Enhances transfer learning with semantic reasoning for robust predictions.
problem Improving robustness of transfer learning models.
method Integrates semantic representations for better knowledge transfer.
result Demonstrated robustness in bus delay and air quality forecasting.
This work shows how to use simulators to learn efficient exploration in real-world RL.
problem Sample complexity of real-world reinforcement learning.
method Coupling exploratory policies learned in simulators with practical approaches.
result Polynomial sample complexity in real world, exponential improvement over direct sim2real transfer.
Paper tackles sim-to-real transfer in continuous domains with partial observations.
problem Lack of theoretical foundation for sim-to-real transfer in continuous domains with partial observations.
method Developed a new algorithm for infinite-horizon average-cost LQGs and established a regret bound.
result A popular robust adversarial training algorithm can learn competitive policies from simulation to real-world environments.
We observe standard transfer learning can improve prediction accuracies of target tasks at the cost of lowering their prediction fairness -- a phenomenon we named discriminatory transfer. We examine prediction fairness of a standard hypothesis transfer algorithm and a standard multi-task learning algorithm, and show th…
Robots navigate wilderness trails using virtual-to-real-world transfer learning.
problem Autonomous navigation of outdoor trails is challenging due to lack of annotated training data.
method Virtual-to-real-world transfer learning with deep learning models trained on synthetic data.
result Classification accuracies of up to 95% on synthetic data and feasibility in real-world trails.
Study improves hypothesis transfer learning for functional linear models.
problem Incompatible TL techniques for high-dimensional FLR methods due to infinite-dimensional nature of functional data.
method Proposes two algorithms for hypothesis transfer learning in RKHS framework, leveraging RKHS distance and aggregation techniques.
result Establishes asymptotic lower bounds and matching upper bounds for the proposed algorithms, demonstrating their effectiveness.
Toolkit simplifies transfer learning for researchers.
problem Making transfer learning accessible to non-experts.
method Wraps 17 models, provides easy-to-use interfaces.
result Simplifies model selection for real-world applications.
This work improves GNN training efficiency by maximizing ego-graph information.
problem Training dedicated GNNs is costly for large-scale graphs.
method Proposes EGI (Ego-Graph Information maximization) to capture essential graph information and establish a theoretical framework for transfer learning.
result Demonstrates the effectiveness of EGI in improving GNN training efficiency and transferability.
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-item preference data. In many real-world applications, preference data are usually sparse, which would make models overfit and fail to give accurate predictions. Recently, several research works show that by transferring k…
Study measures impact of data and neural net similarity on transferability in restaurant sales data.
problem Identify indicators for successful transferability of neural nets across different data sets.
method Empirical study on sales data from six restaurants, calculating indicators based on data and neural net similarities.
result Negative correlations between transferability and indicators, allowing better model performance and fewer transfers.
Bayesian optimization adapts domain parameters for more robust robot policies.
problem Learning policies for robot control from simulation data often fails in the real world due to the 'reality gap'.
method Bayesian Domain Randomization (BayRn) uses Bayesian optimization to adapt domain parameter distributions during training.
result BayRn achieves better sim-to-real transfer compared to fixed distribution methods.
Survey examines challenges and solutions in sim-to-real transfer for robotics.
problem Challenges in transferring robotic systems from simulation to real-world environments.
method Leveraging techniques like domain randomization, real-to-sim transfer, state and action abstractions, and sim-real co-training.
result Promising results in closing the reality gap across various robotic domains.
Dreaming model enables robots to learn policies from images.
problem Learning to control robots based on images is challenging.
method Learning a future representation/scene regressor conditioned on robot actions.
result Dreaming model enables policy learning that transfers to real-world.
Paper presents new dataset for disentanglement learning from physical objects.
problem Transfer of disentanglement models from synthetic to real-world data.
method Developed a dataset of physical objects with controlled variations, used a robotic arm for precise manipulation.
result Disentanglement models perform poorly on real data but selection of models and hyperparameters improves transfer.
RHPO improves data-efficiency for hierarchical reinforcement learning.
problem High data requirements for general reinforcement learning algorithms in robotics.
method RHPO employs compositional inductive biases and task sharing mechanisms.
result RHPO enables stable and fast learning for complex domains with positive transfer.
Evaluates transfer learning methods in dynamic data availability scenarios.
problem Real-world data availability varies over time, leading to unrealistic TL method evaluations.
method Proposes a data manipulation framework to simulate varying data availability and domain transformations.
result Demonstrates the usefulness of the framework on proprietary and publicly available datasets.
How can we recognise social roles of people, given a completely unlabelled social network? We present a transfer learning approach to network role classification based on feature transformations from each network's local feature distribution to a global feature space. Experiments are carried out on real-world datasets.…
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.
Paper tackles artist style transfer using quadratic potential.
problem Applying a specific artist's style to real-world photographs.
method Adversarial training with quadratic potential divergence and integration of deep learning techniques.
result Demonstrates improved quality of artist stylized images.
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.
Study evaluates scalability and real-world impact of disentangled representations.
problem Scalability and real-world impact of disentangled representations.
method New high-resolution dataset and architectures for disentangled representation learning.
result Disentanglement predicts out-of-distribution task performance.
Develops variable-lag Granger causality and Transfer Entropy for time series analysis.
problem Fixed time delay assumption in Granger causality and Transfer Entropy does not hold in many applications.
method Variable-lag Granger causality and Transfer Entropy, using optimal warping path of Dynamic Time Warping (DTW).
result Proposed methods perform better than existing methods in both simulated and real-world datasets.
Paper evaluates membership inference attacks on transfer learning models.
problem Evaluating membership inference attacks on transfer learning models.
method Shadow model training strategy to derive data for membership inference classifier.
result Membership inference attacks can achieve effective performance against transfer learning models.
Transfer learning for causal forest
problem Estimating CATE in a causal forest
method Offset method adapted to causal context
result Bound on CATE error
Paper tackles multi-source transfer learning with diverse labeling volume and reliability.
problem Challenges in multi-source transfer learning with diverse labeling volume and reliability.
method Combines domain similarity and source reliability through a new transfer learning method, and integrates distribution matching and uncertainty sampling in pool-based active learning.
result Demonstrates superior performance over state-of-the-art transfer learning methods.
This paper introduces a new transfer learning method for regression.
problem Boosting predictive power in scenarios with scarce data.
method Affine model transfer, following expected-square loss minimization.
result The affine model transfer method encompasses various existing methods and clarifies its theoretical properties.
Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance. We propose a Bayesian transfer…
Paper proposes a novel online transfer learning method to reduce domain discrepancy.
problem Online transfer learning with online distribution discrepancy minimization.
method The method seeks a new feature representation to simultaneously reduce marginal and conditional distribution discrepancies.
result The proposed method outperforms state-of-the-art methods in comprehensive experiments.
This paper defines and quantifies transferability in domain generalization.
problem Understanding and quantifying transferability between domains.
method Formal definition and estimation of transferability, upper bound for target error.
result Many algorithms do not learn transferable features, proposing a new algorithm.
Deep features transfer well across datasets, improving generalization and training speed.
problem Understanding why deep features transfer well across different datasets.
method Analyzed transferability from improved generalization, optimization, and feasibility perspectives.
result Transferred models find flatter minima and have more favorable loss landscapes, leading to better training.
AutoTransfer improves subject transfer learning for biosignal datasets.
problem Subject transfer learning for challenging biosignal datasets.
method Regularization framework with mutual information or divergence penalties.
result Improves subject transfer learning performance on EEG, EMG, and ECoG datasets.
Paper improves sample efficiency of transfer learning in diffusion models.
problem Diffusion models need too much data to train from scratch.
method Assumes shared low-dimensional representation across tasks for improved sample efficiency.
result Sample complexity of target tasks can be reduced with a well-learned representation.
Proposes a transfer learning framework for sparse SIMs without raw source data.
problem Lack of direct access to raw source data and known link functions in transfer learning.
method Source-data-free framework based on SIM, using summary statistics and a multilayer perceptron.
result Consistent improvements over existing approaches in synthetic and real-world data.
New method automates asymmetric choice for better skill transfer in reinforcement learning.
problem Improving sample efficiency and transferability of reinforcement learning agents.
method Attentive Priors for Expressive and Transferable Skills (APES) using hierarchical KL-regularization.
result APES automates asymmetric choice, leading to better skill transfer across sequential tasks.
DVT transfers knowledge across domains using semi-supervised deep generative models.
problem Reduces labeling effort in domains with abundant labeled examples.
method Deep Variational Transfer (DVT) using a shared latent Gaussian mixture model.
result Achieves state-of-the-art classification performances across different datasets.
CLS measures dataset similarity through decision rule performance.
problem Measuring dataset similarity in machine learning, especially for transfer learning and domain adaptation.
method Cross-Learning Score (CLS) measures similarity through bidirectional generalization performance of decision rules, linking to cosine similarity under canonical linear models.
result CLS effectively measures dataset similarity and transferability, validated on synthetic and real-world datasets.
XMixup improves transfer learning accuracy by 1.9% with less training time.
problem Efficiently transfer knowledge from large source datasets to target tasks with small samples.
method Cross-domain Mixup technique that selects auxiliary samples from source datasets and augments training samples via mixup strategy.
result Improves accuracy by 1.9% on average over six real-world transfer learning datasets.
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.
Trans-GCR uses GCR model for node classification, providing theoretical guarantees and superior performance.
problem Challenges in obtaining node classification labels in real-world scenarios.
method Graph Convolutional Multinomial Logistic Regression (GCR) model and transfer learning method based on GCR.
result Trans-GCR provides superior empirical performance and theoretical guarantees.
Enhances VAR model estimation using transfer learning.
problem Estimating high-dimensional VAR models with temporal dependencies.
method Transfer learning for VAR models with low-rank and sparse structures.
result Theoretical guarantees for model parameter consistency and informative set selection.
Improves accuracy in image classification tasks by optimizing learning rates.
problem Learning from small amounts of labeled data in deep learning.
method Optimizes learning rates for neural network layers based on dataset parameters.
result Improvements in accuracy of 127% on ImageNet22k and Oxford Flowers datasets.
Paper extends transfer learning for decision rules, improving treatment rule estimation.
problem Estimating optimal individualized treatment rules under changing conditions.
method Bayes decision rules and low-dimensional empirical risk minimization.
result Consistent estimators and risk bounds established under mild conditions.
DM improves self-supervised transfer learning by matching target distributions.
problem Improving self-supervised transfer learning performance.
method Distribution Matching (DM) method that drives representation distribution towards a predefined reference distribution.
result DM outperforms existing methods on target classification tasks.
A new framework for robust transfer learning that avoids negative transfer in domains with unequal information.
problem Negative transfer in unsupervised domain adaptation, especially when source and target domains have different levels of informativeness.
method Decision-theoretic framework based on Le Cam's theory of statistical experiments, using constructive approximations to replace strict invariance with directional simulability.
result Le Cam Distortion achieves near-perfect frequency estimation and zero source utility loss in various domains, demonstrating superior performance compared to traditional methods.
Graph few-shot learning improves node classification with prior knowledge transfer.
problem Challenging semi-supervised node classification with few labeled nodes.
method Incorporates prior knowledge from auxiliary graphs into a transferable metric space.
result Improves classification accuracy on target graphs.
Enhances source domain knowledge with target data for transfer learning.
problem Limited data in target domains and rigid model assumptions in transfer learning.
method Transfer learning through Enhanced Sufficient Representation (TESR).
result TESR enhances source domain knowledge with target data, improving transfer learning performance.
Paper tackles hypothesis transfer learning for black-box models.
problem Difficult to build universal machine learning models across different institutions.
method Dynamic Knowledge Distillation (dkdHTL) with instance-wise weighting.
result Empirical results show the effectiveness of dkdHTL.
MANGA transfers policies across environments with varying dynamics and noise.
problem Transferring policies across multiple environments with different dynamics and motor noise.
method Decouples policy learning from system identification, trains dynamics-conditioned policies, and learns dynamics parameters from rollouts.
result Demonstrates effective transfer of learned policies across four MuJoCo agents using agnostic RL and imitation learning methods.