The case for and against fixed step-size: Stochastic approximation algorithms in optimization and machine learning

Documenti analoghi

• Revisiting Step-Size Assumptions in Stochastic Approximation
  di: Lauand, Caio Kalil, et al.
  Pubblicazione: (2024)
• The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning
  di: Borkar, Vivek, et al.
  Pubblicazione: (2021)
• Global Convergence and Acceleration for Single Observation Gradient Free Optimization
  di: Lauand, Caio Kalil, et al.
  Pubblicazione: (2025)
• Extremely Fast Convergence Rates for Extremum Seeking Control with Polyak-Ruppert Averaging
  di: Lauand, Caio Kalil, et al.
  Pubblicazione: (2022)
• Markovian Foundations for Quasi-Stochastic Approximation with Applications to Extremum Seeking Control
  di: Lauand, Caio Kalil, et al.
  Pubblicazione: (2022)