:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Marion, Pierre, Chizat, Lénaïc
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2405.13456
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Annealed Sinkhorn for Optimal Transport: convergence, regularization path and debiasing
by: Chizat, Lénaïc
Published: (2024)

The Hidden Width of Deep ResNets: Tight Error Bounds and Phase Diagram
by: Chizat, Lénaïc
Published: (2025)

The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks
by: Chizat, Lénaïc, et al.
Published: (2023)

Phase Diagram of Dropout for Two-Layer Neural Networks in the Mean-Field Regime
by: Chizat, Lénaïc, et al.
Published: (2025)

Doubly Regularized Entropic Wasserstein Barycenters
by: Chizat, Lénaïc
Published: (2023)

An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games
by: Wang, Guillaume, et al.
Published: (2022)

ResNets of All Shapes and Sizes: Convergence of Training Dynamics in the Large-scale Limit
by: Chaintron, Louis-Pierre, et al.
Published: (2026)

Mean-Field Langevin Dynamics for Signed Measures via a Bilevel Approach
by: Wang, Guillaume, et al.
Published: (2024)

Local convergence of mean-field Langevin dynamics: from gradient flows to linearly monotone games
by: Wang, Guillaume, et al.
Published: (2026)

Quantitative Convergence of Wasserstein Gradient Flows of Kernel Mean Discrepancies
by: Chizat, Lénaïc, et al.
Published: (2026)

Quantitative Local Convergence of Mean-Field Stein Variational Gradient Flow
by: Chizat, Lénaïc, et al.
Published: (2026)

Linear regression with overparameterized linear neural networks: Tight upper and lower bounds for implicit $\ell^1$-regularization
by: Matt, Hannes, et al.
Published: (2025)

Implicit regularization of deep residual networks towards neural ODEs
by: Marion, Pierre, et al.
Published: (2023)

Sharper Exponential Convergence Rates for Sinkhorn's Algorithm in Continuous Settings
by: Chizat, Lénaïc, et al.
Published: (2024)

Displacement smoothness of entropic optimal transport
by: Carlier, Guillaume, et al.
Published: (2022)

Fréchet regression with implicit denoising and multicollinearity reduction
by: Mansouri, Dou El Kefel, et al.
Published: (2024)

Convergence of drift-diffusion PDEs arising as Wasserstein gradient flows of convex functions
by: Chizat, Lénaïc, et al.
Published: (2025)

Deep Huber quantile regression networks
by: Tyralis, Hristos, et al.
Published: (2023)

Attention layers provably solve single-location regression
by: Marion, Pierre, et al.
Published: (2024)

Derivative-based regularization for regression
by: Lopedoto, Enrico, et al.
Published: (2024)

The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective
by: Lin, Chi-Heng, et al.
Published: (2022)

Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization
by: Feng, Siqi, et al.
Published: (2024)

On sparse regression, Lp-regularization, and automated model discovery
by: McCulloch, Jeremy A., et al.
Published: (2023)

Bayes optimal learning in high-dimensional linear regression with network side information
by: Nandy, Sagnik, et al.
Published: (2023)

Augmented balancing weights as linear regression
by: Bruns-Smith, David, et al.
Published: (2023)

Meta-learning of shared linear representations beyond well-specified linear regression
by: Even, Mathieu, et al.
Published: (2025)

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

A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
by: Kim, Youngseok, et al.
Published: (2022)

ProtFAD: Introducing function-aware domains as implicit modality towards protein function prediction
by: Wang, Mingqing, et al.
Published: (2024)

Closed-form $\ell_r$ norm scaling with data for overparameterized linear regression and diagonal linear networks under $\ell_p$ bias
by: Zhang, Shuofeng, et al.
Published: (2025)

Clustering in Deep Stochastic Transformers
by: Fedorov, Lev, et al.
Published: (2026)

Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relation
by: Gross, Markus, et al.
Published: (2023)

Semisupervised regression in latent structure networks on unknown manifolds
by: Acharyya, Aranyak, et al.
Published: (2023)

On weight and variance uncertainty in neural networks for regression tasks
by: Monemi, Moein, et al.
Published: (2025)

High-dimensional logistic regression with missing data: Imputation, regularization, and universality
by: Verchand, Kabir Aladin, et al.
Published: (2024)

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability
by: Gouvêa, Rogério Almeida, et al.
Published: (2025)

Call for Action: towards the next generation of symbolic regression benchmark
by: Aldeia, Guilherme S. Imai, et al.
Published: (2025)

Non-omniscient backdoor injection with one poison sample: Proving the one-poison hypothesis for linear regression, linear classification, and 2-layer ReLU neural networks
by: Peinemann, Thorsten, et al.
Published: (2025)

Generalisation and benign over-fitting for linear regression onto random functional covariates
by: Jones, Andrew, et al.
Published: (2025)

Metric space valued Fréchet regression
by: Györfi, László, et al.
Published: (2026)