arXiv research

Study evaluates cross-validation methods for clinical ECG classification, finding leave-source-out more reliable.

Paper improves volatility forecasting for new issues and spin-offs.

New method restores source features for SFDA without source data.

New technique for multiple-source adaptation without density estimation.

Adaptive kernel approach learns causal effects from diverse data sources.

We aim to understand the value of additional labeled or unlabeled target data in transfer learning, for any given amount of source data; this is motivated by practical questions around minimizing sampling costs, whereby, target data is usually harder or costlier to acquire than source data, but can yield better accurac…

Provides a compendium of data sources for various applications.

Study shows algorithms benefit from limited target data with many source domains.

In this paper, a new approach for classification of target task using limited labeled target data as well as enormous unlabeled source data is proposed which is called self-taught learning. The target and source data can be drawn from different distributions. In the previous approaches, covariate shift assumption is co…

Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the even…

New method combines multiple data sources for optimal decision-making with limited outcomes.

In data stream mining, predictive models typically suffer drops in predictive performance due to concept drift. As enough data representing the new concept must be collected for the new concept to be well learnt, the predictive performance of existing models usually takes some time to recover from concept drift. To spe…

Much information available on the web is copied, reused or rephrased. The phenomenon that multiple web sources pick up certain information is often called trend. A central problem in the context of web data mining is to detect those web sources that are first to publish information which will give rise to a trend. We p…

New ICA method for sources with mixed spectra.

New PCA method handles multiple datasets and detects sparse patterns robustly.

Bayesian approach uses generative models as priors for better source separation.

New method disentangles sources of different timescales in planetary seismic data.

We study the issue of PAC-Bayesian domain adaptation: We want to learn, from a source domain, a majority vote model dedicated to a target one. Our theoretical contribution brings a new perspective by deriving an upper-bound on the target risk where the distributions' divergence---expressed as a ratio---controls the tra…

We are losing biodiversity at an unprecedented scale and in many cases, we do not even know the basic data for the species. Traditional methods for wildlife monitoring are inadequate. Development of new computer vision tools enables the use of images as the source of information about wildlife. Social media is the rich…

A new method for analyzing multi-source, multi-way data reduces dimensionality and reveals shared and individual structures.

New method uses neural networks to identify sources from limited data in complex systems.

FinGPT is an open-source financial LLM for democratizing financial data.

New method improves multi-fidelity Bayesian optimization by accounting for local correlations and varying noise.

Paper tackles robust transfer learning with unreliable source data.

Study improves model fit by transferring info from related datasets.

Clinical AI models fail to transfer between sites due to site-specific practices.

Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a two-layer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality i…

Algorithm adapts pretrained semantic segmentation models to new domains.

Multi-source transfer learning has been proven effective when within-target labeled data is scarce. Previous work focuses primarily on exploiting domain similarities and assumes that source domains are richly or at least comparably labeled. While this strong assumption is never true in practice, this paper relaxes it a…

Method transfers knowledge without label overlap, source data, or target architecture consistency.

LMGPs enable efficient, accurate data fusion across multiple data sources.

The paper develops methods to identify stable associations across multiple studies.

Improves generalization with few samples using a new regularization method.

Study when to replace machine learning models with new data.

New method reduces inference variance for faster optimization.

Gravitational wave memory increases faster than Brownian motion in early universe and astrophysical sources.

Proposes LVGP for multi-source data fusion in science and engineering.

This paper improves stock price prediction using multimodal data.

New method estimates rate-distortion function using optimal transport.

Alternative app data improves credit scoring for underserved borrowers.

A new era in radioastronomy will begin with the upcoming large-scale surveys planned at the Australian Square Kilometre Array Pathfinder (ASKAP). ASKAP started its Early Science program in October 2017 and several target fields were observed during the array commissioning phase. The SCORPIO field was the first observed…

We consider the problem of locating a point-source heart arrhythmia using data from a standard diagnostic procedure, where a reference catheter is placed in the heart, and arrival times from a second diagnostic catheter are recorded as the diagnostic catheter moves around within the heart. We model this situation as a …

New method uses correlation-ratio for transfer learning, improving target model inference.

New method improves source separation using adversarial NMF.

Framework reuses pre-trained models for data-free transfer learning.

BeST metric selects best source models for transfer learning.

CoDATS improves DA on time series data with weak supervision.

The paper tackles domain generalization using functional regression.