Cover Image

A method of supervised learning from conflicting data with hidden contexts

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Tianren, Jiang, Yizhou, Chen, Feng
Format:	Preprint
Published:	2021
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Feature contamination: Neural networks learn uncorrelated features and fail to generalize
by: Zhang, Tianren, et al.
Published: (2024)

When Do Neural Networks Learn World Models?
by: Zhang, Tianren, et al.
Published: (2025)

Exploring the Hidden Reasoning Process of Large Language Models by Misleading Them
by: Chen, Guanyu, et al.
Published: (2025)

Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional data
by: Delrocq, Ariane, et al.
Published: (2026)

Multimodal machine learning with large language embedding model for polymer property prediction
by: Zhang, Tianren, et al.
Published: (2025)

Preserving Silent Features for Domain Generalization
by: Zhao, Chujie, et al.
Published: (2024)

Reconciling In-Context and In-Weight Learning via Dual Representation Space Encoding
by: Chen, Guanyu, et al.
Published: (2026)

Inpainting physics: self-supervised learning for context-driven fluid simulation
by: Weidner, Jonas, et al.
Published: (2026)

SEGAN: semi-supervised learning approach for missing data imputation
by: Pan, Xiaohua, et al.
Published: (2024)

Distributionally robust self-supervised learning for tabular data
by: Ghosh, Shantanu, et al.
Published: (2024)

Semi-supervised learning for linear extremile regression
by: Jiang, Rong, et al.
Published: (2025)

Uncovering hidden geometry in Transformers via disentangling position and context
by: Song, Jiajun, et al.
Published: (2023)

Self-supervised learning on gene expression data
by: Dradjat, Kevin, et al.
Published: (2025)

Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions
by: Tran, Hoang, et al.
Published: (2025)

The hidden risks of temporal resampling in clinical reinforcement learning
by: Frost, Thomas, et al.
Published: (2026)

Universal hidden monotonic trend estimation with contrastive learning
by: Pineau, Edouard, et al.
Published: (2022)

A Generalized Spectral Framework to Expain Neural Scaling and Compression Dynamics
by: Zhang, Yizhou
Published: (2025)

Combining supervised and unsupervised learning methods to predict financial market movements
by: Palma, Gabriel Rodrigues, et al.
Published: (2024)

From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning
by: Huang, Zhe, et al.
Published: (2025)

CHILLI: A data context-aware perturbation method for XAI
by: Anwar, Saif, et al.
Published: (2024)

Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method
by: Wang, Siwei, et al.
Published: (2023)

On the consistency of supervised learning with missing values
by: Josse, Julie, et al.
Published: (2019)

Systematic comparison of semi-supervised and self-supervised learning for medical image classification
by: Huang, Zhe, et al.
Published: (2023)

Renormalizable Spectral-Shell Dynamics as the Origin of Neural Scaling Laws
by: Zhang, Yizhou
Published: (2025)

SegMatch: A semi-supervised learning method for surgical instrument segmentation
by: Wei, Meng, et al.
Published: (2023)

Simple and near-optimal algorithms for hidden stratification and multi-group learning
by: Tosh, Christopher, et al.
Published: (2021)

Shallow diffusion networks provably learn hidden low-dimensional structure
by: Boffi, Nicholas M., et al.
Published: (2024)

A theoretical framework for self-supervised contrastive learning for continuous dependent data
by: Marusov, Alexander, et al.
Published: (2025)

Online learning in bandits with predicted context
by: Guo, Yongyi, et al.
Published: (2023)

Transformer learns the cross-task prior and regularization for in-context learning
by: Lu, Fei, et al.
Published: (2025)

High-dimensional semi-supervised learning: in search for optimal inference of the mean
by: Zhang, Yuqian, et al.
Published: (2019)

Semi-supervised learning with max-margin graph cuts
by: Kveton, Branislav, et al.
Published: (2026)

Informative missingness and its implications in semi-supervised learning
by: Wu, Jinran, et al.
Published: (2025)

On the sample complexity of semi-supervised multi-objective learning
by: Wegel, Tobias, et al.
Published: (2025)

In-context learning agents are asymmetric belief updaters
by: Schubert, Johannes A., et al.
Published: (2024)

An extension of linear self-attention for in-context learning
by: Hagiwara, Katsuyuki
Published: (2025)

TSFM in-context learning for time-series classification of bearing-health status
by: Tokic, Michel, et al.
Published: (2025)

Weakly-supervised anomaly detection for multimodal data distributions
by: Tan, Xu, et al.
Published: (2024)

SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data
by: Jurek, Kacper, et al.
Published: (2026)

The broader spectrum of in-context learning
by: Lampinen, Andrew Kyle, et al.
Published: (2024)