ART improves transfer learning performance with robust theory and methods.
problem Improving performance of primary tasks using auxiliary data.
method Adaptive Robust Transfer Learning (ART) pipeline with theoretical guarantees.
result ART provides a provable theoretical guarantee for adaptive transfer and robustness.
UDA learns target domain from unlabeled data via source knowledge transfer.
problem Learning unlabeled target domain data.
method Transferring both source knowledge and target-relatedness.
result Improves UDA performance by simultaneous knowledge transfer.
AdaTrans adapts to feature and sample transfer in high-dimensional regression.
problem High-dimensional linear regression with more features than samples.
method F-AdaTrans and S-AdaTrans methods using fused-penalties and adaptive weights.
result AdaTrans achieves convergence rates close to oracle estimators and near-minimax optimal rates.
Adaptive Transfer Learning improves wind power prediction accuracy.
problem Efficiently predicting wind power using limited labeled data.
method Adaptive Transfer Learning in Deep Neural Networks for wind power prediction.
result Adaptive Transfer Learning improves prediction accuracy across different wind farms and task domains.
Paper proposes DDA for better transfer learning performance.
problem Domain discrepancy between source and target distributions.
method Dynamic Distribution Adaptation (DDA) to evaluate and adapt distribution importance.
result DDA improves transfer learning performance on various tasks.
Interventional domain adaptation improves feature transferability by removing spurious correlations.
problem Improper feature transferability due to spurious correlations in domain adaptation.
method Intervention strategy using unlabeled target data to generate counterfactual features and train discriminability invariance.
result Consistent performance improvements over state-of-the-art approaches in various domain adaptation tasks.
SATL adapts to varying smoothness in hypothesis transfer learning.
problem Fixed kernel regularization fails in varying smoothness settings.
method Proposes SATL, a two-phase KRR algorithm with adaptive Gaussian kernels.
result SATL achieves minimax optimality with matching upper and lower bounds.
New method tackles concept shifts in nonparametric regression using robust and adaptive transfer learning.
problem Concept shifts and sample scarcity in target domains hinder nonparametric regression.
method Robust and adaptive transfer learning procedure leveraging fixed bandwidth Gaussian kernels.
result Spectral algorithms with fixed bandwidth Gaussian kernels attain minimax convergence rates for nonparametric regression.
New estimator achieves minimax optimal risk in transfer learning.
problem Nonparametric regression with transfer learning.
method Confidence thresholding estimator and data-driven adaptive algorithm.
result Adaptive algorithm achieves minimax risk up to a logarithmic factor.
Transfer learning framework for fragility modeling under domain shift and class imbalance
problem Data gaps in structural fragility modeling
method Transfer learning
result Improves failure detection and predictive stability in low-data regimes
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.
Adaptive transfer learning improves time series classification.
problem Improving generalization in time series classification.
method Proposed a general adaptive transfer learning framework for multi-view time series data.
result Framework maintains desirable generalization ability.
DAAN dynamically adapts adversarial learning for better domain adaptation.
problem Dynamic evaluation of global vs local domain distributions for adversarial learning.
method Dynamic Adversarial Adaptation Network (DAAN) that dynamically learns domain-invariant representations.
result DAAN achieves better classification accuracy compared to state-of-the-art methods.
The paper proposes a uniformity regularization scheme to improve deep neural network transferability.
problem Improving deep neural network transferability and adaptation to new tasks.
method Introduces a uniformity regularization scheme to encourage high uniformity in embedding space.
result Uniformity regularization consistently offers benefits over baseline methods and achieves state-of-the-art performance in Deep Metric Learning and Meta-Learning.
Proposes a method to improve multi-output Gaussian process for transfer learning.
problem Negative transfer and domain inconsistency in multi-output Gaussian process.
method Regularized MGP with convolution process and domain adaptation.
result Outperforms state-of-the-art benchmarks in simulation and real-world studies.
New technique explains convergence in ML models with data modifications.
problem Understanding convergence of ML models under data changes.
method Analogue of Fatou's lemma and gamma-convergence.
result Relevance and applications in general ML tasks and domain adaptation.
Adaptive source selection for positive transfer in linear models improves target dataset performance.
problem Limited task-specific labeled data in business settings.
method Greedily decides from which sources and how many samples to incorporate into the target dataset using an accept/reject rule based on a data-dependent estimate of the transfer gain.
result Consistent gains over classical and recent strong baselines while avoiding negative transfer.
Domain adaptation and transfer learning help machine learning models adapt to new data distributions.
problem Machine learning models struggle with data distribution changes between training and test phases.
method Expands on risk minimization, discusses three types of data set shift, and various approaches to domain adaptation.
result Transfer learning and domain adaptation are essential for improving model performance in changing data environments.
DARec adapts rating patterns across domains without auxillary info.
problem Cross-domain recommendation challenges.
method Deep domain adaptation model (DARec) that transfers rating patterns.
result Best performance on public datasets.
Proposes a novel framework for unsupervised domain adaptation using causal representations.
problem Transferability of deep model representations across domains is limited.
method Integrates causal inference into deep learning pipeline for domain-invariant feature learning.
result Demonstrates superior performance in unsupervised domain adaptation using causal representations.
DANE adapts network embeddings across multiple domains.
problem Learning embeddings for multiple networks without transferability.
method Graph Convolutional Network with adversarial learning.
result DANE achieves superior performance in cross-network domain adaptation.
DARL framework tackles partial domain adaptation by selecting source instances for positive transfer.
problem Tackles the challenge of selecting source instances for positive transfer in partial domain adaptation.
method Proposes a Domain Adversarial Reinforcement Learning (DARL) framework that uses deep Q-learning and domain adversarial learning to select source instances and learn domain-invariant features.
result Demonstrates superior performance over existing methods for partial domain adaptation on several benchmark datasets.
Proposes TFDF to learn transferable and discriminative features for unsupervised domain adaptation.
problem Difficult to induce supervised classifier without labeled data in unsupervised domain adaptation.
method TFDF optimizes transferability and discriminability by aligning distributions and minimizing class confusion.
result TFDF achieves better performance on real-world datasets compared to existing methods.
AdaGCN transfers labels across networks via adversarial domain adaptation and graph convolution.
problem Cross-network node classification with limited labeled data.
method Adversarial domain adaptation and graph convolution.
result AdaGCN successfully transfers labels with low labeled data on source networks and significant domain divergence.
Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. Howeve…
The paper provides theoretical insights into deep domain adaptation.
problem Closing the gap between source and target domains in deep domain adaptation.
method A rigorous framework to explain transfer learning and minimize loss.
result First theoretical result characterizing joint space and transfer learning gain.
Single episode policy transfer in RL without access to rewards.
problem Performing near-optimally in a single attempt with unknown dynamics.
method Optimizes a probe and inference model to estimate latent variables for universal control policy.
result Significantly outperforms existing adaptive approaches in diverse domains.
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.
Adapters add few trainable parameters per task, improving NLP performance.
problem Parameter inefficiency in fine-tuning large pre-trained models for multiple downstream tasks.
method Adapter modules that add only a few trainable parameters per task, allowing for high parameter sharing and task extensibility.
result Adapters achieve near state-of-the-art performance with minimal additional parameters.
New findings on representation changes in transfer learning.
problem Understanding representation changes in transfer learning.
method Analysis of pre-trained and fine-tuned representations in transfer learning.
result Representation changes mainly occur in the head, but early layers are also important for cross-domain adaptation.
Adversarial adaptation improves reinforcement learning performance.
problem Slow training and exploration in reinforcement learning.
method Adversarial domain adaptation for feature initialization.
result Significant improvement in reinforcement learning performance.
New study reveals surprising adaptive rates in model selection for transfer learning.
problem Model selection in transfer learning with varying transfer distances.
method Theoretical analysis focusing on classification.
result Adaptive rates can be arbitrarily slower than oracle rates.
This paper proposes a new method to improve domain adaptation by distinguishing between marginal and dependence structure differences.
problem Existing domain adaptation methods fail to differentiate between marginal and dependence structure differences, leading to suboptimal transferability.
method The paper introduces a new approach that measures and optimizes the differences in internal dependence structure separately from marginals.
result The new method significantly improves transferability and robustness compared to existing benchmarks on real-world datasets.
Paper studies transfer learning for nonparametric classification, establishing rates and proposing adaptive classifiers.
problem Transfer learning in nonparametric classification under different distributions.
method Established minimax rates and proposed adaptive classifiers based on weighted K-NN approach.
result Data-driven adaptive classifier achieves near-optimal rates over various parameter spaces.
SpotTune adapts fine-tuning strategies per instance for improved transfer learning.
problem Improving transfer learning performance with deep neural networks.
method Adaptive fine-tuning approach using policy networks to decide whether to use pre-trained or fine-tuned layers.
result SpotTune outperforms traditional fine-tuning on 12 out of 14 standard datasets and achieves highest scores on Visual Decathlon.
Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domai…
PTF accelerates RL by reusing source policies without measuring task similarity.
problem Leveraging prior knowledge for faster RL.
method Adaptive Policy Transfer Framework (PTF) for RL.
result Significantly accelerates RL learning process and surpasses state-of-the-art methods.
The study analyzes transfer learning using information theory.
problem Transfer learning in different distributions.
method Information-theoretic analysis, focusing on KL divergence.
result Upper bounds for general transfer learning algorithms and specific ERM.
Adapts Neyman-Pearson classification for both source and target distribution shifts.
problem Minimizing errors while controlling both Type-I and Type-II errors under distribution shifts.
method Derives an adaptive procedure that guarantees improved error rates and adapts to uninformative sources.
result Automatic adaptation to uninformative sources avoids negative transfer.
Transfer learning for causal forest
problem Estimating CATE in a causal forest
method Offset method adapted to causal context
result Bound on CATE error
Machine learning models adapt to motor learning but face challenges.
problem Adapting machine learning to handle motor variability and differentiate new movements from known ones.
method Parameter adaptation, transfer and meta-learning, reinforcement learning.
result Challenges in applying machine learning models for motor learning support systems.
AMEAN tackles BTDA by learning meta-sub-targets to bridge domain gaps and misalignments.
problem Blending-target Domain Adaptation (BTDA) with multiple sub-targets that are hard to distinguish.
method AMEAN uses two adversarial processes: first to align source and mixed target domains, second to learn meta-sub-targets.
result AMEAN significantly outperforms existing DA algorithms in BTDA scenarios.
Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, whilst test data is sampled from a target domain. This paper proposes and studies an approach, called feature-level domain adaptation (FLDA), …
Flexible framework for transfer learning with optimal rates.
problem Inference about a target population using related source data.
method Adaptive transfer learning framework allowing covariate-dependent relationships.
result Achieves minimax optimal rates of convergence by adapting to transfer relationship.
Efficiently uses imitation, adaptation, and simulation to improve robot learning.
problem Improving efficiency in data collection and curation for autonomous systems.
method Utilizes imitation learning, domain adaptation, and transfer from simulation.
result Increased efficiency in data-driven approaches for robot learning.
A new method improves EEG classification across subjects efficiently.
problem Challenges in adapting and retaining knowledge for EEG classifiers across different subjects.
method Meta UPdate Strategy (MUPS-EEG) for continuous EEG classification.
result Outperforms current state-of-the-art methods in adapting to new subjects and retaining knowledge of learned subjects.
New method transfers causal mechanisms for few-shot domain adaptation.
problem Few labeled target domain data for regression problems.
method Mechanism transfer using structural equations in causal modeling.
result Method can adapt from apparently different distributions.
Domain-Adversarial Neural Networks improve fault diagnosis models across different machines.
problem Improving fault diagnosis models on new machines with limited labeled data.
method Domain-Adversarial Neural Networks (DANN) and other methods for domain adaptation.
result Unified experimental protocol for fair comparison of domain adaptation methods.