Cover Image

Active Subspaces in Infinite Dimension

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kundu, Poorbita, Wycoff, Nathan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On Thompson Sampling and Bilateral Uncertainty in Additive Bayesian Optimization
by: Wycoff, Nathan
Published: (2025)

Regression Trees Know Calculus
by: Wycoff, Nathan
Published: (2024)

A Probabilistic Basis for Low-Rank Matrix Learning
by: Segert, Simon, et al.
Published: (2025)

Formal Bayesian Transfer Learning via the Total Risk Prior
by: Wycoff, Nathan, et al.
Published: (2025)

Proximal Iteration for Nonlinear Adaptive Lasso
by: Wycoff, Nathan, et al.
Published: (2024)

Voronoi Candidates for Bayesian Optimization
by: Wycoff, Nathan, et al.
Published: (2024)

Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design
by: Abeer, A N M Nafiz, et al.
Published: (2024)

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale
by: Park, Sihwan, et al.
Published: (2025)

Optimal Embedding Dimension for Sparse Subspace Embeddings
by: Chenakkod, Shabarish, et al.
Published: (2023)

Decomposition-Based Intrinsic Modeling of Shape-Constrained Functional Data
by: Kundu, Poorbita, et al.
Published: (2024)

asKAN: Active Subspace embedded Kolmogorov-Arnold Network
by: Zhou, Zhiteng, et al.
Published: (2025)

Depth Separation in Norm-Bounded Infinite-Width Neural Networks
by: Parkinson, Suzanna, et al.
Published: (2024)

Large Data Limits of Laplace Learning for Gaussian Measure Data in Infinite Dimensions
by: Zhong, Zhengang, et al.
Published: (2026)

Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate
by: Jiang, Ruichen, et al.
Published: (2024)

Subspace Clustering of Subspaces: Unifying Canonical Correlation Analysis and Subspace Clustering
by: Karakasis, Paris A., et al.
Published: (2025)

Sequential Bayesian Optimal Experimental Design in Infinite Dimensions via Policy Gradient Reinforcement Learning
by: Shen, Kaichen, et al.
Published: (2026)

Subspace-Configurable Networks
by: Wang, Dong, et al.
Published: (2023)

Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional Data
by: Cribeiro-Ramallo, Jose, et al.
Published: (2024)

Geometry of Lightning Self-Attention: Identifiability and Dimension
by: Henry, Nathan W., et al.
Published: (2024)

Subspace Langevin Monte Carlo
by: Maunu, Tyler, et al.
Published: (2024)

Analog Physical Systems Can Exhibit Double Descent
by: Dillavou, Sam, et al.
Published: (2025)

Bayesian Fine-tuning in Projected Subspaces
by: Dubovik, Viktar, et al.
Published: (2026)

Bayesian Semi-structured Subspace Inference
by: Dold, Daniel, et al.
Published: (2024)

SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation
by: Shmuel, Dor H., et al.
Published: (2023)

Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning
by: Harrison, Nicholas, et al.
Published: (2024)

Pure Exploration with Infinite Answers
by: Poiani, Riccardo, et al.
Published: (2025)

Decision Theoretic Subgroup Detection With Bayesian Machine Learning
by: Alam, Entejar, et al.
Published: (2025)

Exceedance and force of centrality for functional data
by: Kundu, Poorbita, et al.
Published: (2025)

Infinite-Dimensional Feature Interaction
by: Xu, Chenhui, et al.
Published: (2024)

Native Hierarchical and Compositional Representations with Subspace Embeddings
by: Moreira, Gabriel, et al.
Published: (2025)

Enhanced Latent Multi-view Subspace Clustering
by: Shi, Long, et al.
Published: (2023)

In-Context Learning Operates as Concept Subspace Learning
by: Tang, Wei, et al.
Published: (2026)

Cost-effective Reduced-Order Modeling via Bayesian Active Learning
by: Rahmati, Amir Hossein, et al.
Published: (2025)

Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks
by: D'Agostino, Danny, et al.
Published: (2023)

Deep Learning for Subspace Regression
by: Fanaskov, Vladimir, et al.
Published: (2025)

Matched Topological Subspace Detector
by: Liu, Chengen, et al.
Published: (2025)

Masked Subspace Clustering Methods
by: Song, Jiebo, et al.
Published: (2025)

Model Merging in the Essential Subspace
by: Li, Longhua, et al.
Published: (2026)

DyPP: Dynamic Parameter Prediction to Accelerate Convergence of Variational Quantum Algorithms
by: Kundu, Satwik, et al.
Published: (2023)

Invariant Subspace Decomposition
by: Lazzaretto, Margherita, et al.
Published: (2024)