NUTS-mul and NUTS-BPS show nearly identical qualitative ergodicity behavior depending on target tails, with both mixing in O(d^{1/4}) time for Gaussians but smaller constants for NUTS-BPS.
Monte carlo sampling methods using markov chains and their applications
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
verdicts
UNVERDICTED 4representative citing papers
The paper proves the first optimal O(n^{-1/2}) Wasserstein-1 CLT rates for locally dependent sequences and geometrically ergodic Markov chains, plus new W_p rates for p greater than or equal to 2 under mild moments, with an application to U-statistics.
SecureForge audits LLM code for vulnerabilities, builds a synthetic prompt corpus via Markovian sampling, and optimizes system prompts to cut security issues by up to 48% while preserving unit test performance, with zero-shot transfer to real prompts.
Flow Matching is a generative modeling framework with mathematical foundations, design choices, extensions, and open-source PyTorch code for applications like image and text generation.
citing papers explorer
-
A Theoretical Comparison of No-U-Turn Sampler Variants: Necessary and Sufficient Convergence Conditions and Mixing Time Analysis under Gaussian Targets
NUTS-mul and NUTS-BPS show nearly identical qualitative ergodicity behavior depending on target tails, with both mixing in O(d^{1/4}) time for Gaussians but smaller constants for NUTS-BPS.
-
Wasserstein-p Central Limit Theorem Rates: From Local Dependence to Markov Chains
The paper proves the first optimal O(n^{-1/2}) Wasserstein-1 CLT rates for locally dependent sequences and geometrically ergodic Markov chains, plus new W_p rates for p greater than or equal to 2 under mild moments, with an application to U-statistics.
-
SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization
SecureForge audits LLM code for vulnerabilities, builds a synthetic prompt corpus via Markovian sampling, and optimizes system prompts to cut security issues by up to 48% while preserving unit test performance, with zero-shot transfer to real prompts.
-
Flow Matching Guide and Code
Flow Matching is a generative modeling framework with mathematical foundations, design choices, extensions, and open-source PyTorch code for applications like image and text generation.