Cover Image

Fast convergence of the Expectation Maximization algorithm under a logarithmic Sobolev inequality

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Caprio, Rocco, Johansen, Adam M
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Optimization and Control Statistics Theory Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities
by: Caprio, Rocco, et al.
Published: (2024)

A calculus for Markov chain Monte Carlo: studying approximations in algorithms
by: Caprio, Rocco, et al.
Published: (2023)

Fast Spawn\&Prune (FS\&P): Global convergence of stochastic conic particle gradient descent via birth/death process
by: De Castro, Yohann, et al.
Published: (2026)

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

Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity
by: Bravo, Mario, et al.
Published: (2025)

Learning an Optimal Assortment Policy under Observational Data
by: Han, Yuxuan, et al.
Published: (2025)

Risk reversal for least squares estimators under nested convex constraints
by: Al-Ghattas, Omar
Published: (2026)

Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis
by: Li, Yingying, et al.
Published: (2023)

RandALO: Out-of-sample risk estimation in no time flat
by: Nobel, Parth, et al.
Published: (2024)

A connection between Tempering and Entropic Mirror Descent
by: Chopin, Nicolas, et al.
Published: (2023)

Mean-field underdamped Langevin dynamics and its spacetime discretization
by: Fu, Qiang, et al.
Published: (2023)

Learning linear dynamical systems under convex constraints
by: Tyagi, Hemant, et al.
Published: (2023)

Joint Learning of Linear Dynamical Systems under Smoothness Constraints
by: Tyagi, Hemant
Published: (2024)

A review of NMF, PLSA, LBA, EMA, and LCA with a focus on the identifiability issue
by: Qi, Qianqian, et al.
Published: (2025)

Stochastic Approximation with Biased MCMC for Expectation Maximization
by: Gruffaz, Samuel, et al.
Published: (2024)

FraPPE: Fast and Efficient Preference-based Pure Exploration
by: Das, Udvas, et al.
Published: (2025)

Joint learning of a network of linear dynamical systems via total variation penalization
by: Donnat, Claire, et al.
Published: (2025)

Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
by: Zhang, Dake, et al.
Published: (2024)

Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity
by: Liang, Tengyuan, et al.
Published: (2024)

Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
by: Zhang, Zhiheng, et al.
Published: (2024)

On the Uniform Convergence of Subdifferentials in Stochastic Optimization and Learning
by: Ruan, Feng
Published: (2024)

Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent
by: Karnik, Santhosh, et al.
Published: (2024)

The Collusion of Memory and Nonlinearity in Stochastic Approximation With Constant Stepsize
by: Huo, Dongyan, et al.
Published: (2024)

On Regularization via Early Stopping for Least Squares Regression
by: Sonthalia, Rishi, et al.
Published: (2024)

Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex Finite Sum Problems
by: Zhong, Yanjie, et al.
Published: (2024)

The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
by: Collins-Woodfin, Elizabeth, et al.
Published: (2024)

Off-policy estimation with adaptively collected data: the power of online learning
by: Lee, Jeonghwan, et al.
Published: (2024)

On the Sample Complexity of Set Membership Estimation for Linear Systems with Disturbances Bounded by Convex Sets
by: Xu, Haonan, et al.
Published: (2024)

Hyperparameter tuning via trajectory predictions: Stochastic prox-linear methods in matrix sensing
by: Lou, Mengqi, et al.
Published: (2024)

An Improved Analysis of Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling
by: Huang, Xunpeng, et al.
Published: (2024)

Wasserstein Contraction of Coordinate Ascent Variational Inference
by: Caprio, Rocco, et al.
Published: (2026)

Stochastic Optimization with Optimal Importance Sampling
by: Aolaritei, Liviu, et al.
Published: (2025)

A Theory of Feature Learning in Kernel Models
by: Chen, Yunlu, et al.
Published: (2023)

High-dimensional Limit of SGD for Diagonal Linear Networks
by: Malaxechebarría, Begoña García, et al.
Published: (2026)

Error Analysis of Triangular Optimal Transport Maps for Filtering
by: Al-Jarrah, Mohammad, et al.
Published: (2025)

Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
by: Gao, Yihang, et al.
Published: (2025)

A Spectral Framework for Closed-Form Relative Density Estimation
by: Bach, Francis
Published: (2026)

Learning and Decision-Making with Data: Optimal Formulations and Phase Transitions
by: Bennouna, Amine, et al.
Published: (2021)

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria
by: Liang, Tengyuan
Published: (2022)

Extreme mass distributions for quasi-copulas
by: Omladič, Matjaž, et al.
Published: (2025)