arXiv research

The paper defines conditions for learning causal graphs from data with unobserved variables.

This paper tackles non-convex phase retrieval with structured assumptions.

Improved causal discovery methods for large graphs without strict assumptions.

In the paper two important theorems about complete affine spheres are generalized to the case of statistical structures on abstract manifolds. The assumption about constant sectional curvature is replaced by the assumption that the curvature satisfies some inequalities.

Develops methods for constructing likelihoods and priors for Bayesian networks.

The paper proposes using low rank assumption to improve causal structure learning in DAGs.

Paper relaxes independence assumption for non-centered data.

Paper proves stability of solutions for specific hyperbolic systems.

Study reward-free RL in non-linear settings, improving efficiency and removing assumptions.

New algorithm reduces regret in combinatorial causal bandits without graph structure.

A test for sparsity in Bayesian networks helps choose algorithms.

Distributions over rankings are used to model data in various settings such as preference analysis and political elections. The factorial size of the space of rankings, however, typically forces one to make structural assumptions, such as smoothness, sparsity, or probabilistic independence about these underlying distri…

Bayesian model selection improves causal discovery in complex datasets.

New theory allows ICA without assuming non-Gaussian sources.

Wave equation map reveals manifold's structure.

New DA method CIRM outperforms existing methods under structural causal model assumptions.

In this work, we consider the identifiability assumption of Gaussian linear structural equation models (SEMs) in which each variable is determined by a linear function of its parents plus normally distributed error. It has been shown that linear Gaussian structural equation models are fully identifiable if all error va…

Study conformal product structures on reducible Riemannian manifolds.

We characterize distributional equivalence in latent-variable models with cycles.

New algorithms solve stochastic variational inequalities without bounded variance assumption.

SpinSVAR estimates SVAR models with sparse input, improving accuracy and scalability.

New estimators reduce computation for Kendall's tau and conditional Kendall's tau matrices under structural assumptions.

We identify direct causes of a target variable from observational data without full DAG identifiability.

Improved model for non-smooth signals with complex spectra.

New score-based methods identify causal structures with latent variables.

New approach uses 'forward-looking' counterfactuals for treatment choice.

New methods generalize nonlinear ICA beyond structural sparsity.

Novel risk bound for structured prediction tackles non-i.i.d. data.

We establish Bochner-type formulas for operators related to $CR$ automorphisms and spherical $CR$ structures. From such formulas, we draw conclusions about rigidity by making assumptions on the Tanaka-Webster curvature and torsion.

Researchers identify latent variables and causal structures from nonlinear hierarchical models.

This study validates BN structure learning algorithms under noisy data, revealing performance discrepancies.

Proposes a partially linear structure to capture nonlinear relationships in mixture of experts models.

Study real logarithms of semi-simple matrices, focusing on differential structure.

New result on Einstein manifolds using conformal product structures.

Proposes a new method for algorithmic recourse in confounded settings.

We study the geometry at infinity of expanding gradient Ricci solitons of dimension greater than two with finite asymptotic curvature ratio without curvature sign assumptions. We mainly prove that they have a cone structure at infinity.

Deep learning models converge to Gaussian dynamics with mixed structured inputs.

We investigate if kernel regularization methods can achieve minimax convergence rates over a source condition regularity assumption for the target function. These questions have been considered in past literature, but only under specific assumptions about the decay, typically polynomial, of the spectrum of the the kern…

On a 3-manifold bounding a compact 4-manifold, let a conformal structure be induced from a complete Einstein metric which conformally compactifies to a Kähler metric. Formulas are derived for the eta invariant of this conformal structure under additional assumptions. One such assumption is that the Kähler metric admits…

For compact Kählerian manifolds, the holomorphic pseudosymmetry reduces to the local symmetry if additionally the scalar curvature is constant and the structure function is non-negative. Similarly, the holomorphic Ricci-pseudosymmetry reduces to the Ricci-symmetry under these additional assumptions. We construct exampl…

Optimal reinsurance contracts for multiple dependent risks are derived without specific dependency assumptions.

DAG-FOCI learns causal relationships without parametric assumptions.

A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individuals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, …

Classic Topic Models are built under the Bag Of Words assumption, in which word position is ignored for simplicity. Besides, symmetric priors are typically used in most applications. In order to easily learn topics with different properties among the same corpus, we propose a new line of work in which the paragraph str…

Proposes a differentiable structure learning framework for general binary data.

The time-evolving precision matrix of a piecewise-constant Gaussian graphical model encodes the dynamic conditional dependency structure of a multivariate time-series. Traditionally, graphical models are estimated under the assumption that data is drawn identically from a generating distribution. Introducing sparsity a…

In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. We design an inference algorithm based on Expectation Propagation and evaluate the mode…

We introduce structured prediction energy networks (SPENs), a flexible framework for structured prediction. A deep architecture is used to define an energy function of candidate labels, and then predictions are produced by using back-propagation to iteratively optimize the energy with respect to the labels. This deep a…