1 citations · 1 across the 5 of their papers we have counts for
5 papers
MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning
Harrison Rush, Vincent Davis, Simone Antonelli +3
We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity? We cast this as a bu…
Predicting Channel Closures in the Lightning Network with Machine Learning
Simone Antonelli, Vincent Davis, Harrison Rush +4
The Lightning Network (LN) is a second-layer protocol for Bitcoin designed to enable fast and cost-efficient off-chain transactions. Channels in the LN can be closed either by mutu…
Verification of Lightning Network Channel Balances with Trusted Execution Environments (TEE)
Vikash Singh, Barrett Little, Philip Hayes +4
Verifying the private liquidity state of Lightning Network (LN) channels is desirable for auditors, service providers, and network participants who need assurance of financial capa…
Bayesian Binary Search
Vikash Singh, Matthew Khanzadeh, Vincent Davis +4
We present Bayesian Binary Search (BBS), a novel probabilistic variant of the classical binary search/bisection algorithm. BBS leverages machine learning/statistical techniques to…
Channel Balance Interpolation in the Lightning Network via Machine Learning
Vincent Davis, Emanuele Rossi, Vikash Singh
The Bitcoin Lightning Network is a Layer 2 payment protocol that addresses Bitcoin's scalability by facilitating quick and cost effective transactions through payment channels. Thi…