Paper tackles transferring knowledge for learning distance metrics across different domains.
problem Mitigating label information deficiency in target distance metric learning.
method Develops a flexible HTDML framework that uses knowledge fragments from source domain to help target metric learning.
result Reduces generalization error in the target domain using the proposed transfer strategy.
ATLAS separates invariant and transferable latent factors across diverse environments.
problem Transfer learning and robust prediction in heterogeneous environments.
method ATLAS leverages invariance principle to disentangle latent factors and uses auxiliary labels for robust prediction.
result Near-oracle performance and robust transferable prediction in new environments.
MPHD transfers knowledge across different domains for Bayesian optimization.
problem Optimizing functions with unknown or diverse domains.
method MPHD uses neural nets to map domain-specific contexts to GP specifications, enabling transfer learning across heterogeneous search spaces.
result MPHD improves black-box function optimization performance on diverse domains.
Bayesian optimization improves with transfer learning for aircraft design.
problem Cold start problem in Bayesian optimization for aircraft design.
method Ensemble of surrogate models using transfer learning in a constrained Bayesian optimization framework.
result Significant improvement in convergence and prediction accuracy.
Proposes a transfer learning method for high-dimensional quantile regression.
problem Inadequate handling of heterogeneity and heavy tails in transfer learning.
method High-dimensional quantile regression framework with double transfer learning estimator.
result Established error bounds and valid confidence intervals for high-dimensional quantile regression coefficients.
Develops a method to learn metrics across multiple domains using heterogeneous transfer learning.
problem Limited labeled data in target domain and heterogeneous data across multiple domains.
method HMTML framework that learns metrics and transformations across multiple domains, maximizing high-order covariance in a common subspace.
result Effective feature transformations and metrics learned across multiple domains, validated by extensive experiments.
HGKT transfers knowledge from seen to unseen classes in GZSL without prior unseen class info.
problem Learning to classify unseen classes in GZSL.
method Structured heterogeneous graph with graph neural network for knowledge transfer.
result Achieves state-of-the-art results on public benchmark datasets.
A novel transfer learning framework combines multiple data sources for PU learning.
problem Challenges in PU learning due to lack of negative labels and data scarcity.
method Model averaging of heterogeneous data sources, including binary labeled, semi-supervised, and PU data.
result Method outperforms other methods in predictive accuracy and robustness, especially under limited labeled data.
MetaPerturb learns to improve generalization across different tasks and architectures.
problem Improving generalization on unseen data for diverse tasks and architectures.
method MetaPerturb is a meta-learned set-based perturbation function that improves generalization performance across heterogeneous tasks and architectures.
result MetaPerturb significantly outperforms baselines on most tasks and architectures with minimal increase in parameter size and no hyperparameters to tune.
Transmeter quickly measures transferability between different datasets.
problem Measuring transferability between heterogeneous multivariate datasets.
method Pre-trained source model, adversarial network, and encoder-decoder architecture.
result Transmeter provides the most accurate transferability measurement up to 10.3 times faster.
FedZKT enables resource-constrained devices to participate in federated learning with heterogeneous models.
problem Inequality in resource allocation hinders participation from resource-constrained devices in federated learning.
method Zero-shot knowledge transfer through a server-assigned distillation process.
result FedZKT effectively transfers knowledge across heterogeneous on-device models without requiring comparable local training efforts.
Trans-GLMC tackles source heterogeneity in transfer learning for structured clusters.
problem Source heterogeneity makes it hard to use multiple related auxiliary sources effectively.
method Trans-GLMC constructs clusters of sources, then combines global fusion, within-cluster refinement, and target debiasing.
result Improves facility-specific prediction and identifies interpretable communities of hospitals with mutual transferability.
Adaptive transfer learning model for varying mechanisms across domains.
problem Improving inference in a target domain by leveraging related source domains with varying mechanisms.
method Semi-parametric domain-varying coefficient model (DVCM) for structured transfer learning.
result Minimax rate-optimal adaptive transfer learning estimator with provable negative transfer safeguards.
The paper improves classification accuracy by leveraging a shared signal across domains in high-dimensional classification.
problem Improving classification accuracy in high-dimensional data with shared signals across domains.
method Transfer learning for linear discriminant analysis, decomposing mean differences into common and domain-specific components.
result Deterministic limits for transfer performance, leading to optimal weights and corrections for bias.
A meta-learning approach for automatic knowledge transfer between networks.
problem Improving performance in small-data real-world problems with heterogeneous architectures and tasks.
method Meta-learning to automatically learn what knowledge to transfer and where in the target network.
result Meta-transfer approach significantly outperforms hand-crafted methods on various datasets and network architectures.
This survey reviews Heterogeneous Representation Learning (HRL) for diverse data types.
problem Challenges in learning from heterogeneous data types.
method Unified learning framework for modeling various learning settings.
result Unified mathematical framework for multi-view, transfer, privileged, and multi-task learning.
Proposes STN for HDA by aligning domain-shared classifier and subspace.
problem Learning in target domain using knowledge from heterogeneous source domain.
method End-to-end learning of domain-shared classifier and domain-invariant subspace; soft-label strategy for unlabeled data; adaptive coefficient for soft-labels.
result Significantly outperforms state-of-the-art approaches in various transfer tasks.
Transfer learning improves MNI's performance in high-dimensional linear regression.
problem Improving model performance in high-dimensional linear regression with diverse data.
method Proposes a Transfer MNI approach, analyzing its excess risk and conditions for outperformance.
result Identifies free-lunch covariate shift regimes where knowledge transfer benefits.
Paper tackles federated learning with privacy, enhancing target data analysis.
problem Heterogeneity and privacy of distributed data in federated learning.
method Formulates federated differential privacy, studies statistical problems under privacy constraints.
result Federated differential privacy offers a balance between privacy and knowledge transfer.
Improves search performance by transferring knowledge from recommender system.
problem Cold start and feedback loop problems in search retrieval.
method Zero-Shot Heterogeneous Transfer Learning framework.
result Significant improvements in relevance and user interactions over production system.
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…
Transfer learning improves loan recovery rate forecasting under data scarcity.
problem Data scarcity in loan portfolios limits RR modeling accuracy.
method Introduces FT-MDN-Transformer, a mixture-density tabular Transformer architecture for TL.
result FT-MDN-Transformer outperforms baseline models in RR forecasting, especially under covariate and conditional shifts.
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…
MicAugment transfers audio style from few seconds of input to match target conditions.
problem Audio model robustness to diverse acquisition conditions.
method Identifies and applies transformations learned from target audio to input audio.
result MicAugment significantly improves model robustness in downstream tasks.
Paper tackles nonparametric classification with privacy constraints, achieving optimal accuracy.
problem Nonparametric classification under distributed differential privacy constraints.
method Minimax and adaptive transfer learning, considering privacy, sample sizes, and heterogeneity.
result Developed an adaptive classifier achieving optimal misclassification rate with privacy constraints.
Paper tackles skill transfer in RL for morphologically different agents.
problem Transfer skills between morphologically different reinforcement learning agents.
method Proposes a paired variational encoder-decoder model (PVED) for subspace learning.
result Demonstrates improved skill transfer efficiency compared to state-of-the-art methods.
Paper proposes a method to improve DAG structure reconstruction using auxiliary DAGs.
problem Improving DAG structure reconstruction with limited data.
method Introduces structural similarity measures and a transfer learning framework.
result Significant improvement in DAG reconstruction, even with dissimilar auxiliary DAGs.
Framework improves target domain prediction using quantile matching.
problem Improving prediction accuracy in data-scarce target domains.
method Conditional quantile matching for distributional alignment.
result Empirical risk minimizer achieves tighter excess risk bound.
Paper proposes a transfer learning framework for tensor Gaussian graphical models.
problem Pooling heterogeneous tensor data for improved estimation and variable selection.
method Transfer learning framework that uses data-adaptive weights from auxiliary domains.
result Significant improvement in estimation errors and variable selection consistency.
ChemCPA predicts cellular responses to novel drugs using transfer learning.
problem Scaling high-throughput screens to measure cellular responses for many drugs is costly and challenging.
method ChemCPA, a new encoder-decoder architecture combined with transfer learning.
result Training on existing bulk RNA HTS datasets improves generalization performance, reducing the need for extensive single-cell screens.
Proposes a federated transfer learning method to improve precision medicine models for underrepresented populations.
problem Underrepresentation of minorities in precision medicine research leads to underperforming risk prediction models.
method Two-way federated transfer learning strategy integrating diverse populations and healthcare institutions.
result Improves risk prediction models for underrepresented populations, reducing performance gaps.
GDA-HIN adapts across heterogeneous networks by aligning shared and private node types.
problem Domain adaptation challenges in heterogeneous networks with shared and private node types.
method Generalized Domain Adaptive model across HINs (GDA-HIN) that aligns identical-type nodes and edges while utilizing different-type nodes and edges.
result GDA-HIN outperforms state-of-the-art methods in various domain adaptation tasks across heterogeneous networks.
A method for clustering using transfer learning from similar labeled data.
problem Clustering with datasets having different features and labeled data.
method Constructing meta-features to describe structural characteristics of data and transferring them between source and target domains.
result The method is efficient and works under arbitrary feature descriptions of source and target domains with smaller complexity.
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.
TransNet improves community detection on target networks using privacy-preserved source networks.
problem Community detection on sensitive network data with privacy constraints.
method Spectral clustering framework leveraging locally distributed privacy-preserved auxiliary networks via randomized response and adaptive weighting.
result TransNet delivers strong gains in community detection across various privacy levels and heterogeneity patterns.
Localized transfer learning improves nonparametric regression performance.
problem Improving nonparametric regression performance on target tasks.
method Localized transfer learning framework that models heterogeneity and partition covariate space into cells.
result Sharp minimax rates show local transfer mitigates the curse of dimensionality.
We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the sam…
Adaptive kernel approach learns causal effects from diverse data sources.
problem Learning causal effects from multiple, decentralized data sources in a federated setting.
method Adaptive transfer algorithm using Random Fourier Features to estimate similarities and disentangle loss function components.
result Empirically outperforms baselines on decentralized data sources with different distributions.
FedGradNorm improves federated MTL by balancing task learning speeds.
problem Statistical and data heterogeneity in federated MTL.
method Dynamic-weighting normalization of gradient norms.
result FedGradNorm achieves faster training performance and balanced datasets.
TL-ANDI distills context from source data to improve transfer learning for TFMs.
problem Limited transfer learning due to context-size constraints and distribution shifts.
method TL-ANDI uses posterior-aware distillation to construct a compact source context and locally distills labels.
result Improves transfer performance by addressing context-size and distribution shifts.
MetaDVFS uses device and application metadata to improve DVFS efficiency.
problem Improving energy efficiency in mobile platforms with diverse applications and hardware.
method Formulates DVFS as a multi-task reinforcement learning problem and introduces MetaDVFS, leveraging metadata for knowledge transfer.
result MetaDVFS achieves up to 26% improvement in Quality of Experience and up to 17% improvement in Performance-Power Ratio.
TradeFM learns market microstructure from trade events, improving financial model accuracy.
problem Lack of generalizable models for market microstructure.
method Generative Transformer model trained on billions of trade events, using scale-invariant features and universal tokenization.
result TradeFM generates rollouts that match key stylized facts of financial returns and outperforms existing models.
Study shows DL models trained on healthy subjects perform worse on patients' ECG data.
problem Inefficiency of DL models on heterogeneous datasets for heart beat detection.
method Investigated and evaluated the use of Transfer Learning to adapt DL models to different datasets.
result Transfer Learning improves classification performance on small sample size datasets.
Unified model detects transferable variables and source data in high-dimensional linear regression.
problem Scarcity of target data and heterogeneity of source and target data distributions.
method UTrans model, estimation error bounds, hypothesis testing for source detection.
result UTrans achieves lower estimation and prediction errors than existing methods.
Method learns feature map between source and target domains for high-dimensional regression with missing features.
problem High-dimensional regression with differing feature sets in target and source domains.
method First learns a feature map between missing and observed features using source data, then imputes missing features in target domain, and performs two-step transfer learning for penalized regression.
result Developed upper bounds on estimation and prediction errors for HTL, showing dependence on model complexity, sample size, feature map quality, and domain differences.
Fog learning distributes ML model training across heterogeneous devices and networks.
problem Challenges with conventional federated learning in heterogeneous networks.
method Intelligent distribution of ML model training across nodes from edge devices to cloud servers.
result Enhanced federated learning with multi-layer hybrid framework considering network, heterogeneity, and proximity.
Deep neural network predicts multiphase flow in heterogeneous domains.
problem Predicting multiphase flow in complex, heterogeneous systems.
method Deep neural network model for handling permeability heterogeneity and learning interplay of forces.
result Highly accurate predictions of CO2 saturation distribution with computational efficiency.
Modeling student behaviors and multiple predictions for early intervention.
problem Predicting student outcomes and interactions among multiple tasks.
method Proposes a variant of LSTM and soft-attention mechanism for heterogeneous behaviors, and co-attention mechanism for task interactions.
result Demonstrated effectiveness in predicting student outcomes and interactions.