Tessellation Localized Transfer learning for nonparametric regression

• URL: http://arxiv.org/abs/2601.00987v1
• Date: Fri, 02 Jan 2026 20:58:05 GMT
• Title: Tessellation Localized Transfer learning for nonparametric regression
• Authors: Hélène Halconruy, Benjamin Bobbia, Paul Lejamtel,
• Abstract summary: Transfer learning aims to improve performance on a target task by leveraging information from related source tasks.<n>We propose a nonparametric regression transfer learning framework that explicitly models heterogeneity in the source-target relationship.
• Score: 0.764671395172401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer learning aims to improve performance on a target task by leveraging information from related source tasks. We propose a nonparametric regression transfer learning framework that explicitly models heterogeneity in the source-target relationship. Our approach relies on a local transfer assumption: the covariate space is partitioned into finitely many cells such that, within each cell, the target regression function can be expressed as a low-complexity transformation of the source regression function. This localized structure enables effective transfer where similarity is present while limiting negative transfer elsewhere. We introduce estimators that jointly learn the local transfer functions and the target regression, together with fully data-driven procedures that adapt to unknown partition structure and transfer strength. We establish sharp minimax rates for target regression estimation, showing that local transfer can mitigate the curse of dimensionality by exploiting reduced functional complexity. Our theoretical guarantees take the form of oracle inequalities that decompose excess risk into estimation and approximation terms, ensuring robustness to model misspecification. Numerical experiments illustrate the benefits of the proposed approach.

Related papers

• TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training [53.93696896939915]
  Training tool-use agents typically rely on Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected tasks.<n>We propose TopoCurate, an interaction-aware framework that projects multi-trial rollouts from the same task into a unified semantic quotient topology.<n>TopoCurate achieves consistent gains of 4.2% (SFT) and 6.9% (RL) over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-03-02T10:38:54Z)
• Minimax optimal adaptive structured transfer learning through semi-parametric domain-varying coefficient model [9.091986429838117]
  We study a multi-source, single-target transfer learning problem under conditional distributional drift.<n>We develop an adaptive transfer learning estimator that selectively borrows strength from informative source domains.
  arXiv  Detail & Related papers  (2026-02-20T03:53:06Z)
• Transfer Learning Through Conditional Quantile Matching [3.86972243789112]
  We introduce a transfer learning framework for regression that leverages heterogeneous source domains to improve predictive performance in a data-scarce target domain.<n>Our approach learns a conditional generative model separately for each source domain and calibrates the generated responses to the target domain via conditional quantile matching.
  arXiv  Detail & Related papers  (2026-02-02T17:19:55Z)
• A Turn Toward Better Alignment: Few-Shot Generative Adaptation with Equivariant Feature Rotation [67.2019317630466]
  Few-shot image generation aims to effectively adapt a source generative model to a target domain using very few training images.<n>We propose Equivariant Feature Rotation (EFR), a novel adaptation strategy that aligns source and target domains at two complementary levels.<n>Our method significantly enhances the generative performance within the targeted domain.
  arXiv  Detail & Related papers  (2025-12-24T13:48:22Z)
• Heterogeneous transfer learning for high dimensional regression with feature mismatch [1.6385815610837167]
  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.<n>Most homogeneous transfer and multi-task learning methods assume that the target and proxy domains have the same feature space.<n>We propose a two-stage method that involves learning the relationship between the missing and observed features through a projection step in the proxy data.
  arXiv  Detail & Related papers  (2024-12-24T01:29:31Z)
• PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings [55.55445978692678]
  PseudoNeg-MAE enhances global feature representation of point cloud masked autoencoders by making them both discriminative and sensitive to transformations.<n>We propose a novel loss that explicitly penalizes invariant collapse, enabling the network to capture richer transformation cues while preserving discriminative representations.
  arXiv  Detail & Related papers  (2024-09-24T07:57:21Z)
• Time-series Generation by Contrastive Imitation [87.51882102248395]
  We study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy. At inference, the learned policy serves as the generator for iterative sampling, and the learned energy serves as a trajectory-level measure for evaluating sample quality.
  arXiv  Detail & Related papers  (2023-11-02T16:45:25Z)
• Robust Transfer Learning with Unreliable Source Data [11.813197709246289]
  We introduce a novel quantity called the ''ambiguity level'' that measures the discrepancy between the target and source regression functions.<n>We propose a simple transfer learning procedure, and establish a general theorem that shows how this new quantity is related to the transferability of learning.
  arXiv  Detail & Related papers  (2023-10-06T21:50:21Z)
• Compressed Regression over Adaptive Networks [58.79251288443156]
  We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
  arXiv  Detail & Related papers  (2023-04-07T13:41:08Z)
• Estimation and inference for transfer learning with high-dimensional quantile regression [3.4510296013600374]
  We propose a transfer learning procedure in the framework of high-dimensional quantile regression models. We establish error bounds of transfer learning estimator based on delicately selected transferable source domains. By adopting data-splitting technique, we advocate a transferability detection approach that guarantees to circumvent negative transfer.
  arXiv  Detail & Related papers  (2022-11-26T14:40:19Z)
• Which Invariance Should We Transfer? A Causal Minimax Learning Approach [18.71316951734806]
  We present a comprehensive minimax analysis from a causal perspective. We propose an efficient algorithm to search for the subset with minimal worst-case risk. The effectiveness and efficiency of our methods are demonstrated on synthetic data and the diagnosis of Alzheimer's disease.
  arXiv  Detail & Related papers  (2021-07-05T09:07:29Z)
• Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation [109.73983088432364]
  We propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA) We introduce the LIRR algorithm for jointly textbfLearning textbfInvariant textbfRepresentations and textbfRisks.
  arXiv  Detail & Related papers  (2020-10-09T15:42:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.