arXiv research

GACBO optimizes unknown causal graphs with interventions.

Identifies root causes of outliers in unknown cyclic graphs.

New algorithms for causal bandits without knowing the graph structure.

New method learns graph structure and uncertainty from data.

Optimizes causal effects on unknown graphs using Causal Entropy Optimization.

New method identifies latent causal graphs without parametric assumptions.

The paper predicts responses on out-of-sample nodes using latent positions on unknown curves.

MetaCaDI learns causal graphs and unknown interventions from few data instances.

We consider the problem of undirected graphical model inference. In many applications, instead of perfectly recovering the unknown graph structure, a more realistic goal is to infer some graph invariants (e.g., the maximum degree, the number of connected subgraphs, the number of isolated nodes). In this paper, we propo…

Study on identifying AMP chain graph models under known and unknown component decompositions.

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data. However, there are theoretical limitations on the identifiability of underlying structures obtained from observational data alone. Interventional data provides muc…

New method infers causal effects without knowing control variables.

Investigates sequential problems on graph structures and large action spaces.

Model identifies causal structure from paired observational and interventional data with unknown soft interventions.

In this paper, we present a simple non-parametric method for learning the structure of undirected graphs from data that drawn from an underlying unknown distribution. We propose to use Brownian distance covariance to estimate the conditional independences between the random variables and encodes pairwise Markov graph. …

This paper considers a new framework to detect communities in a graph from the observation of signals at its nodes. We model the observed signals as noisy outputs of an unknown network process, represented as a graph filter that is excited by a set of unknown low-rank inputs/excitations. Application scenarios of this m…

Proposes a new CBO method without known causal graphs.

This paper tackles unknown causal graphs and soft interventions, establishing regret bounds and an efficient algorithm.

New method uses manifold learning to infer latent positions of 1D submanifolds in random dot product graphs.

This work proposes a method to learn graph structure for multivariate time series forecasting.

Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. If this is not possible, the data structure has to be inferred f…

A new GP framework for discovering unknown functions and hypergraph structure.

Graph-based LRE estimates likelihood-ratios collaboratively for nodes.

This paper considers the problem of estimating the structure of multiple related directed acyclic graph (DAG) models. Building on recent developments in exact estimation of DAGs using integer linear programming (ILP), we present an ILP approach for joint estimation over multiple DAGs, that does not require that the ver…

Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be inferred. To predict whether a relation holds between entities, embeddings are typically compared in the latent space following a relation-spe…

A new growth model for dynamic networks using Markovian latent points.

Paper models graph edge dependencies using latent variables for community detection.

Bayesian methods estimate regression functions on submanifolds using graph Laplacian eigenbasis.

A graph bandit algorithm learns optimal paths on unknown graphs.

Unified framework for clustering and learning causal graphs across subjects.

A novel GPUM constructs Gaussian Processes for unknown manifolds with probabilistic metrics.

We study, to the best of our knowledge, the first Bayesian algorithm for unimodal Multi-Armed Bandit (MAB) problems with graph structure. In this setting, each arm corresponds to a node of a graph and each edge provides a relationship, unknown to the learner, between two nodes in terms of expected reward. Furthermore, …

A Bayesian treatment of latent directed graph structure for non-iid data is provided where each child datum is sampled with a directed conditional dependence on a single unknown parent datum. The latent graph structure is assumed to lie in the family of directed out-tree graphs which leads to efficient Bayesian inferen…

Study identifies causal relationships without direct supervision from unknown interventions.

This paper shows how to estimate distances in latent space of random graphs using entropic OT.

Bayesian optimisation framework for graph functions.

Framework for completing computational graphs using Gaussian Processes.

Empirical error estimates improve graph sparsification reliability.

Novel graph-spanning algorithm detects changes in high-dimensional data.

A new model clusters networks with community-specific submanifold structures.

We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network $\mathcal{M}$ on a graph with $n$ discrete variables and bounded in-degree and bounded `confounded components', we show that $O(\log n)$ interventions on an unknown causal Bayesian ne…

DGM learns graph structure for better graph neural network performance.

Method learns SDEs from one trajectory using GP priors and randomized cross-validation.

Hierarchical Latent Attribute Models (HLAMs) are a family of discrete latent variable models that are attracting increasing attention in educational, psychological, and behavioral sciences. The key ingredients of an HLAM include a binary structural matrix and a directed acyclic graph specifying hierarchical constraints…

We propose a framework that learns the graph structure underlying a set of smooth signals. Given $X\in\mathbb{R}^{m\times n}$ whose rows reside on the vertices of an unknown graph, we learn the edge weights $w\in\mathbb{R}_+^{m(m-1)/2}$ under the smoothness assumption that $\text{tr}{X^\top LX}$ is small. We show that …

This work focuses on the estimation of multiple change-points in a time-varying Ising model that evolves piece-wise constantly. The aim is to identify both the moments at which significant changes occur in the Ising model, as well as the underlying graph structures. For this purpose, we propose to estimate the neighbor…

The edge structure of the graph defining an undirected graphical model describes precisely the structure of dependence between the variables in the graph. In many applications, the dependence structure is unknown and it is desirable to learn it from data, often because it is a preliminary step to be able to ascertain c…

Effective information analysis generally boils down to properly identifying the structure or geometry of the data, which is often represented by a graph. In some applications, this structure may be partly determined by design constraints or pre-determined sensing arrangements, like in road transportation networks for e…