arXiv research

This survey reviews Heterogeneous Representation Learning (HRL) for diverse data types.

Detects dense subhypergraphs in heterogeneous random hypergraphs.

Paper analyzes D-SGD convergence with heterogeneous data and proposes topology learning.

Introduces a new system for modeling bank solvency contagion with heterogeneous impacts and exposures.

This research tackles sample complexity in causal graph recovery with temporal heterogeneity.

New methods handle both data and network heterogeneity in federated learning.

CAP adapts optimization to class attributes for better fairness.

This study analyzes decision-making in diverse environments where past data may not predict future outcomes.

Method for understanding heterogeneous treatment effects in complex causal graphs.

DFFL tackles federated learning with heterogeneous objectives and constraints.

New bucketing scheme improves Byzantine robustness for heterogeneous data.

Bayesian optimization improves with transfer learning for aircraft design.

VAEM extends VAEs to handle mixed-type data heterogeneity.

Efficiently solves heterogeneous QPs by reducing variables using instance-specific projections.

New bounds for LDP with heterogeneous privacy levels guaranteeing high probability of accuracy.

The causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions. Mining for patterns of individual-level effect differences, a problem known as heterogeneous treatment effect estimation, has many important applications, from precision medicine to recommender…

V1 cortex reconstructs images as Poisson equation solutions with varying weights.

The thesis clarifies when local updates outperform centralized methods in heterogeneous data environments.

Paper introduces HGSL for heterogeneous graphs, improving edge type and weight recovery.

The paper tackles personalized policy learning from diverse data sources in a federated setting.

A new algorithm optimizes local objectives in federated learning with heterogeneous clients.

PerPCA separates unique and shared features from heterogeneous data.

Estimates price sensitivity from transaction data using a novel odds ratio method.

Deep RL solves complex economic models with heterogeneous agents.

New method quantifies variable importance in causal forests for treatment effect heterogeneity.

Paper shows local SGD outperforms mini-batch SGD under certain conditions.

A federated minimax framework for heterogeneous clients.

Graph representation learning is to learn universal node representations that preserve both node attributes and structural information. The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering. When a graph is heterogeneous, the problem becomes more…

New strategies improve multi-agent decision-making on irregular networks.

A discrete system's heterogeneity is measured by the Rényi heterogeneity family of indices (also known as Hill numbers or Hannah--Kay indices), whose units are {the numbers equivalent}. Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require {a priori} (A) categorical partitioning and …

New test detects differences in heterogeneous datasets.

Upper bounds on utility for managing heterogeneous collectivised funds.

This paper tackles objective inconsistency in federated optimization with heterogeneous clients.

Characterizes uncertainty in low-rank matrix completion with noisy data.

In this paper, we consider the problem of event classification with multi-variate time series data consisting of heterogeneous (continuous and categorical) variables. The complex temporal dependencies between the variables combined with sparsity of the data makes the event classification problem particularly challengin…

Study robust estimation under varying corruption probabilities in data.

A new method detects communities in multi-relational networks.

A framework for causal classification using uplift and causal heterogeneity methods.

MindFlayer SGD improves parallel SGD for heterogeneous, random compute times.

This paper optimizes reinsurance contracts with belief differences between insurer and reinsurer.

The Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data. An adequate selection of the probabilities and priors of these bayesian models allows the model to better adapt to the data nature (i.e. heterogeneity, s…

New method estimates heterogeneous treatment effects with improved guarantees.

A firm with heterogeneous shareholders optimizes dividends under ambiguity aggregation.

CR-UOT improves matching of heterogeneous single-cell omics profiles.

Meta-learner estimates heterogeneous DiD effects robustly.

Given a set of heterogeneous source datasets with their classifiers, how can we quickly find the most useful source dataset for a specific target task? We address the problem of measuring transferability between source and target datasets, where the source and the target have different feature spaces and distributions.…

Bayesian Causal Forest models estimate treatment effects with noncompliance.

Datasets containing large samples of time-to-event data arising from several small heterogeneous groups are commonly encountered in statistics. This presents problems as they cannot be pooled directly due to their heterogeneity or analyzed individually because of their small sample size. Bayesian nonparametric modellin…