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.
GDA-HIN adapts across heterogeneous networks by aligning shared and private node types.
Domain adaptation leverages the knowledge in one domain - the source domain - to improve learning efficiency in another domain - the target domain. Existing heterogeneous domain adaptation research is relatively well-progressed, but only in situations where the target domain contains at least a few labeled instances. I…
CWAN tackles multi-source heterogeneous domain adaptation with conditional weighting.
Paper introduces a new model to handle multi-task learning across different input domains.
CDSPP learns domain-specific projections for heterogeneous domain adaptation.
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 …
CCs learn high-dimensional distributions from heterogeneous data.
MPHD transfers knowledge across different domains for Bayesian optimization.
The paper improves classification accuracy by leveraging a shared signal across domains in high-dimensional classification.
Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain. In this paper, we propose a Soft Transfer Network (STN), which jointly learns a domain-shared classifier and a domain-invariant subspace in an end-to-end manner, for a…
Develops wcPCA for better low-rank approximations in heterogeneous domains.
Extends SW and GSW to compare heterogeneous joint distributions.
Adaptive transfer learning model for varying mechanisms across domains.
Boosted tree method improves MTL in heterogeneous domains.
The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques alleviate this, but need some instances from the target domain to drive adaptation. Domain generalisation is the recently topical problem of lear…
Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource constraints. Due to the large-scale and dynamic nature of resource coordination in hospitals and factories, human domain experts manually plan a…
We propose a representation learning framework for medical diagnosis domain. It is based on heterogeneous network-based model of diagnostic data as well as modified metapath2vec algorithm for learning latent node representation. We compare the proposed algorithm with other representation learning methods in two practic…
The goal of transfer learning is to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. In this paper, we examine the problem of transfer distance metric learning (DML), which usually aims to mitigate the label information deficiency issue in t…
Framework integrates mental disorder measurements for personalized treatment.
A novel transfer learning framework combines multiple data sources for PU learning.
Previous transfer learning methods based on deep network assume the knowledge should be transferred between the same hidden layers of the source domain and the target domains. This assumption doesn't always hold true, especially when the data from the two domains are heterogeneous with different resolutions. In such ca…
A new distance for mixed-variable, hierarchical datasets with meta variables.
We introduce reinforcement learning for heterogeneous teams in which rewards for an agent are additively factored into local costs, stimuli unique to each agent, and global rewards, those shared by all agents in the domain. Motivating domains include coordination of varied robotic platforms, which incur different costs…
Framework improves target domain prediction using quantile matching.
CICME estimates common and domain-specific causal mechanisms from multi-sensor data.
Domain adaptation aims to assist the modeling tasks of the target domain with knowledge of the source domain. The two domains often lie in different feature spaces due to diverse data collection methods, which leads to the more challenging task of heterogeneous domain adaptation (HDA). A core issue of HDA is how to pre…
Study improves predictive models for ICU data across hospitals.
Framework assesses variable importance for heterogeneous treatment effects.
MTL2L learns to adapt optimisation rules for unseen data.
Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to underst…
Surveying joint Gaussian graphical models to identify shared structures across domains.
Paper proposes a transfer learning framework for tensor Gaussian graphical models.
While deep learning models become more widespread, their ability to handle unseen data and generalize for any scenario is yet to be challenged. In medical imaging, there is a high heterogeneity of distributions among images based on the equipment that generates them and their parametrization. This heterogeneity trigger…
Understanding learning materials (e.g. test questions) is a crucial issue in online learning systems, which can promote many applications in education domain. Unfortunately, many supervised approaches suffer from the problem of scarce human labeled data, whereas abundant unlabeled resources are highly underutilized. To…
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 …
Develops scalable model for learning velocity fields in complex traffic scenarios.
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.…
Geometric Graph Alignment enhances IoT intrusion detection using NID data.
MetaPerturb learns to improve generalization across different tasks and architectures.
Proposes a transfer learning method for high-dimensional quantile regression.
Proposes a convex model for mixed logit to handle individual heterogeneity.
Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain via sparse domain transforms and propose a latent probit model (LPM) to jointly learn the domain t…
SHIFT method optimally estimates heterogeneous discrete distributions with limited communication.
Smile-GANs clusters brain MRI scans to reveal disease subtypes and progression.
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…
DUET enhances multivariate time series forecasting by clustering time and channels.
Transfer learning improves loan recovery rate forecasting under data scarcity.
A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e.g., sunny weather) for achieving high performance on the test data in a target domain (e.g., rainy weather). Whether the target contains a single homogeneous domain or multiple heterogeneous domains, existing wor…