Cover Image

GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Aoran, Feng, Yang
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Robust and Adaptive Spectral Method for Representation Multi-Task Learning with Contamination
by: Huang, Yian, et al.
Published: (2025)

Inversion-Free Natural Gradient Descent on Riemannian Manifolds
by: Draca, Dario, et al.
Published: (2026)

Multi-Task Learning with Summary Statistics
by: Knight, Parker, et al.
Published: (2023)

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings
by: Sui, Yang, et al.
Published: (2025)

Supervised Manifold Learning for Functional Data
by: Tan, Ruoxu, et al.
Published: (2025)

Neyman-Pearson Multi-class Classification via Cost-sensitive Learning
by: Tian, Ye, et al.
Published: (2021)

SMART: A Spectral Transfer Approach to Multi-Task Learning
by: Zhao, Boxin, et al.
Published: (2026)

The Topology and Geometry of Neural Representations
by: Lin, Baihan, et al.
Published: (2023)

Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives
by: Behdin, Kayhan, et al.
Published: (2022)

A Unified Hierarchical Multi-Task Multi-Fidelity Framework for Data-Efficient Surrogate Modeling in Manufacturing
by: Mehta, Manan, et al.
Published: (2026)

Entropic Riemannian Neural Optimal Transport
by: Micheli, Alessandro, et al.
Published: (2026)

Riemannian Laplace Approximation with the Fisher Metric
by: Yu, Hanlin, et al.
Published: (2023)

Task Ecologies and the Evolution of World-Tracking Representations in Large Language Models
by: Riva, Giulio Valentino Dalla
Published: (2026)

Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit
by: Zhang, Chaozhi, et al.
Published: (2024)

Augmentation Invariant Manifold Learning
by: Wang, Shulei
Published: (2022)

Skeleton Regression: A Graph-Based Approach to Estimation with Manifold Structure
by: Wei, Zeyu, et al.
Published: (2023)

Vector Quantile Regression on Manifolds
by: Pegoraro, Marco, et al.
Published: (2023)

Structure Maintained Representation Learning Neural Network for Causal Inference
by: Sun, Yang, et al.
Published: (2025)

Representation-Aware Distributionally Robust Optimization: A Knowledge Transfer Framework
by: Wang, Zitao, et al.
Published: (2025)

Interventional Imbalanced Multi-Modal Representation Learning via $β$-Generalization Front-Door Criterion
by: Li, Yi, et al.
Published: (2024)

Towards Identifiability of Hierarchical Temporal Causal Representation Learning
by: Li, Zijian, et al.
Published: (2025)

Contrastive Network Representation Learning
by: Dong, Zihan, et al.
Published: (2025)

Discrete Causal Representation Learning
by: Zhang, Wenjin, et al.
Published: (2026)

Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing
by: Kim, Kwangho
Published: (2026)

Adaptive Multi-task Learning for Multi-sector Portfolio Optimization
by: Fan, Qingliang, et al.
Published: (2025)

Task-Agnostic Machine-Learning-Assisted Inference
by: Miao, Jiacheng, et al.
Published: (2024)

Score Matching for Truncated Density Estimation on a Manifold
by: Williams, Daniel J., et al.
Published: (2022)

Multi-Domain Empirical Bayes for Linearly-Mixed Causal Representations
by: Wu, Bohan, et al.
Published: (2026)

Representation Retrieval Learning for Heterogeneous Data Integration
by: Xu, Qi, et al.
Published: (2025)

Causal Effect Estimation with Learned Instrument Representations
by: Dean, Frances, et al.
Published: (2026)

Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks
by: Cui, Ziqiang, et al.
Published: (2024)

Multi-modal Causal Structure Learning and Root Cause Analysis
by: Zheng, Lecheng, et al.
Published: (2024)

Learning over von Mises-Fisher Distributions via a Wasserstein-like Geometry
by: You, Kisung, et al.
Published: (2025)

Learning Treatment Representations for Downstream Instrumental Variable Regression
by: Lin, Shiangyi, et al.
Published: (2025)

Introducing Diminutive Causal Structure into Graph Representation Learning
by: Gao, Hang, et al.
Published: (2024)

Task-tailored Pre-processing: Fair Downstream Supervised Learning
by: Sohn, Jinwon, et al.
Published: (2026)

Causally-Aware Unsupervised Feature Selection Learning
by: Shen, Zongxin, et al.
Published: (2024)

SOFARI-R: High-Dimensional Manifold-Based Inference for Latent Responses
by: Zheng, Zemin, et al.
Published: (2025)

The Kernel Manifold: A Geometric Approach to Gaussian Process Model Selection
by: Islam, Md Shafiqul, et al.
Published: (2026)

Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models
by: Tian, Ye, et al.
Published: (2022)