165 citations · 950 across the 71 of their papers we have counts for
6 papers · 1 filter
Precise Error Rates for Computationally Efficient Testing
Ankur Moitra, Alexander S. Wein
We revisit the fundamental question of simple-versus-simple hypothesis testing with an eye towards computational complexity, as the statistically optimal likelihood ratio test is o…
Optimality and Sub-optimality of PCA I: Spiked Random Matrix Models
Amelia Perry, Alexander S. Wein, Afonso S. Bandeira +1
A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, introduced by Johnstone, in which a prominent eigenvector (or "spike") is…
Rates of estimation for determinantal point processes
Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet +1
Determinantal point processes (DPPs) have wide-ranging applications in machine learning, where they are used to enforce the notion of diversity in subset selection problems. Many e…
Learning Determinantal Point Processes with Moments and Cycles
John Urschel, Victor-Emmanuel Brunel, Ankur Moitra +1
Determinantal Point Processes (DPPs) are a family of probabilistic models that have a repulsive behavior, and lend themselves naturally to many tasks in machine learning where retu…
Maximum likelihood estimation of determinantal point processes
Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet +1
Determinantal point processes (DPPs) have wide-ranging applications in machine learning, where they are used to enforce the notion of diversity in subset selection problems. Many e…
Optimality and Sub-optimality of PCA for Spiked Random Matrices and Synchronization
Amelia Perry, Alexander S. Wein, Afonso S. Bandeira +1
A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, in which a prominent eigenvector is planted into a random matrix. These d…