Develops neural network for directed hypergraphs for node classification.
problem Irregular data structure, particularly directed graphs.
method Directed hypergraph neural network and semi-supervised learning method.
result Novel directed hypergraph neural network achieves highest accuracies on node classification tasks.
New method clusters data points by finding optimal directions.
problem Subspace clustering problem, especially in noisy and close subspaces.
method Optimal direction search via convex program, alternating direction method of multipliers.
result Significantly outperforms existing methods, especially in noisy scenarios.
Unsupervised method discovers interpretable directions in GAN latent space.
problem Discovering interpretable directions in GAN latent space without supervision.
method Model-agnostic procedure to identify directions corresponding to semantic manipulations.
result Findings include directions for background removal and competitive saliency detection performance.
New method identifies valid IVs for bi-directional MR with invalid instruments.
problem Estimating causal effects from observational data with invalid instruments and unmeasured confounding.
method Theoretical investigation and cluster fusion-like method to discover valid IV sets.
result Theoretical demonstration and experimental validation of the method's effectiveness.
Two multifidelity trust-region methods use low-fidelity models for efficient optimization.
problem Efficiently solving complex optimization problems with limited data.
method Sketched Trust-Region (STR) and SVD Trust-Region (SVDTR) methods using low-fidelity models.
result Potential gain in efficiency demonstrated through numerical examples.
New method for community detection in sparse directed SBMs with exact recovery guarantees.
problem Exact recovery in sparse directed SBMs, especially with growing communities.
method Two-stage procedure: neighborhood-smoothing followed by K K K -means clustering. result Exact recovery of all community labels with probability tending to one under mild sparsity and separation conditions.
Deep Q-learning generates directed acyclic graphs.
problem Generating DAGs with specified structures.
method Deep reinforcement learning, specifically deep Q-learning.
result Demonstrated capability of generating DAGs in sparse reward environments.
New algorithms improve causal direction inference accuracy using parallel ensemble methods.
problem Stability of causal direction inference results from observational data.
method Parallel ensemble frameworks to map and improve inference accuracy.
result Significant improvement in accuracy of causal direction inference.
A new GAN method ensures unbiased updates towards the steepest descent direction.
problem GANs update generator parameters in non-optimal directions.
method Introduces a theoretical framework and divergence approximating Wasserstein distance, ensuring unbiased steepest descent updates.
result Sets a new state-of-the-art on language generation tasks.
New graph AE and VAE model predicts directed links better than existing methods.
problem Link prediction in directed graphs, especially for unobserved edges.
method Gravity-inspired decoder scheme for directed graphs.
result Outperforms standard graph AE and VAE on three real-world directed link prediction tasks.
DIGRAC clusters directed graphs using flow imbalance, outperforming existing methods.
problem Clustering directed networks without label supervision.
method DIGRAC uses a graph neural network with a novel imbalance loss for directed flow imbalance.
result DIGRAC outperforms 10 state-of-the-art methods on directed graph clustering.
Dimension reduction of multivariate data supervised by auxiliary information is considered. A series of basis for dimension reduction is obtained as minimizers of a novel criterion. The proposed method is akin to continuum regression, and the resulting basis is called continuum directions. With a presence of binary sup…
Method discovers nonlinear relations from time series data.
problem Identifying directional relations from nonlinear interactions in time series.
method Minimum predictive information regularization method for deep learning.
result Substantially outperforms other methods for learning nonlinear relations.
Proposes a novel approach using vector cross product to preserve directional edges in directed graphs.
problem Preserving directional edges in directed graphs for tasks like link prediction and node recommendation.
method Integrates the non-commutative property of vector cross product into a Siamese neural network to learn N-dimensional embeddings.
result Low-dimensional embeddings effectively preserve directional properties and outperform state-of-the-art methods.
PyTorch Geometric Signed Directed fills the gap for GNNs on signed and directed graphs.
problem Lack of unified software packages for GNNs on signed and directed networks.
method Developed a software package with GNN models, synthetic and real-world data, and evaluation metrics.
result Demonstrates the effectiveness of the implemented methods through experiments.
Spectral clustering for directed graphs using likelihood estimation.
problem Clustering directed graphs with edge directions.
method Maximum likelihood estimation on stochastic block models.
result Significant performance gains over existing methods.
Direct method finds Yang-Mills connections for SO(3) bundles.
problem Finding Yang-Mills connections for SO(3) bundles over closed 4-manifolds.
method Direct minimizing method with test connections and assumptions.
result Minimizing sequence converges to a minimizer or anti-selfdual/selfdual connection.
Optimizes CNNs by directing gradients along output channels.
problem Improving generalization error in CNNs.
method Output-channel directed re-weighted L2 or Sobolev metrics.
result Improves generalization error by optimizing gradients.
We propose two new alternating direction methods to solve "fully" nonsmooth constrained convex problems. Our algorithms have the best known worst-case iteration-complexity guarantee under mild assumptions for both the objective residual and feasibility gap. Through theoretical analysis, we show how to update all the al…
New clustering methods use motifs to organize networks.
problem Organizing directed graphs efficiently.
method Construct clustering methods parametrized by motifs.
result New clustering methods can organize networks.
We study direct limits ( G , K ) = l i m → ( G n , K n ) (G,K) = \varinjlim (G_n,K_n) ( G , K ) = lim ( G n , K n ) of compact Gelfand pairs. First, we develop a criterion for a direct limit representation to be a multiplicity--free discrete direct sum of irreducible representations. Then we look at direct limits G / K = l i m → G n / K n G/K = \varinjlim G_n/K_n G / K = lim G n / K n of compact riemannian symmetric spaces, …
Detects adversarial directions to make reinforcement learning policies more robust.
problem Adversarial attacks exploit non-robust directions in reinforcement learning policies, leading to instability.
method Local quadratic approximation of deep neural policy loss to identify non-robust directions.
result Provides a theoretical basis for distinguishing safe from adversarial observations.
This paper provides a block coordinate descent algorithm to solve unconstrained optimization problems. In our algorithm, computation of function values or gradients is not required. Instead, pairwise comparison of function values is used. Our algorithm consists of two steps; one is the direction estimate step and the o…
New method estimates root-directed tree from extreme data.
problem Discovering causality in river networks from extreme flow data.
method Qualitative max-linear Bayesian network approach to estimate bivariate scores and root-directed spanning tree.
result The new estimator is consistent under max-linear Bayesian network model with noise.
The paper presents a new method to represent directed graphs using pseudo-Riemannian manifolds.
problem Representing directed graphs in a compact and meaningful way.
method Combines pseudo-Riemannian metric structure, non-trivial global topology, and a unique likelihood function.
result Low-dimensional cylindrical Minkowski and anti-de Sitter spacetimes produce equal or better graph representations than curved Riemannian manifolds.
TRA detects causal direction from bivariate data using geometric shapes.
problem Inferring causal direction from observational data is challenging and unreliable.
method TRA compares rank-based copula-standardized residual clouds to detect causal direction.
result TRA is robust and superior in detecting causal direction across various scenarios.
Survey of SDR methods for high-dimensional regression and embedding.
problem Reducing dimensionality in high-dimensional data.
method Involves both statistical and machine learning approaches, covering inverse and forward regression methods.
result Supervised Kernel Dimension Reduction is equivalent to supervised PCA.
New method clusters directed and undirected graphs without losing directional information.
problem Clustering directed graphs due to asymmetry in edge connectivity.
method Generalized Dirichlet Energy (GDE) and generalized spectral clustering (GSC).
result GSC outperforms existing methods in clustering accuracy and robustness.
Two spectral algorithms detect clusters in directed graphs with cyclic or acyclic patterns.
problem Detecting clusters in directed graphs with cyclic or acyclic patterns.
method Computation of extremal eigenvalues of the transition matrix associated with the directed graph.
result The proposed methods outperform state-of-the-art methods on synthetic datasets and real-world networks.
The paper studies kernel smoothing and mean shift for directional data, deriving convergence rates and mode estimation.
problem Statistical and computational problems of kernel smoothing for directional data.
method Generalization of mean shift to directional data, derivation of convergence rates, and investigation of mode estimation.
result Statistical convergence rates of directional KDE and its derivatives, ascending property of directional mean shift, and mode estimation.
A new kernel test reduces noise in MMD by focusing on leading eigen-directions.
problem Noise in trailing directional components degrades power of standard kernel two-sample tests.
method Truncate MMD spectral decomposition, retaining only leading eigen-directions.
result Our method achieves superior power and robustness, especially in high-dimensional and unbalanced settings.
AB-SAGA optimizes distributed optimization over directed graphs using variance reduction and stochastic weights.
problem Optimizing distributed stochastic optimization over directed graphs with stochastic weights.
method AB-SAGA combines variance reduction and network-level gradient tracking, using both row and column stochastic weights.
result AB-SAGA converges linearly to the global optimal with a constant step-size and achieves a linear speed-up over centralized methods.
This paper presents a method to summarize directed graphs while preserving edge information.
problem Summarizing directed graphs while maintaining edge directionality.
method A model based on minimizing reconstruction error with non-negative constraints, related to Max-Cut criterion, using multiplicative update algorithms.
result The proposed method identifies compressed nodes and directed compressed relations, providing a more accurate representation of directed graphs.
This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model a directed graph as a finite set of observations from a diffusion on a manifold endowed with a vector field. This is the first generative model of its kind for directed graphs. We introduce…
Local discovery method uncovers direct unfairness in complex systems.
problem Identifying causal pathways of unfairness in complex domains.
method Local discovery for direct discrimination (LD3) method.
result LD3 returns a valid adjustment set (VAS) for assessing unfairness.
DEDACT breaks down feature importance into direct and associative components.
problem Lack of clear distinction between direct and associative feature importance.
method DEDACT framework to decompose direct and associative importance measures.
result Provides insight into sources of prediction-relevant information and feature pathways.
iSearch uses innovation directions for robust PCA and outlier detection.
problem Robust PCA and outlier detection in data with outliers.
method iSearch uses innovation directions to compute optimal data points and identify outliers.
result iSearch provides robust PCA and outlier detection with performance guarantees.
Yau's Affine Normal Descent optimizes smooth unconstrained problems with geometrically adapted directions.
problem Optimizing smooth unconstrained problems with geometrically adapted directions.
method Yau's Affine Normal Descent (YAND) uses the equi-affine normal of level-set hypersurfaces as search directions.
result YAND converges globally under standard smoothness assumptions and locally quadratically near nondegenerate minimizers.
Improved spectral-based GCN for directed graphs.
problem Cannot directly work on directed graphs.
method Redefined Laplacians to improve propagation model.
result Outperforms state-of-the-art methods on directed graph datasets.
Efficient algorithm learns direct causes and effects from data.
problem Discovering direct causes and effects from data in a large space.
method ELCS algorithm using N-structures and Markov Blanket discovery.
result ELCS achieves better accuracy and efficiency than state-of-the-art methods.
Chandrasekaran, Parrilo and Willsky (2010) proposed a convex optimization problem to characterize graphical model selection in the presence of unobserved variables. This convex optimization problem aims to estimate an inverse covariance matrix that can be decomposed into a sparse matrix minus a low-rank matrix from sam…
A new pruning method finds sparse minimizers in flat regions of deep neural networks.
problem Finding sparse minimizers in flat regions of deep neural networks.
method Directional pruning using an ℓ 1 \ell_1 ℓ 1 proximal gradient algorithm. result Empirical results show promising sparsity (92%) and comparable minima to SGD.
New graph kernel for weighted directed networks using functor homology.
problem Studying weighted directed networks with functor homology.
method Proposes a new homological method to define graph kernels for weighted directed graphs.
result Defined a new graph kernel for weighted directed graphs.
New model identifies causal direction in nonlinear systems with observed data.
problem Identifying causal direction in nonlinear systems with observed data.
method Cascade Nonlinear Additive Noise Model (CNANM) with Variational Auto-Encoder (VAE) estimation.
result Causal direction is identifiable under suitable conditions on data generation.
The paper presents a method for sound event localization and detection using CRNN models.
problem Sound event localization and detection in complex environments.
method Consecutive ensemble of CRNN models for estimating event onset, offset, direction of arrival, and classification.
result The proposed method outperforms other participants in the DCASE2019 task3.
A novel framework infers causal direction from symbolic sequences using pattern entropy.
problem Challenges in discovering causal direction from temporal symbolic data.
method Dictionary Based Pattern Entropy ( D P E DPE D P E ) framework integrating AIT and Shannon Information Theory. result Minimizing pattern level uncertainty yields a robust framework for causal discovery.
Estimates modes and ridges in mixed Euclidean and directional spaces.
problem Estimating local modes and density ridges in product spaces combining Euclidean and directional metrics.
method Extends mean shift algorithm to product spaces, addressing challenges in generalization.
result Established convergence of the proposed methods and demonstrated effectiveness on real-world datasets.
Stochastic gradient descent outperforms traditional force-directed methods.
problem Improving graph layout quality and efficiency.
method Applying stochastic gradient descent for stress minimization.
result Stochastic gradient descent is simpler and more robust than traditional methods.