Research map / Optimization / Optimizers
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes (NeurIPS 2020 · similarity 0.91)
- A Variational Analysis of Stochastic Gradient Algorithms (ICML 2016 · similarity 0.91)
- Stochastic Gradient Descent for Gaussian Processes Done Right (ICLR 2024 · similarity 0.91)
- Efficiently sampling functions from Gaussian process posteriors (ICML 2020 · similarity 0.90)
- Optimizing Posterior Samples for Bayesian Optimization via Rootfinding (ICLR 2025 · similarity 0.89)
- Stochastic Optimization Schemes for Performative Prediction with Nonconvex Loss (NeurIPS 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
