2 citations · 3 across the 2 of their papers we have counts for
5 papers
Couplings for Multinomial Hamiltonian Monte Carlo
Kai Xu, Tor Erlend Fjelde, Charles Sutton +1
Hamiltonian Monte Carlo (HMC) is a popular sampling method in Bayesian inference. Recently, Heng & Jacob (2019) studied Metropolis HMC with couplings for unbiased Monte Carlo estim…
Telescoping Density-Ratio Estimation
Benjamin Rhodes, Kai Xu, Michael U. Gutmann
Density-ratio estimation via classification is a cornerstone of unsupervised learning. It has provided the foundation for state-of-the-art methods in representation learning and ge…
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
Mohamed Tarek, Kai Xu, Martin Trapp +2
We present the preliminary high-level design and features of DynamicPPL.jl, a modular library providing a lightning-fast infrastructure for probabilistic programming. Besides a com…
Generative Ratio Matching Networks
Akash Srivastava, Kai Xu, Michael U. Gutmann +1
Deep generative models can learn to generate realistic-looking images, but many of the most effective methods are adversarial and involve a saddlepoint optimization, which requires…
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge
Kai Xu, Dae Hoon Park, Chang Yi +1
Interpreting black box classifiers, such as deep networks, allows an analyst to validate a classifier before it is deployed in a high-stakes setting. A natural idea is to visualize…