:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Mauri, Lorenzo, Zanella, Giacomo
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2405.08999
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Error Bounds and Optimal Schedules for Masked Diffusions with Factorized Approximations
by: Lavenant, Hugo, et al.
Published: (2025)

Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation
by: Mensch, Zier, et al.
Published: (2026)

A fast non-reversible sampler for Bayesian finite mixture models
by: Ascolani, Filippo, et al.
Published: (2025)

Mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression
by: Ascolani, Filippo, et al.
Published: (2025)

Spectral gap of Metropolis-within-Gibbs under log-concavity
by: Secchi, Cecilia, et al.
Published: (2025)

Faster Sampling via Stochastic Gradient Proximal Sampler
by: Huang, Xunpeng, et al.
Published: (2024)

Complexity of Markov Chain Monte Carlo for Generalized Linear Models
by: Chak, Martin, et al.
Published: (2025)

Linear-cost unbiased posterior estimates for crossed effects and matrix factorization models via couplings
by: Ceriani, Paolo Maria, et al.
Published: (2024)

Robust Stochastic Optimization via Gradient Quantile Clipping
by: Merad, Ibrahim, et al.
Published: (2023)

Convergence rate of random scan Coordinate Ascent Variational Inference under log-concavity
by: Lavenant, Hugo, et al.
Published: (2024)

Partially factorized variational inference for high-dimensional mixed models
by: Goplerud, Max, et al.
Published: (2023)

Stochastic Sample Approximations of (Local) Moduli of Continuity
by: Nazarov, Rodion, et al.
Published: (2025)

Multiple Importance Sampling for Stochastic Gradient Estimation
by: Salaün, Corentin, et al.
Published: (2024)

Online Importance Sampling for Stochastic Gradient Optimization
by: Salaün, Corentin, et al.
Published: (2023)

Posterior Approximation using Stochastic Gradient Ascent with Adaptive Stepsize
by: Lim, Kart-Leong, et al.
Published: (2024)

Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients
by: Fonseca, Yuri, et al.
Published: (2024)

Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo
by: Wang, Ziyi, et al.
Published: (2023)

Weighted Low-rank Approximation via Stochastic Gradient Descent on Manifolds
by: Xu, Conglong, et al.
Published: (2025)

Conjugate gradient methods for high-dimensional GLMMs
by: Pandolfi, Andrea, et al.
Published: (2024)

Entropy contraction of the Gibbs sampler under log-concavity
by: Ascolani, Filippo, et al.
Published: (2024)

Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
by: Kumar, Ramnath, et al.
Published: (2023)

Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models
by: Ascolani, Filippo, et al.
Published: (2024)

Revisiting Stochastic Approximation and Stochastic Gradient Descent
by: Karandikar, Rajeeva Laxman, et al.
Published: (2025)

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

A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimation
by: Li, Zehao, et al.
Published: (2025)

The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
by: Livni, Roi
Published: (2024)

Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond
by: Zhang, Lijun, et al.
Published: (2023)

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

Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
by: Lin, Jihao Andreas, et al.
Published: (2023)

Inference on covariance structure in high-dimensional multi-view data
by: Mauri, Lorenzo, et al.
Published: (2025)

Stochastic Gradient Langevin Dynamics with Variance Reduction
by: Huang, Zhishen, et al.
Published: (2021)

Regret and Sample Complexity of Online Q-Learning via Concentration of Stochastic Approximation with Time-Inhomogeneous Markov Chains
by: Singh, Rahul, et al.
Published: (2026)

Sampling via Gaussian Mixture Approximations
by: Huang, Yongchao
Published: (2025)

First and Second Order Approximations to Stochastic Gradient Descent Methods with Momentum Terms
by: Lu, Eric
Published: (2025)

Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics
by: Zheng, Haoyang, et al.
Published: (2024)

Euphonium: Steering Video Flow Matching via Process Reward Gradient Guided Stochastic Dynamics
by: Zhong, Ruizhe, et al.
Published: (2026)

Flatness-Aware Stochastic Gradient Langevin Dynamics
by: Bruno, Stefano, et al.
Published: (2025)

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective
by: Wang, Weixin, et al.
Published: (2025)

Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization
by: Yang, Yufeng, et al.
Published: (2024)

Optimal and computationally tractable lower bounds for logistic log-likelihoods
by: Anceschi, Niccolò, et al.
Published: (2024)