidden node activation effects.

DAG models with hidden variables present many difficulties that are not present when all nodes are observed. In particular, fully observed DAG models are identified and correspond to well-defined sets ofdistributions, whereas this is not true if nodes are unobserved. Inthis paper we characterize exactly the set of dist…

New estimators for causal effects in DAGs with hidden variables, addressing computational and statistical challenges.

New method identifies causal relationships in presence of hidden variables.

In this work, we propose the marginal structured SVM (MSSVM) for structured prediction with hidden variables. MSSVM properly accounts for the uncertainty of hidden variables, and can significantly outperform the previously proposed latent structured SVM (LSSVM; Yu & Joachims (2009)) and other state-of-art methods, espe…

This paper enhances LSTM neural networks for multi-variable time series data, providing interpretable insights.

New method learns differential equations from data with hidden variables.

Modified asymmetric hidden Markov models for time series with autoregressive components.

CgNN uses network structure as IVs to estimate causal effects in networks.

In this paper, we propose multi-variable LSTM capable of accurate forecasting and variable importance interpretation for time series with exogenous variables. Current attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To …