Challenges of big data analysis
Big Data bring new opportunities to modern society and challenges to data scientists. On the
one hand, Big Data hold great promises for discovering subtle population patterns and …
one hand, Big Data hold great promises for discovering subtle population patterns and …
Transformers as statisticians: Provable in-context learning with in-context algorithm selection
Y Bai, F Chen, H Wang, C ** from saddle points—online stochastic gradient for tensor decomposition
We analyze stochastic gradient descent for optimizing non-convex functions. In many cases
for non-convex functions the goal is to find a reasonable local minimum, and the main …
for non-convex functions the goal is to find a reasonable local minimum, and the main …
Phase retrieval via Wirtinger flow: Theory and algorithms
We study the problem of recovering the phase from magnitude measurements; specifically,
we wish to reconstruct a complex-valued signal about which we have phaseless samples of …
we wish to reconstruct a complex-valued signal about which we have phaseless samples of …
Structured regularizers for high-dimensional problems: Statistical and computational issues
Regularization is a widely used technique throughout statistics, machine learning, and
applied mathematics. Modern applications in science and engineering lead to massive and …
applied mathematics. Modern applications in science and engineering lead to massive and …
Guaranteed matrix completion via non-convex factorization
Matrix factorization is a popular approach for large-scale matrix completion. The optimization
formulation based on matrix factorization, even with huge size, can be solved very efficiently …
formulation based on matrix factorization, even with huge size, can be solved very efficiently …
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
We provide novel theoretical results regarding local optima of regularized M-estimators,
allowing for nonconvexity in both loss and penalty functions. Under restricted strong …
allowing for nonconvexity in both loss and penalty functions. Under restricted strong …