The paper introduces heterogeneous manifolds for better graph embeddings.
problem Graph embeddings in Euclidean spaces often fail to capture the curvature of real-world graphs.
method The authors propose heterogeneous rotationally-symmetric manifolds with a radial dimension to account for varying curvature.
result The method improves graph embeddings by better preserving high-order structures and heterogeneous random graphs.
New algorithm tackles heterogeneous curvature in online convex optimization.
problem Adversarial bandit convex optimization with varying curvature.
method Developed an adaptive algorithm that learns curvature on the fly.
result Achieves optimal regret bounds even with heterogeneous curvature.
Study on evolving interfaces with complex curvature and density effects.
problem Understanding the dynamics of evolving heterogeneous elastic interfaces.
method Modeling an evolving curve with a density function, analyzing the associated gradient flow evolution.
result Analysis of the preservation and asymptotic behavior of geometric properties in the evolving system.
Improved Local SGD convergence for general convex objectives with bounded second-order heterogeneity.
problem Understanding when and why Local SGD outperforms alternatives in distributed optimization.
method Established improved convergence guarantees for Local SGD on general convex objectives under bounded second-order heterogeneity.
result Upper bounds for Local SGD are nearly tight, providing a sharper convergence theory.
In recent years, there has been a growing interest in geometric evolution in heterogeneous media. Here we consider curvature driven fows of planar curves, with an additional space-dependent forcing term. Motivated by a homogenization problem, we look for estimates which depend only on the uniform norm of the forcing te…
A new model for graph clustering using curvature spaces.
problem Graph clustering from a geometric perspective.
method Introducing a heterogeneous curvature space and a contrastive learning approach.
result CONGREGATE model outperforms state-of-the-art competitors.
ElbowSig assesses clustering structure at multiple scales.
problem Selecting optimal number of clusters in unsupervised learning.
method Formalizes elbow heuristic with a normalized discrete curvature statistic.
result Validates multiscale clustering structure over various resolutions.
In this note, we derive an approximation for the mean curvature normal vector on vertices of triangulated surface meshes from the Young-Laplace equation and the force balance principle. We then demonstrate that the approximation expression from our physics-based derivation is equivalent to the discrete Laplace-Beltrami…
Study on dynamic curves with elastic energy and spontaneous curvature.
problem Modeling and analyzing dynamic planar curves with elastic energy.
method Gradient flow of inclination angle, nonlocal quasilinear system, local well-posedness, global existence, convergence.
result Local well-posedness, global existence, convergence of the flow for weak regularity initial data.
Researchers approximate spectral targets on manifolds with constant negative curvature.
problem Prescribing an arbitrary finite portion of the Laplace-Beltrami spectrum on manifolds of constant negative curvature.
method Constructing macroscopically heterogeneous hyperbolic covering manifolds in d≥3 and using discrete spectral limit theorems in d=2. result Any finite strictly increasing target list can be approximated to arbitrary precision by a closed manifold of constant negative curvature.
We address the minimization of the Canham-Helfrich functional in presence of multiple phases. The problem is inspired by the modelization of heterogeneous biological membranes, which may feature variable bending rigidities and spontaneous curvatures. With respect to previous contributions, no symmetry of the minimizers…
AIHT improves online high-dimensional quantile regression by separating support discovery and refinement.
problem Online high-dimensional quantile regression with structural sparsity.
method Adaptive Iterative Hard Thresholding (AIHT) alternates stochastic updates with adaptive hard-thresholding steps.
result AIHT achieves logarithmic regret for the sliding-window objective in high-dimensional settings.
GeoERM learns shared representations on Riemannian manifolds for multi-task learning.
problem Heterogeneous and adversarial tasks in MTL.
method Geometry-aware MTL framework embedding shared representation on Riemannian manifold, optimizing via manifold operations.
result GeoERM improves estimation accuracy and stability, outperforming alternatives.
VSAE learns from missing heterogeneous data by modeling latent dependencies.
problem Learning from partially-observed heterogeneous data with missingness.
method Variational selective autoencoder (VSAE) models joint distribution of observed, unobserved, and missing data.
result VSAE improves over state-of-the-art models in data generation and imputation tasks.
We develop a computationally efficient method to estimate Ollivier-Ricci curvature.
problem Computational infeasibility of evaluating Ollivier-Ricci curvature on large graphs.
method Derive explicit transfer moduli between OR and BF curvatures, construct lazy transport envelopes, and use cross-edge matching.
result Deterministic bounds for OR curvature parameterized by local graph combinatorics, reducing complexity to worst-case O(max_v deg(v)^1.5).
Study quantifies impacts of heterogeneity in FL on smartphone data.
problem Heterogeneity in FL devices causes performance degradation.
method Collected 136k smartphone data, built heterogeneity-aware FL platform, conducted extensive experiments.
result Heterogeneity causes up to 9.2% accuracy drop and 2.32x training time increase.
New methods handle both data and network heterogeneity in federated learning.
problem Challenges in federated learning due to data and network heterogeneity.
method Two novel client selection schemes that minimize theoretical runtime to convergence.
result Our methods are at least competitive to and up to 20 times better than existing baselines.
New method for learning on heterogeneous graphs without meta-paths.
problem Learning on heterogeneous graphs is sensitive to meta-paths choice, leading to poor performance.
method Decompose heterogeneous graph into homogeneous relation-type graphs, combine higher-order representations, use attention mechanisms.
result Our model outperforms state-of-the-art baselines in vertex classification tasks on heterogeneous graph datasets.
Financial markets are notoriously complex environments, presenting vast amounts of noisy, yet potentially informative data. We consider the problem of forecasting financial time series from a wide range of information sources using online Gaussian Processes with Automatic Relevance Determination (ARD) kernels. We measu…
Detects dense subhypergraphs in heterogeneous random hypergraphs.
problem Testing for the existence of a dense subhypergraph in heterogeneous random hypergraphs.
method Established detection boundaries and constructed asymptotically powerful and adaptive tests.
result Developed tests for distinguishing between null and alternative hypotheses.
HGT models heterogeneous graphs with dedicated node and edge representations.
problem Infeasibility of existing GNNs for heterogeneous graphs.
method Node- and edge-type dependent parameters, relative temporal encoding, heterogeneous mini-batch graph sampling.
result HGT outperforms state-of-the-art GNNs by 9%--21% on various downstream tasks.
Paper analyzes D-SGD convergence with heterogeneous data and proposes topology learning.
problem Efficiently dealing with data heterogeneity in decentralized learning.
method Revisits D-SGD analysis, introduces neighborhood heterogeneity, and proposes topology learning.
result Formulates topology learning as a tractable optimization problem and demonstrates its effectiveness.
Method for understanding heterogeneous treatment effects in complex causal graphs.
problem Heterogeneity and comorbidity in healthcare problems.
method Developed a new approach to characterize heterogeneous causal effects (HCEs) in graphical contexts, including heterogeneous causal graphs (HCGs) with confounders and mediators.
result Established theoretical forms and properties of HCEs in linear and nonlinear models, and developed interactive structural learning for estimation.
This survey reviews Heterogeneous Representation Learning (HRL) for diverse data types.
problem Challenges in learning from heterogeneous data types.
method Unified learning framework for modeling various learning settings.
result Unified mathematical framework for multi-view, transfer, privileged, and multi-task learning.
LATTE tackles heterogeneous network embedding challenges with layer-stacked attention.
problem Aggregating higher-order indirect relations in heterogeneous networks.
method Layer-stacked ATTention Embedding (LATTE) that decomposes meta relations at each layer.
result LATTE achieves state-of-the-art performance on benchmark datasets.
UNTIE learns representations of coupled categorical data.
problem Challenges in learning from unlabeled categorical data with complex couplings.
method UNTIE approach for unsupervised representation learning of heterogeneous couplings.
result UNTIE significantly improves categorical data representations on 25 diverse datasets.
MCBP detects boundaries in high-dimensional data using curvature.
problem Boundary detection in high-dimensional data.
method MCBP uses mean curvature to model data manifold curvature.
result MCBP improves clustering performance in complex scenarios.
The thesis clarifies when local updates outperform centralized methods in heterogeneous data environments.
problem Understanding when local updates are more effective than centralized or mini-batch methods in distributed optimization.
method Fine-grained consensus-error-based analysis framework, focusing on bounded second-order heterogeneity and third-order smoothness.
result Local updates outperform centralized or mini-batch methods under realistic models of data heterogeneity.
Unified MTL framework for heterogeneous data integrates shared and task-specific encoders.
problem Efficiently sharing information across multiple tasks with heterogeneous data.
method Dual-encoder framework with task-shared and task-specific encoders.
result Unified algorithm alternates learning task-specific and shared encoders and coefficients.
Paper introduces HGSL for heterogeneous graphs, improving edge type and weight recovery.
problem Learning structure in heterogeneous graphs with multiple node and edge types.
method Proposes H2MN model for DGPs and derives alternating optimization method.
result Demonstrates superior performance on synthetic and real-world datasets.
The paper compares methods for estimating heterogeneous treatment effects using multiple randomized trials.
problem Estimating heterogeneous treatment effects reliably and precisely with a single dataset is challenging.
method Non-parametric approaches for estimating heterogeneous treatment effects using data from multiple trials.
result Methods that directly allow for heterogeneity of the treatment effect across trials perform better than those that do not.
The paper introduces a new volatility model for state heterogeneous financial markets using high-frequency data.
problem State heterogeneity in financial volatility processes.
method Developed a state heterogeneous GARCH-Ito (SG-Ito) model based on continuous Ito diffusion process.
result Empirical studies reveal various state heterogeneities in S&P 500 index volatility.
VAEM extends VAEs to handle mixed-type data heterogeneity.
problem Heterogeneous data with different types and marginal distributions.
method Two-stage training approach to handle mixed-type data.
result VAEM improves deep generative model performance on diverse tasks.
Bayesian approach for estimating heterogeneous treatment effects in RDD designs.
problem Heterogeneity in treatment effects in RDD designs can lead to misleading conclusions.
method Direct Bayesian Additive Regression Trees (BART) for modeling heterogeneous treatment effects.
result Flexibly captures complicated structures of heterogeneous treatment effects as a function of covariates.
A new method clusters heterogeneous subgroups for accurate causal learning.
problem Diverse causal relationships across different time spans, regions, or strategies.
method Nonlinear Causal Kernel Clustering
result Reduction in prediction error through enhanced causal learning.
The aim of this paper is to propose a heterogeneous agent model of stock markets that develop complicated endogenous price fluctuations. We find occurrences of non-stationary chaos, or speculative bubble, are caused by the heterogeneity of traders' strategies. Furthermore, we show that the distributions of returns gene…
The paper proposes a mixture model with segmentation for heterogeneous functional data.
problem Heterogeneity in time and population for functional data.
method Mixture model with segmentation of time, maximum likelihood estimator, EM algorithm with dynamic programming.
result The method is consistent and identifiable, and illustrated on simulated and real datasets.
GEM detects malicious accounts using adaptive embeddings from heterogeneous graphs.
problem Detecting malicious accounts on a leading mobile payment platform.
method Adaptive learning of discriminative embeddings from heterogeneous account-device graphs with attention mechanism for node importance.
result GEM consistently outperforms competitive methods in detecting malicious accounts.
DyHATR learns dynamic heterogeneous networks for better link prediction.
problem Learning effective representations of dynamic heterogeneous networks for link prediction.
method Hierarchical attention for heterogeneous information and temporal RNN for evolutionary patterns.
result DyHATR significantly outperforms state-of-the-art baselines on link prediction tasks.
Energy distance measures feature heterogeneity in federated learning.
problem Heterogeneity across data sources hinders model aggregation in federated learning.
method Introduced Taylor approximations of energy distance for efficient computation.
result Taylor approximations accurately capture feature discrepancies, improving convergence.
Intratumor heterogeneity is often manifested by vascular compartments with distinct pharmacokinetics that cannot be resolved directly by in vivo dynamic imaging. We developed tissue-specific compartment modeling (TSCM), an unsupervised computational method of deconvolving dynamic imaging series from heterogeneous tumor…
Estimates price sensitivity from transaction data using a novel odds ratio method.
problem Estimate price sensitivity from transaction-level data with partially observed treatment assignments.
method Recursive partitioning procedure with adversarial imputation for robust estimation.
result Validated on synthetic data and applied to three case studies, demonstrating heterogeneity in treatment effects.
New method quantifies variable importance in causal forests for treatment effect heterogeneity.
problem Lack of understanding how input variables affect treatment effect heterogeneity in causal forests.
method Developed a new importance variable algorithm for causal forests based on the drop and relearn principle.
result Shows how to handle forest retraining without a confounding variable and introduces a corrective term for confounders.
This paper evaluates CFL algorithms for handling data heterogeneity in federated learning.
problem Handling data heterogeneity among clients in federated learning.
method Comparative evaluation of two state-of-the-art CFL algorithms with a proposed taxonomy of data heterogeneities.
result Analysis of CFL performance across different heterogeneity scenarios using extrinsic clustering metrics.
Estimates population mean from user-level data with privacy, accounting for heterogeneity.
problem Heterogeneous user data with varying numbers of data points and distributions.
method Simple model of heterogeneous user data, differential privacy mechanism for estimation.
result Asymptotic optimality of the proposed estimator and general lower bounds on error.
A federated minimax framework for heterogeneous clients.
problem Training with edge devices having different datasets and capabilities.
method Proposes a federated minimax optimization framework with normalized updates.
result Improves convergence and communication complexity for nonconvex functions.
Paper extends multi-task Gaussian Cox processes for heterogeneous tasks.
problem Modeling multiple heterogeneous correlated tasks jointly.
method Data augmentation and mean-field approximation for non-conjugate Bayesian inference.
result Demonstrates improved performance and inference on synthetic and real data.
Algorithm uncovers treatment effect heterogeneity in educational RD designs.
problem Discovering sources of treatment effect heterogeneity in regression discontinuity designs.
method Causal supervised machine learning algorithm to build a 'regression discontinuity tree'.
result Algorithm uncovers various sources of heterogeneity in the impact of attending a better secondary school.