arXiv research

GACBO optimizes unknown causal graphs with interventions.

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

Identifies root causes of outliers in unknown cyclic graphs.

New method identifies latent causal graphs without parametric assumptions.

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

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…

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

Framework for completing computational graphs using Gaussian Processes.

New algorithms for causal bandits without knowing the graph structure.

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…

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

New method learns graph structure and uncertainty from data.

Proposes a new CBO method without known causal graphs.

New algorithm identifies best intervention without graph knowledge.

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…

We develop an unsupervised, nonparametric, and scalable statistical learning method for detection of unknown objects in noisy images. The method uses results from percolation theory and random graph theory. We present an algorithm that allows to detect objects of unknown shapes and sizes in the presence of nonparametri…

New formulas for spatial 2-bouquet graphs discovered.

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

Extends angular synchronization to heterogeneous groups, improving accuracy in multiple applications.

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

Optimizes causal effects on unknown graphs using Causal Entropy Optimization.

A graph bandit algorithm learns optimal paths on unknown graphs.

Efficiently matches random graphs with inhomogeneous edge probabilities.

This paper evaluates knowledge graph completion models under the open-world assumption, revealing unexpected behavior of metrics.

Proposes a graph neural network for efficient multi-agent routing.

New method infers causal effects without knowing control variables.

We study agents communicating over an underlying network by exchanging messages, in order to optimize their individual regret in a common nonstochastic multi-armed bandit problem. We derive regret minimization algorithms that guarantee for each agent $v$ an individual expected regret of $\widetilde{O}\left(\sqrt{\left(…

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

We consider the problem of recovering a function input of a differential equation formulated on an unknown domain $M$. We assume to have access to a discrete domain $M_n=\{x_1, \dots, x_n\} \subset M$, and to noisy measurements of the output solution at $p\le n$ of those points. We introduce a graph-based Bayesian inve…

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

The notion of a pseudoknot is defined as an equivalence class of knot diagrams that may be missing some crossing information. We provide here a topological invariant schema for pseudoknots and their relatives, 4-valent rigid vertex spatial graphs and singular knots, that is obtained by replacing unknown crossings or ve…

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

Study identifies causal relationships without direct supervision from unknown interventions.

The paper extends NUP representations to factor graphs for better estimation.

A graph manifold rational homology $3$-sphere $W$ with a left-orderable fundamental group admits a co-oriented taut foliation, though it is unknown whether it admits a smooth co-oriented taut foliation. In this paper we extend the gluing theorem of arXiv:1401.7726 to graph manifold rational homology solid tori and use …

Kernel smoothing on unknown manifolds with bounds and asymptotic normality.

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

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

A novel approach is put forth that utilizes data similarity, quantified on a graph, to improve upon the reconstruction performance of principal component analysis. The tasks of data dimensionality reduction and reconstruction are formulated as graph filtering operations, that enable the exploitation of data node connec…

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…

Develops GNNs for incomplete graphs, improving learning from missing node attributes.

A graph is called intrinsically knotted if every embedding of the graph contains a knotted cycle. Johnson, Kidwell and Michael showed that intrinsically knotted graphs have at least 21 edges. Recently Lee, Kim, Lee and Oh, and, independently, Barsotti and Mattman, showed that $K_7$ and the 13 graphs obtained from $K_7$…

Estimates multiple networks using graphons for non-aligned graphs.

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. …

Graph Kalman filters adapt classical filters to graph data.

Recent advances in Quantum Topology assign $q$-series to knots in at least three different ways. The $q$-series are given by generalized Nahm sums (i.e., special $q$-hypergeometric sums) and have unknown modular and asymptotic properties. We give an efficient method to compute those $q$-series that come from planar gra…

We introduce Graphical TREX (GTREX), a novel method for graph estimation in high-dimensional Gaussian graphical models. By conducting neighborhood selection with TREX, GTREX avoids tuning parameters and is adaptive to the graph topology. We compare GTREX with standard methods on a new simulation set-up that is designed…