5 papers
Confidence Sets for Multidimensional Scaling
Siddharth Vishwanath, Ery Arias-Castro
We develop a formal statistical framework for classical multidimensional scaling (CMDS) applied to noisy dissimilarity data. We establish distributional convergence results for the…
Signal Recovery from Random Dot-Product Graphs Under Local Differential Privacy
Siddharth Vishwanath, Jonathan Hehir
We consider the problem of recovering latent information from graphs under -edge local differential privacy where the presence of relationships/edges between two users…
Minimax Optimality of Classical Scaling Under General Noise Conditions
Siddharth Vishwanath, Ery Arias-Castro
We establish the consistency of classical scaling under a broad class of noise models, encompassing many commonly studied cases in literature. Our approach requires only finite fou…
Repelling-Attracting Hamiltonian Monte Carlo
Siddharth Vishwanath, Hyungsuk Tak
We propose a variant of Hamiltonian Monte Carlo (HMC), called the Repelling-Attracting Hamiltonian Monte Carlo (RAHMC), for sampling from multimodal distributions. The key idea tha…
Stability of Sequential Lateration and of Stress Minimization in the Presence of Noise
Ery Arias-Castro, Siddharth Vishwanath
Sequential lateration is a class of methods for multidimensional scaling where a suitable subset of nodes is first embedded by some method, e.g., a clique embedded by classical sca…