:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Szwagier, Tom, Pennec, Xavier
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2502.06022
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The curse of isotropy: from principal components to principal subspaces
by: Szwagier, Tom, et al.
Published: (2023)

Parsimonious Gaussian mixture models with piecewise-constant eigenvalue profiles
by: Szwagier, Tom, et al.
Published: (2025)

Eigengap Sparsity for Covariance Parsimony
by: Szwagier, Tom, et al.
Published: (2025)

Barycentric subspace analysis of network-valued data
by: Maignant, Elodie, et al.
Published: (2025)

Effective formulas for the geometry of normal homogeneous spaces. Application to flag manifolds
by: Rabenoro, Dimbihery, et al.
Published: (2023)

A geometric framework for asymptotic inference of principal subspaces in PCA
by: Rabenoro, Dimbihery, et al.
Published: (2022)

Towards Minimax Optimality of Model-based Robust Reinforcement Learning
by: Clavier, Pierre, et al.
Published: (2023)

Nested Expectations with Kernel Quadrature
by: Chen, Zonghao, et al.
Published: (2025)

Bootstrapping Expectiles in Reinforcement Learning
by: Clavier, Pierre, et al.
Published: (2024)

Efficient Gaussian process learning via subspace projections
by: Cazelles, Elsa, et al.
Published: (2026)

NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces
by: Levrero-Florencio, Francesc, et al.
Published: (2026)

Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures
by: Papillon, Mathilde, et al.
Published: (2024)

A spectral method for multi-view subspace learning using the product of projections
by: Sergazinov, Renat, et al.
Published: (2024)

Nested Slice Sampling: Vectorized Nested Sampling for GPU-Accelerated Inference
by: Yallup, David, et al.
Published: (2026)

Nested Inheritance Dynamics
by: Moraffah, Bahman
Published: (2024)

Nonlinear subspace clustering by functional link neural networks
by: Shi, Long, et al.
Published: (2024)

Linear combinations of latents in generative models: subspaces and beyond
by: Bodin, Erik, et al.
Published: (2024)

NestGNN: A Graph Neural Network Framework Generalizing the Nested Logit Model for Travel Mode Choice
by: Zhou, Yuqi, et al.
Published: (2025)

Good practices for evaluation of machine learning systems
by: Ferrer, Luciana, et al.
Published: (2024)

NestQuant: Nested Lattice Quantization for Matrix Products and LLMs
by: Savkin, Semyon, et al.
Published: (2025)

Statistical learning theory and Occam's razor: The core argument
by: Sterkenburg, Tom F.
Published: (2023)

Second-order difference subspace
by: Fukui, Kazuhiro, et al.
Published: (2024)

Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks
by: Anthes, Daniel, et al.
Published: (2023)

A universal linearized subspace refinement framework for neural networks
by: Cao, Wenbo, et al.
Published: (2026)

Curvature-Guided LoRA: Steering in the pretrained NTK subspace
by: Zheng, Frédéric, et al.
Published: (2026)

Robust agents learn causal world models
by: Richens, Jonathan, et al.
Published: (2024)

Does SGD really happen in tiny subspaces?
by: Song, Minhak, et al.
Published: (2024)

Shared active subspace for multivariate vector-valued functions
by: Musayeva, Khadija, et al.
Published: (2024)

Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces
by: Lövdal, Sofie, et al.
Published: (2024)

NEST: Nested Event Stream Transformer for Sequences of Multisets
by: Sun, Minghui, et al.
Published: (2026)

Residual subspace evolution strategies for nonlinear inverse problems
by: Alemanno, Francesco
Published: (2025)

Preferential subspace identification (PSID) with forward-backward smoothing
by: Sani, Omid G., et al.
Published: (2025)

Log-Euclidean Lie Groups
by: Bisson, Olivier, et al.
Published: (2025)

Flagfolds: an approach to multi-dimensional varifolds
by: Buet, Blanche, et al.
Published: (2023)

NestQuant: Post-Training Integer-Nesting Quantization for On-Device DNN
by: Xie, Jianhang, et al.
Published: (2025)

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining
by: Jiang, Zhida, et al.
Published: (2026)

Nesting Particle Filters for Experimental Design in Dynamical Systems
by: Iqbal, Sahel, et al.
Published: (2024)

Nested Nonparametric Instrumental Variable Regression
by: Meza, Isaac, et al.
Published: (2021)

Nested Learning: The Illusion of Deep Learning Architectures
by: Behrouz, Ali, et al.
Published: (2025)

Nested Spatio-Temporal Time Series Forecasting
by: Ai, Yinghao, et al.
Published: (2026)