4 accepted papers
Instance-wise feature selection is a valuable tool for interpreting labeled data and the predictions of black-box models. In contrast to global feature selection techniques, instance-wise methods dynamically identify important features for each instance. A growing number of methods learn a *selector…
We propose a variation of the Hamiltonian Monte Carlo sampling (HMC) where the equations of motion are simulated for a fixed traversed distance rather than the conventional fixed simulation time. This new mechanism tends to generate proposals that have higher target probability values. The momentum…
Volume preservation is usually regarded as a necessary property for the leapfrog transition functions that are used in Hamiltonian Monte Carlo (HMC) and No-U-Turn (NUTS) samplers to guarantee convergence to the target distribution. In this work we rigorously prove that with minimal algorithmic modif…
Hamiltonian Monte Carlo (HMC) is a successful approach for sampling from continuous densities. However, it has difficulty simulating Hamiltonian dynamics with non-smooth functions, leading to poor performance. This paper is motivated by the behavior of Hamiltonian dynamics in physical systems like o…