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Tag: transfer

  • Optimizing Data Transfer in Distributed AI/ML Training Workloads

    Optimizing Data Transfer in Distributed AI/ML Training Workloads A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems – part 3 The post Optimizing Data Transfer in Distributed AI/ML Training Workloads appeared first on Towards Data Science. Chaim Rand Go to original source

  • Classification Imbalance as Transfer Learning

    Classification Imbalance as Transfer Learning arXiv:2601.10630v1 Announce Type: new Abstract: Classification imbalance arises when one class is much rarer than the other. We frame this setting as transfer learning under label (prior) shift between an imbalanced source distribution induced by the observed data and a balanced target distribution under which performance is evaluated. Within this…

  • Optimizing Data Transfer in Batched AI/ML Inference Workloads

    Optimizing Data Transfer in Batched AI/ML Inference Workloads A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems – part 2 The post Optimizing Data Transfer in Batched AI/ML Inference Workloads appeared first on Towards Data Science. Chaim Rand Go to original source

  • Optimizing Data Transfer in AI/ML Workloads

    Optimizing Data Transfer in AI/ML Workloads A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems The post Optimizing Data Transfer in AI/ML Workloads appeared first on Towards Data Science. Chaim Rand Go to original source

  • Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learning

    Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learning arXiv:2512.16489v1 Announce Type: new Abstract: Generalizing causal knowledge across diverse environments is challenging, especially when estimates from large-scale datasets must be applied to smaller or systematically different contexts, where external validity is critical. Model-based estimators of individual treatment…

  • SETrLUSI: Stochastic Ensemble Multi-Source Transfer Learning Using Statistical Invariant

    SETrLUSI: Stochastic Ensemble Multi-Source Transfer Learning Using Statistical Invariant arXiv:2509.15593v1 Announce Type: new Abstract: In transfer learning, a source domain often carries diverse knowledge, and different domains usually emphasize different types of knowledge. Different from handling only a single type of knowledge from all domains in traditional transfer learning methods, we introduce an ensemble learning…

  • Robust Data Fusion via Subsampling

    Robust Data Fusion via Subsampling arXiv:2508.12048v1 Announce Type: new Abstract: Data fusion and transfer learning are rapidly growing fields that enhance model performance for a target population by leveraging other related data sources or tasks. The challenges lie in the various potential heterogeneities between the target and external data, as well as various practical concerns…

  • Transfer Learning for Matrix Completion

    Transfer Learning for Matrix Completion arXiv:2507.02248v1 Announce Type: new Abstract: In this paper, we explore the knowledge transfer under the setting of matrix completion, which aims to enhance the estimation of a low-rank target matrix with auxiliary data available. We propose a transfer learning procedure given prior information on which source datasets are favorable. We…

  • A Transfer Learning Framework for Multilayer Networks via Model Averaging

    A Transfer Learning Framework for Multilayer Networks via Model Averaging arXiv:2506.12455v1 Announce Type: new Abstract: Link prediction in multilayer networks is a key challenge in applications such as recommendation systems and protein-protein interaction prediction. While many techniques have been developed, most rely on assumptions about shared structures and require access to raw auxiliary data, limiting…

  • Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning

    Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning arXiv:2504.16192v1 Announce Type: cross Abstract: The continuous improvement in weather forecast skill over the past several decades is largely due to the increasing quantity of available satellite observations and their assimilation into operational forecast systems. Assimilating these observations requires observation operators in the form…

  • Transfer Learning for High-dimensional Reduced Rank Time Series Models

    Transfer Learning for High-dimensional Reduced Rank Time Series Models arXiv:2504.15691v1 Announce Type: new Abstract: The objective of transfer learning is to enhance estimation and inference in a target data by leveraging knowledge gained from additional sources. Recent studies have explored transfer learning for independent observations in complex, high-dimensional models assuming sparsity, yet research on time…

  • Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression

    Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression arXiv:2501.10870v1 Announce Type: new Abstract: When concept shifts and sample scarcity are present in the target domain of interest, nonparametric regression learners often struggle to generalize effectively. The technique of transfer learning remedies these issues by leveraging data or pre-trained models from similar…

  • Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement Learning

    Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement Learning arXiv:2501.04870v1 Announce Type: new Abstract: In dynamic decision-making scenarios across business and healthcare, leveraging sample trajectories from diverse populations can significantly enhance reinforcement learning (RL) performance for specific target populations, especially when sample sizes are limited. While existing transfer learning methods primarily focus on linear regression settings,…

  • Transfer Neyman-Pearson Algorithm for Outlier Detection

    Transfer Neyman-Pearson Algorithm for Outlier Detection arXiv:2501.01525v1 Announce Type: cross Abstract: We consider the problem of transfer learning in outlier detection where target abnormal data is rare. While transfer learning has been considered extensively in traditional balanced classification, the problem of transfer in outlier detection and more generally in imbalanced classification settings has received less…

  • Heterogeneous transfer learning for high dimensional regression with feature mismatch

    Heterogeneous transfer learning for high dimensional regression with feature mismatch arXiv:2412.18081v1 Announce Type: new Abstract: We consider the problem of transferring knowledge from a source, or proxy, domain to a new target domain for learning a high-dimensional regression model with possibly different features. Recently, the statistical properties of homogeneous transfer learning have been investigated. However,…

  • A Note on Estimation Error Bound and Grouping Effect of Transfer Elastic Net

    A Note on Estimation Error Bound and Grouping Effect of Transfer Elastic Net arXiv:2412.01010v1 Announce Type: new Abstract: The Transfer Elastic Net is an estimation method for linear regression models that combines $ell_1$ and $ell_2$ norm penalties to facilitate knowledge transfer. In this study, we derive a non-asymptotic $ell_2$ norm estimation error bound for the…