Data-driven Adaptive Network Models: Quantitative Group Testing The quantitative group testing (QGT) problem aims at learning an underlying binary vector x of length n with a sparsity parameter k. The information acquisition process about x involves conducting pooled measurements, also known as group tests, where the outcome reveals the number of ones within the pool. […]
Empirical Network Optimization via Non-uniform Sampling Large-scale distributed optimization is a key component of the design of multi-scale network protocols. Traditionally, architecture-level hyperparameters and protocol parameters are optimized manually at run-time, which requires a human fine-tuning step to produce an optimized network performance metric. We will capitalize on the abundance of data and past design […]
Empirical Network Optimization via Distributed Zeroth-order Optimization Distributed zeroth-order optimization forms the backbone of empirical network optimization, and has wide applications in federated learning, where multiple agents use their local data and computation to collectively train a model. There are two main challenges in practical network optimization tasks. The first is the unavailable gradient of […]
