In a recent work, [20] studied the following "fair" variants of classical clustering problems such as k-means and k-median: given a set of n data points in R

Interest in this problem derives from applications in machine learning, Bayesian statistics, and optimization where, rather than obtaining all the observations at once, one constantly acquires new data, and must continuously update the distribution.

Neural Information Processing Systems http://nips.cc/

Hamiltonian Monte Carlo (HMC) is a widely deployed method to sample from highdimensional distributions in Statistics and Machine learning. HMC is known to run very efficiently in practice and its popular second-order "leapfrog" implementation has long been conjectured to run in d

Neural Information Processing Systems http://nips.cc/

Interest in this problem derives from applications in machine learning, Bayesian statistics, and optimization where, rather than obtaining all the observations at once, one constantly acquires new data, and must continuously update the distribution.