15 citations · 21 across the 7 of their papers we have counts for
7 papers
Posterior Sampling in High Dimension via Diffusion Processes
Andrea Montanari, Yuchen Wu
Sampling from the posterior is a key technical problem in Bayesian statistics. Rigorous guarantees are difficult to obtain for Markov Chain Monte Carlo algorithms of common use. In…
Lower Bounds for the Convergence of Tensor Power Iteration on Random Overcomplete Models
Yuchen Wu, Kangjie Zhou
Tensor decomposition serves as a powerful primitive in statistics and machine learning, and has numerous applications in problems such as learning latent variable models or mixture…
Fundamental Limits of Low-Rank Matrix Estimation with Diverging Aspect Ratios
Andrea Montanari, Yuchen Wu
We consider the problem of estimating the factors of a low-rank matrix, when this is corrupted by additive Gaussian noise. A special example of our setting corresponds…
Statistically Optimal First Order Algorithms: A Proof via Orthogonalization
Andrea Montanari, Yuchen Wu
We consider a class of statistical estimation problems in which we are given a random data matrix (and possibly some labels ${\boldsymb…
Streaming Belief Propagation for Community Detection
Yuchen Wu, MohammadHossein Bateni, Andre Linhares +4
The community detection problem requires to cluster the nodes of a network into a small number of well-connected "communities". There has been substantial recent progress in charac…
The estimation error of general first order methods
Michael Celentano, Andrea Montanari, Yuchen Wu
Modern large-scale statistical models require to estimate thousands to millions of parameters. This is often accomplished by iterative algorithms such as gradient descent, projecte…
Higher-Order Kullback-Leibler Aggregation of Markov Chains
Bernhard C. Geiger, Yuchen Wu
We consider the problem of reducing a first-order Markov chain on a large alphabet to a higher-order Markov chain on a small alphabet. We present information-theoretic cost functio…