This study predicts parking availability using multi-source data and a self-supervised learning enhanced transformer.
problem Accurate parking availability prediction to support urban planning and management.
method Proposes SST-iTransformer, a self-supervised learning enhanced spatio-temporal inverted transformer, integrating multi-source data.
result SST-iTransformer achieves state-of-the-art performance in parking availability prediction.
Proposes LVGP for multi-source data fusion in science and engineering.
problem Differences in quality and comprehensiveness of data sources.
method Latent Variable Gaussian Process (LVGP) framework.
result Improved predictions for sparse-data problems.
AIMM-X monitors markets for suspicious behavior using transparent scoring.
problem Detecting market manipulation from benign mechanisms.
method Combines microstructure signals and public attention signals for anomaly detection.
result Transparent scoring allows tracing and understanding flagged windows.
Survey explores methods to adapt deep learning models across multiple labeled domains.
problem Difficulty in obtaining labeled data for deep learning models.
method Multi-source domain adaptation (MDA) to transfer knowledge from labeled to unlabeled or sparsely labeled target domains.
result MDA methods improve performance by minimizing domain shift.
We investigated the training of a shared model for both text-to-speech (TTS) and voice conversion (VC) tasks. We propose using an extended model architecture of Tacotron, that is a multi-source sequence-to-sequence model with a dual attention mechanism as the shared model for both the TTS and VC tasks. This model can a…
Proposes BONMI for integrating noisy matrices from multi-source data.
problem Integrating noisy matrices from multi-source data with block-wise missingness.
method Exploits orthogonal Procrustes problem to align eigenspaces and completes missing blocks.
result Statistical rate for eigenspace of underlying matrix comparable to independently missing assumption.
Proposes Textual Echo Cancellation to improve speech recognition.
problem Improving speech recognition performance and user experience for smart devices.
method A novel sequence-to-sequence model with multi-source attention that processes both the microphone mixture signal and source text of TTS playback.
result Demonstrates enhanced speech recognition performance and reduced latency.
European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at highspatial resolution and high revisit time, respectively, radar and optical imagesthat support a wide range of Earth surface monitoring tasks such as LandUse/Land Cover mapping. A long-standing challenge in the remote sensingcommunity is about…
Paper tackles multi-source domain adaptation for regression.
problem Predicting HDL cholesterol levels using gut microbiome data.
method Two-step procedure: 1) Extend a flexible single-source DA algorithm for classification to regression. 2) Augment with ensemble learning for multi-source DA.
result Consistent improvement in HDL cholesterol level prediction performance over existing methods.
EnMDAP aligns conditional distributions for multi-source domain adaptation using pseudolabels.
problem Training a target model with no labeled data in the absence of target data labels.
method EnMDAP uses label-wise moment matching and ensemble learning with multiple feature extractors.
result EnMDAP achieves state-of-the-art performance in multi-source domain adaptation tasks.
Enhances optimization in multi-source settings with causal principles.
problem Optimizing functions with multiple sources of data and causal dependencies.
method Integrates Multi-Source Bayesian Optimization with Causal Bayesian Optimization principles.
result Improves optimization efficiency and reduces computational complexity.
This work tackles robust multi-source domain adaptation under label shift.
problem Label shift and data contamination in multi-source domain adaptation.
method Domain-weighted empirical risk minimization framework with refinement procedure.
result The proposed method achieves superior performance in multi-category classification problems.
New framework tackles multi-source domain adaptation with optimism and consistency.
problem Adjusting mixture distribution weights and ensuring low error on target domain.
method Mildly optimistic objective function and consistency regularization.
result Beats current state of the art in multi-source domain adaptation.
A new method for analyzing multi-source, multi-way data reduces dimensionality and reveals shared and individual structures.
problem Analyzing multi-source, multi-way data from different high-throughput technologies.
method Multiple Linked Tensor Factorization (MULTIFAC) extending CP decomposition with L2 penalties and EM algorithm for incomplete data.
result MULTIFAC approximates underlying signal, identifies shared and unshared structures, and imputes missing data.
A decentralized approach for multi-source domain adaptation.
problem Transfer knowledge from multiple related domains to an unlabeled target domain.
method Federated Dataset Dictionary Learning (FedDaDiL) framework, eliminating central server, using Wasserstein barycenters.
result Our decentralized approach effectively adapts source domains to an unlabeled target domain.
The paper tackles uncertainty quantification in multi-source settings.
problem Uncertainty quantification under covariate shift is challenging in multi-source settings.
method The paper addresses this by proposing two extensions of weighted conformal prediction: merge-based aggregation and data-pooling.
result Theoretical guarantees are provided for the proposed approaches, and experiments validate their effectiveness.
Study evaluates cross-validation methods for clinical ECG classification, finding leave-source-out more reliable.
problem Overoptimistic cross-validation estimates for new patient sources.
method Empirical evaluation of K-fold and leave-source-out cross-validation methods.
result Leave-source-out cross-validation provides more reliable performance estimates.
SETrLUSI combines diverse knowledge from multiple domains for faster convergence.
problem Handling diverse knowledge from multiple domains in transfer learning.
method Stochastic Ensemble Multi-Source Transfer Learning Using Statistical Invariant (SETrLUSI).
result SETrLUSI accelerates convergence and outperforms related methods.
sJIVE combines structure and prediction in multi-source data.
problem Analyzing multi-source data with shared and unique structures.
method Supervised Joint and Individual Variation Explained (sJIVE) method.
result sJIVE outperforms existing methods in noisy data.
Paper tackles entity matching over multi-source data, optimizing alignment and mitigating negative transfer.
problem Learning effective entity matching models over multi-source large-scale data with relaxed assumptions.
method Proposes a Relaxed Multi-source Large-scale Entity-matching (RMLE) problem and Incentive Compatible Pareto Alignment (ICPA) method.
result Optimized cross-source alignments and mitigated negative transfer, improving entity matching accuracy.
Transformer models show robustness across domains with domain adversarial training.
problem Domain adaptation from multiple sources with no labeled data.
method Domain adversarial training and mixture of experts.
result Domain adversarial training improves representation but not performance.
Graph network predicts circRNA-disease associations using multi-source similarity features.
problem Identifying circRNA-disease associations is challenging and time-consuming.
method Proposes a graph convolution network framework using multi-source similarity information.
result Framework predicts circRNA-disease associations with promising results and outperforms existing methods.
Direct learning framework for integrating multi-source causal data.
problem Conditional average treatment effects inference from heterogeneous data.
method Direct learning framework, double robustness, causal information-aware weighting function.
result Effective causal data fusion in both homogeneous and heterogeneous scenarios.
Study shows multi-source learning is more resilient to adversarial corruption than single-source learning.
problem Learning from multiple untrusted data sources, especially when some are adversarially corrupted.
method Analyzed the scenario where an adversary can corrupt a fixed fraction of data sources, derived a generalization bound for this setting.
result PAC-learnability is possible in the multi-source setting even when some data sources are adversarially corrupted.
In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multi-source fusion scheme in Keyword Spotting (KWS) System for vehicle applications. Vehicle information, as a new source for the original system, is collected by an in-vehicle data acquisition platform while th…
Proposes a dynamic model for urban traffic volume prediction.
problem Urban traffic volume prediction for better traffic management and driver planning.
method Combines bidirectional LSTM, attention mechanism, and external features.
result Improves prediction precision by 3-7 percent on NYC-Taxi and NYC-Bike datasets.
Paper proposes SOTL framework for improving transfer learning accuracy and efficiency.
problem Statistical bias and computational efficiency in multi-source domain adaptation.
method Sparse Optimization for Transfer Learning (SOTL) with L0-regularization.
result SOTL significantly improves estimation accuracy and computational speed, especially under adversarial conditions.
Paper tackles online multi-source domain adaptation using Gaussian mixtures and dictionary learning.
problem Adapting multiple, heterogeneous source domains to a target domain in a streaming fashion.
method Introduces a novel approach for online fitting of Gaussian Mixture Models based on Wasserstein geometry, combined with dataset dictionary learning.
result Demonstrates ability to adapt 'on the fly' to target domain data streams.
Learning from multiple sources of information is an important problem in machine-learning research. The key challenges are learning representations and formulating inference methods that take into account the complementarity and redundancy of various information sources. In this paper we formulate a variational autoenc…
Novel bounds for deep MDA algorithms improve performance and efficiency.
problem Improving performance of MDA algorithms with few target labels and pseudo labels.
method Information-theoretic tools and novel deep MDA algorithm.
result Algorithm-dependent generalization bounds for MDA.
The paper tackles distribution-free prediction intervals for multi-source data.
problem Challenges in achieving valid inferences due to distribution shifts and privacy concerns.
method Derives efficient influence functions, incorporates machine learning, and proposes data-adaptive strategies.
result Achieves parametric rates of convergence to nominal coverage probabilities for prediction intervals.
Modeling wildfire aerosols using satellite data to predict solar radiation reduction.
problem Accurately estimate and predict AOD propagation from wildfires using multi-source satellite data.
method Physics-informed statistical modeling integrating multi-source satellite data with an advection-diffusion equation.
result The proposed approach accurately predicts AOD propagation and demonstrates model interpretability.
New PCA method handles multiple datasets and detects sparse patterns robustly.
problem Handling multi-source data with sparse and outlier-robust PCA.
method Developed a regularization problem with a penalty for structured sparsity and outlier resistance.
result The method detects global and local patterns across multiple data sources robustly.
In this paper, we propose to tackle the problem of reducing discrepancies between multiple domains referred to as multi-source domain adaptation and consider it under the target shift assumption: in all domains we aim to solve a classification problem with the same output classes, but with labels' proportions differing…
Paper tackles adapting multiple domains to a target domain using distillation and dictionary learning.
problem Adapting multiple heterogeneous labeled source domains to an unlabeled target domain.
method Combines Multi-Source Domain Adaptation and Dataset Distillation with Dataset Dictionary Learning.
result Achieves state-of-the-art adaptation performance even with minimal labeled data.
Paper improves volatility forecasting for new issues and spin-offs.
problem Forecasting volatility with limited historical data.
method Multi-source transfer learning approach.
result Transfer learning approach outperforms alternative models.
CWAN tackles multi-source heterogeneous domain adaptation with conditional weighting.
problem Learning cross-domain samples from multiple heterogeneous domains.
method CWAN uses a feature transformer, label classifier, and domain discriminator to learn from multiple sources.
result CWAN outperforms state-of-the-art methods on four real-world datasets.
SIG model identifies invariant variables for MSDA with fewer domain constraints.
problem Challenges in enforcing minimal changes across domains for MSDA.
method Subspace identification theory and variational inference.
result SIG model outperforms existing techniques on various benchmark datasets.
Deep learning system improves accuracy of food packaging date verification.
problem Improper labeling of food packaging poses health risks.
method Multi-source deep domain adaptation for domain-invariant representations and class boundary alignment.
result Significant improvement in classification accuracy of use-by date verification.
Paper tackles MSDA with GMMs and OT, improving over prior art.
problem Adapting multiple heterogeneous source measures to a target measure.
method Optimal Transport between Gaussian Mixture Models, with novel barycenter calculation.
result Improves image classification and fault diagnosis benchmarks.
The paper tackles MSDA by learning dictionary atoms in Wasserstein space.
problem Mitigating data distribution shifts across multiple source domains to target domain.
method Dictionary learning and optimal transport in Wasserstein space; DaDiL algorithm for learning.
result Improved classification performance by 3.15%, 2.29%, and 7.71% in benchmarks.
The investment on the stock market is prone to be affected by the Internet. For the purpose of improving the prediction accuracy, we propose a multi-task stock prediction model that not only considers the stock correlations but also supports multi-source data fusion. Our proposed model first utilizes tensor to integrat…
Bayesian model predicts iron deficiency from multi-source multi-way molecular data.
problem Predicting iron deficiency in rhesus monkeys from multi-source multi-way molecular data.
method Developed a Bayesian approach with a linear model incorporating multi-way dependence and varying signal sizes across sources.
result Model accurately classifies iron deficiency in monkeys and outperforms simpler models.
Novel hyperparameter optimization for target tasks under covariate shift.
problem Hyperparameter optimization under multi-source covariate shift.
method Construct variance reduced estimator to unbiasedly approximate target objective; propose no-regret hyperparameter optimization procedure.
result Proposed framework broadens applications of automated hyperparameter optimization.
The paper develops methods to identify stable associations across multiple studies.
problem Identifying stable associations across multiple studies with possible distributional shifts.
method Modeling heterogeneous multi-source data with multiple high-dimensional regressions and devising a novel sampling method for valid confidence intervals of maximin effects.
result Significant maximin effects indicate stable associations that can be generalized to target populations.
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to individual domains. Our approach employs multiple normalization methods while learning separate affine parameters per domain. For each domain,…
A new method for drone-based geo-localization using style and spatial alignment.
problem Geo-localization of drone-view images with satellite-view images using pre-annotated GPS tags.
method Orientation-based method to align patterns, new branch to extract aligned partial features, style alignment strategy.
result The proposed method outperforms state-of-the-art alternatives in geo-localization accuracy.
Study optimizes data collection from biased, costly sources to minimize risk.
problem Estimating population means and group-conditional means from multiple sources with varying costs and biases.
method Develops a sampling plan that maximizes effective sample size, paired with a post-stratification estimator.
result Achieves budgeted minimax optimal risk for estimating population means and group-conditional means.