A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method for learning on heterogeneous graphs without meta-paths.
LATTE tackles heterogeneous network embedding challenges with layer-stacked attention.
A new method detects communities in multi-relational networks.
MTHetGNN models complex relations in multivariate time series forecasting.
In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector spac…
Proposes a THGNN for dynamic financial time series prediction.
In online social networks people often express attitudes towards others, which forms massive sentiment links among users. Predicting the sign of sentiment links is a fundamental task in many areas such as personal advertising and public opinion analysis. Previous works mainly focus on textual sentiment classification, …
Study shows price bubbles can exist even with heterogeneous beliefs.
HIRM models noisy, sparse, heterogeneous relational data using hierarchical clustering and Dirichlet processes.
Recent work has developed Bayesian methods for the automatic statistical analysis and description of single time series as well as of homogeneous sets of time series data. We extend prior work to create an interpretable kernel embedding for heterogeneous time series. Our method adds practically no computational cost co…
Self-supervised pretraining for heterogeneous hypergraphs improves graph-based tasks.
The dynamics of a stock market with heterogeneous agents is discussed in the framework of a recently proposed spin model for the emergence of bubbles and crashes. We relate the log returns of stock prices to magnetization in the model and find that it is closely related to trading volume as observed in real markets. Th…
Enhances social spam detection using multi-level dependency of relational sequences.
We address the problem of semi-supervised learning in relational networks, networks in which nodes are entities and links are the relationships or interactions between them. Typically this problem is confounded with the problem of graph-based semi-supervised learning (GSSL), because both problems represent the data as …
The problem of identifying geometric structure in heterogeneous, high-dimensional data is a cornerstone of representation learning. While there exists a large body of literature on the embeddability of canonical graphs, such as lattices or trees, the heterogeneity of the relational data typically encountered in practic…
Unified MTL framework for heterogeneous data integrates shared and task-specific encoders.
Relational Graph Neural Networks improve fraud detection in Super-Apps.
Proposes HeteroJIVE for joint subspace estimation in multi-view data with statistical and structural heterogeneity.
Link prediction is an important and frequently studied task that contributes to an understanding of the structure of knowledge graphs (KGs) in statistical relational learning. Inspired by the success of graph convolutional networks (GCN) in modeling graph data, we propose a unified GCN framework, named TransGCN, to add…
In this paper, we present a multi-period trading model by assuming that traders face not only asymmetric information but also heterogenous prior beliefs, under the requirement that the insider publicly disclose his stock trades after the fact. We show that there is an equilibrium in which the irrational insider camoufl…
The viral spread of fake news has caused great social harm, making fake news detection an urgent task. Current fake news detection methods rely heavily on text information by learning the extracted news content or writing style of internal knowledge. However, deliberate rumors can mask writing style, bypassing language…
Distance metric learning (DML) plays a crucial role in diverse machine learning algorithms and applications. When the labeled information in target domain is limited, transfer metric learning (TML) helps to learn the metric by leveraging the sufficient information from other related domains. Multi-task metric learning …
Paper improves Bayesian network learning from related data sets.
Proposes a method to improve graph neural networks on heterogeneous graphs using meta-paths.
Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space. However, existing NRL methods ignore the impact of properties of relations on the object relevance in heterogeneous information networks (HINs). To tackl…
Inspired by the recent literature on aggregation theory, we aim at relating the long range correlation of the stocks return volatility to the heterogeneity of the investors' expectations about the level of the future volatility. Based on a semi-parametric model of investors' anticipations, we make the connection betwee…
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…
With the expeditious advancement of information technologies, health-related data presented unprecedented potentials for medical and health discoveries but at the same time significant challenges for machine learning techniques both in terms of size and complexity. Those challenges include: the structured data with var…
DiD-BCF model improves causal inference in panel data with robust non-parametric methods.
In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge in meta-learning is the task heterogeneity which cannot be well handled by traditional globally shared meta-learning methods. In addition, cu…
Bayesian optimization improves with transfer learning for aircraft design.
New algorithms for clustering and synthetic data generation of heterogeneous tabular datasets.
ARCO-BO optimizes multi-agent design under heterogeneity, improving efficiency and performance.
The paper introduces heterogeneous manifolds for better graph embeddings.
TIMME detects Twitter users' ideology from sparse, heterogeneous data.
Study finds risk sharing without convexity assumptions.
New GNN method detects money laundering in diverse customer relationships.
Study risk sharing with Lambda VaR under diverse beliefs.
Study tackles contamination and heterogeneity in multi-task learning, improving robustness and personalization.
Identifying behavior that is relatively invariant under different conditions is a challenging task in far-from-equilibrium complex systems. As an example of how the existence of a semi-invariant signature can be masked by the heterogeneity in the properties of the components comprising such systems, we consider the exc…
Survey of IoT recommendation systems and their limitations.
Convolutional neural networks (CNN) have recently achieved state-of-the-art results in various applications. In the case of image recognition, an ideal model has to learn independently of the training data, both local dependencies between the three components (R,G,B) of a pixel, and the global relations describing edge…
LESS combines local predictors for subsets to learn from heterogeneous input-output pairs.
Boosted tree method improves MTL in heterogeneous domains.
Paper proposes HIDAM model to improve MSE default risk assessment using heterogeneous information networks.
When faced with a supervised learning problem, we hope to have rich enough data to build a model that predicts future instances well. However, in practice, problems can exhibit predictive heterogeneity: most instances might be relatively easy to predict, while others might be predictive outliers for which a model train…
Unified survey of treatment effect heterogeneity and uplift modeling methods.
Proposes a new method to optimize graph neural network architectures on heterogeneous information networks.