:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Garcia, Ernesto, Bermolen, Paola, Jonckheere, Matthieu, Shneer, Seva
Format:	Preprint
Published:	2025
Subjects:	Probability Machine Learning
Online Access:	https://arxiv.org/abs/2503.03565
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Waves Everywhere: A Distributional Equation Approach to Front Propagation
by: Jonckheere, Matthieu, et al.
Published: (2026)

Optimization Trade-offs in Asynchronous Federated Learning: A Stochastic Networks Approach
by: Alahyane, Abdelkrim, et al.
Published: (2026)

Risk aggregation and stochastic dominance for a class of heavy-tailed distributions
by: Chen, Yuyu, et al.
Published: (2024)

Stochastic dominance for linear combinations of infinite-mean risks
by: Chen, Yuyu, et al.
Published: (2025)

Weighted Random Dot Product Graphs
by: Marenco, Bernardo, et al.
Published: (2025)

Optimizing Asynchronous Federated Learning: A Delicate Trade-Off Between Model-Parameter Staleness and Update Frequency
by: Alahyane, Abdelkrim, et al.
Published: (2025)

A model of discrete interacting updates
by: Denisov, Denis, et al.
Published: (2025)

Asymptotics for cliques in scale-free random graphs
by: Daly, Fraser, et al.
Published: (2020)

Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability
by: Comte, Céline, et al.
Published: (2023)

First passage percolation on Erdős-Rényi graphs with general weights
by: Daly, Fraser, et al.
Published: (2023)

Partially Stochastic Infinitely Deep Bayesian Neural Networks
by: Calvo-Ordonez, Sergio, et al.
Published: (2024)

$K-$means with learned metrics
by: Groisman, Pablo, et al.
Published: (2026)

Large Deviations and the Peano Phenomenon in Stochastic Differential Equations with Homogeneous Drift
by: Bermolen, Paola, et al.
Published: (2025)

Stochastic Scaling Limits and Synchronization by Noise in Deep Transformer Models
by: Agazzi, Andrea, et al.
Published: (2026)

Online Matching via Reinforcement Learning: An Expert Policy Orchestration Strategy
by: Mignacco, Chiara, et al.
Published: (2025)

Random-Bridges as Stochastic Transports for Generative Models
by: Goria, Stefano, et al.
Published: (2025)

Online matching for the multiclass stochastic block model
by: Soprano-Loto, Nahuel, et al.
Published: (2023)

Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent
by: Schertzer, Adrien, et al.
Published: (2024)

Stochastic Operator Network: A Stochastic Maximum Principle Based Approach to Operator Learning
by: Bausback, Ryan, et al.
Published: (2025)

On the instability of local learning algorithms: Q-learning can fail in infinite state spaces
by: Ayesta, Urtzi, et al.
Published: (2025)

Decentralized Proximal Stochastic Gradient Langevin Dynamics
by: Islam, Mohammad Rafiqul, et al.
Published: (2026)

Limit Theorems for Stochastic Gradient Descent with Infinite Variance
by: Blanchet, Jose, et al.
Published: (2024)

A Free Probabilistic Framework for Denoising Diffusion Models: Entropy, Transport, and Reverse Processes
by: Das, Swagatam
Published: (2025)

Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version
by: Leão, Dorival, et al.
Published: (2026)

Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion
by: Kim, Hwanwoo, et al.
Published: (2025)

Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans
by: Wald, Christian, et al.
Published: (2025)

Approximation to Deep Q-Network by Stochastic Delay Differential Equations
by: Lu, Jianya, et al.
Published: (2025)

Learning Optimal Search Strategies
by: Ankirchner, Stefan, et al.
Published: (2026)

Convergence, Sticking and Escape: Stochastic Dynamics Near Critical Points in SGD
by: Dudukalov, Dmitry, et al.
Published: (2025)

Towards Continuous-Time Approximations for Stochastic Gradient Descent without Replacement
by: Perko, Stefan
Published: (2025)

Stochastic Port-Hamiltonian Neural Networks: Universal Approximation with Passivity Guarantees
by: Di Persio, Luca, et al.
Published: (2026)

Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals
by: Holberg, Christian, et al.
Published: (2024)

Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework
by: Chen, Zaiwei, et al.
Published: (2026)

Free Random Projection for In-Context Reinforcement Learning
by: Hayase, Tomohiro, et al.
Published: (2025)

Wasserstein Parallel Transport for Predicting the Dynamics of Statistical Systems
by: Saidi, Tristan Luca, et al.
Published: (2026)

A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging
by: Khodadadian, Sajad, et al.
Published: (2025)

Steady-State Behavior of Constant-Stepsize Stochastic Approximation: Gaussian Approximation and Tail Bounds
by: Wang, Zedong, et al.
Published: (2026)

Phase Transition for Stochastic Block Model with more than $\sqrt{n}$ Communities
by: Carpentier, Alexandra, et al.
Published: (2025)

Partial recovery and weak consistency in the non-uniform hypergraph Stochastic Block Model
by: Dumitriu, Ioana, et al.
Published: (2021)

Optimal and exact recovery on general non-uniform Hypergraph Stochastic Block Model
by: Dumitriu, Ioana, et al.
Published: (2023)