arXiv Machine Learning
Jun 9
Transfer learning for causal forest
arXiv:2606. 07693v1 Announce Type: cross Abstract: Transfer learning addresses the challenge of transfering knowledge from one domain to another.
Transfer Learning of CATE with Kernel Ridge Regression
arXiv:2502. 11331v4 Announce Type: replace-cross Abstract: The proliferation of data has sparked significant interest in leveraging findings from one study to estimate treatment effects in a different target population without direct outcome observations.
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arXiv Machine Learning
6d ago
Heterogeneous transfer learning for high-dimensional regression with feature mismatch
arXiv:2412. 18081v3 Announce Type: replace-cross Abstract: We study Heterogeneous Transfer Learning (HTL) for high-dimensional regression with differing feature sets.
arXiv Machine Learning
Jul 1
When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts
arXiv:2507. 14661v2 Announce Type: replace-cross Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiting abundant source and unlabeled target data.
arXiv Machine Learning
Jul 7
Transfer Learning in High-dimensional Ising Models
arXiv:2607. 03005v1 Announce Type: new Abstract: In high-dimensional Ising model estimation, target sample sizes are often limited, and effectively using auxiliary binary datasets of unknown relevance remains challenging.
arXiv Machine Learning
Jun 4
Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects
arXiv:2604. 00915v2 Announce Type: replace Abstract: Estimation of heterogeneous long-term treatment effects (HLTEs) is relevant for personalized decision-making in marketing, economics, and medicine, where short-term observational datasets are often combined with long-term observational datasets.
arXiv AI
Aug 6
Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction
arXiv:2605. 24212v2 Announce Type: replace-cross Abstract: Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain.
arXiv Machine Learning
Jul 28
Context-Adaptive Inference: A Unified Statistical and Foundation-Model View
arXiv:2607. 23304v1 Announce Type: cross Abstract: Modern predictive systems are expected to adapt their behavior to the specific situation they are facing.
arXiv Machine Learning
1d ago
Inferential Evaluation of Surrogate-Derived Models under Covariate Shift
arXiv:2608. 15783v1 Announce Type: cross Abstract: In transfer-learning settings, a model derived from abundant surrogate labels may be deployed in a target population where gold-standard outcomes are unobserved.
arXiv Machine Learning
Jul 7
Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation
arXiv:2303. 08777v3 Announce Type: replace-cross Abstract: Cross-validation is one of the most widely used tools for risk estimation and model selection in statistics and machine learning, yet its theoretical properties when embedded in a learning procedure remain insufficiently understood.
arXiv Machine Learning
Jun 5
Harnessing Source Heterogeneity for Cluster-Structured Transfer Learning
arXiv:2606. 05258v1 Announce Type: cross Abstract: Transfer learning is a natural strategy when a target population has limited data but multiple related auxiliary sources are available.
arXiv Machine Learning
Jul 17
Learning Who to Treat When Treatment is Missing
arXiv:2607. 14346v1 Announce Type: new Abstract: Policy learning methods are increasingly used to inform treatment allocation under budget constraints.
arXiv Machine Learning
Jul 10
Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment
arXiv:2603. 19186v3 Announce Type: replace Abstract: Randomized controlled trials (RCTs) are the gold standard for estimating treatment effects, yet they are often underpowered for detecting effect heterogeneity.