165 citations · 950 across the 71 of their papers we have counts for
6 papers · 1 filter
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…
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications
Linus Hamilton, Frederic Koehler, Ankur Moitra
Markov random fields area popular model for high-dimensional probability distributions. Over the years, many mathematical, statistical and algorithmic problems on them have been st…
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane +3
We study the fundamental problem of learning the parameters of a high-dimensional Gaussian in the presence of noise -- where an -fraction of our samples were chosen by…
Being Robust (in High Dimensions) Can Be Practical
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane +3
Robust estimation is much more challenging in high dimensions than it is in one dimension: Most techniques either lead to intractable optimization problems or estimators that can t…
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…