:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Jaffard, Sophie, Vaiter, Samuel, Muzy, Alexandre, Reynaud-Bouret, Patricia
Format:	Preprint
Published:	2023
Subjects:	Statistics Theory
Online Access:	https://arxiv.org/abs/2304.08061
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration
by: Jaffard, Sophie, et al.
Published: (2024)

Neural Coding as a Statistical Testing Problem
by: Ost, Guilherme, et al.
Published: (2022)

General oracle inequalities for a penalized log-likelihood criterion based on non-stationary data
by: Aubert, Julien, et al.
Published: (2024)

Non-asymptotic statistical test of the diffusion coefficient of stochastic differential equations
by: Melnykova, Anna, et al.
Published: (2023)

Separation rates for the detection of synchronization of interacting point processes in a mean field frame. Application to neuroscience
by: Tchouanti, Josué, et al.
Published: (2024)

Spiking Neural Models for Decision-Making Tasks with Learning
by: Jaffard, Sophie, et al.
Published: (2025)

Chemical Reaction Networks Learn Better than Spiking Neural Networks
by: Jaffard, Sophie, et al.
Published: (2026)

Sharp bounds on aggregate expert error
by: Kontorovich, Aryeh, et al.
Published: (2024)

Change Point Detection and Mean-Field Dynamics of Variable Productivity Hawkes Processes
by: Kresin, Conor, et al.
Published: (2025)

Optimal neural network approximation of smooth compositional functions on sets with low intrinsic dimension
by: Nagler, Thomas, et al.
Published: (2026)

Posterior concentration in spatio-temporal Hawkes processes
by: Miscouridou, Xenia, et al.
Published: (2026)

Spectral analysis of multivariate stationary Hawkes processes
by: Tang, Yifu, et al.
Published: (2026)

Common Drivers in Sparsely Interacting Hawkes Processes
by: Kreiss, Alexander, et al.
Published: (2025)

Statistical theory for image classification using deep convolutional neural networks with cross-entropy loss under the hierarchical max-pooling model
by: Kohler, Michael, et al.
Published: (2020)

Hoeffding-type decomposition for $U$-statistics on bipartite networks
by: Minh, Tâm Le, et al.
Published: (2023)

A non-local estimator for locally stationary Hawkes processes
by: Deschatre, Thomas, et al.
Published: (2025)

ERM-Lasso classification algorithm for Multivariate Hawkes Processes paths
by: Dion-Blanc, Charlotte, et al.
Published: (2024)

$β$-integrated local depth and corresponding partitioned local depth representation
by: Wang, Siyi, et al.
Published: (2025)

On Observation Time for Recovering Latent Hawkes Networks
by: Linkerhägner, Jonas, et al.
Published: (2026)

Detecting Mutual Excitations in Non-Stationary Hawkes Processes
by: Mossel, Elchanan, et al.
Published: (2026)

Hawkes process with a diffusion-driven baseline: long-run behavior, inference, statistical tests
by: Perrin, Maya Sadeler, et al.
Published: (2025)

Foundations of locally-balanced Markov processes
by: Livingstone, Samuel, et al.
Published: (2025)

On the VC dimension of deep group convolutional neural networks
by: Sepliarskaia, Anna, et al.
Published: (2024)

Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes
by: Zhu, Yichen, et al.
Published: (2022)

Mitigating loss of variance in ensemble data assimilation: machine learning-based and distance-free localization
by: Silva, Vinicius L. S., et al.
Published: (2025)

On the rates of convergence for learning with convolutional neural networks
by: Yang, Yunfei, et al.
Published: (2024)

A theory of ROC analysis of rule-out and rule-in diagnostics with applications to mammography data
by: Mastrianni, Michelle, et al.
Published: (2026)

Forecasting threshold exceedance of atmospheric variables at a specific location
by: Baggio, Roberta, et al.
Published: (2026)

From rough to multifractal multidimensional volatility: A multidimensional Log S-fBM model
by: Zarhali, Othmane, et al.
Published: (2026)

Provably Efficient Posterior Sampling for Sparse Linear Regression via Measure Decomposition
by: Montanari, Andrea, et al.
Published: (2024)

MCMC-driven learning
by: Bouchard-Côté, Alexandre, et al.
Published: (2024)

Sliding down the stairs: how correlated latent variables accelerate learning with neural networks
by: Bardone, Lorenzo, et al.
Published: (2024)

Provable Efficiency of Guidance in Diffusion Models for General Data Distribution
by: Li, Gen, et al.
Published: (2025)

On the rate of convergence of an over-parametrized deep neural network regression estimate learned by gradient descent
by: Kohler, Michael
Published: (2025)

Improving probabilistic wind speed forecasting using M-Rice distribution and spatial data integration
by: Baggio, Roberta, et al.
Published: (2023)

Breaking AR's Sampling Bottleneck: Provable Acceleration via Diffusion Language Models
by: Li, Gen, et al.
Published: (2025)

Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond
by: Sawaya, Kazuma
Published: (2025)

From Hawkes Processes to Attention: Time-Modulated Mechanisms for Event Sequences
by: Tan, Xinzi, et al.
Published: (2026)

On the Provable Performance Guarantee of Efficient Reasoning Models
by: Zeng, Hao, et al.
Published: (2025)

Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems
by: Altekrüger, Fabian, et al.
Published: (2023)