arXiv research

Paper extends SVI to non-conjugate models with two levels of hidden variables.

New framework explains why over-parameterized neural networks work well.

This paper proposes a generative model, the latent Dirichlet hidden Markov models (LDHMM), for characterizing a database of sequential behaviors (sequences). LDHMMs posit that each sequence is generated by an underlying Markov chain process, which are controlled by the corresponding parameters (i.e., the initial state …

This paper shows how path spaces on two-level manifolds can be Hilbert manifold structures.

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 …

The paper uses machine learning to predict malaria risk using environmental and climate variables.

Paper introduces MLRH, a probabilistic model for multilabel classification.

This work presents entropic constraints from DAGs with hidden variables.

New methods for estimating causal effects in hidden variable DAGs.

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

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 …

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…

Method selects variables for malaria risk prediction using Lasso and GLM.

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

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

Polynomial delay algorithm tests causal models with hidden variables.

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…

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

QT improves inference in complex PGMs with hidden variables.

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

Develops a framework for interpretable two-level Boolean rules.

A theorem connects integral of second-order derivatives to function rise.

New objective improves disentanglement of discrete and continuous variables.

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

RBMs model binary interactions with hidden node activation effects.

Paper discovers hidden common variables in nonlinear data.

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

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 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.

The multi-layer IB problem optimizes relevance and compression rates.

New method learns differential equations from data with hidden variables.

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

This work analyzes generalization in federated learning using information theory.

Recent advancements in neural networks learn features from complex inputs.

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

Method learns structure of BAPs without hidden variables.

New method learns graph structure with hidden causes from observational data.

Hybrid model improves traffic flow prediction accuracy.

This paper addresses the issue of model selection for hidden Markov models (HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has been recently developed for model selection on independent hidden variables (i.e., mixture models), for time-dependent hidden variables. As with FAB in mixture model…

Paper proposes a flexible framework for joint modeling of sequence features and summary response variables.