306 citations · 2.2k across the 100 of their papers we have counts for
7 papers · 1 filter
Two Methods For Wild Variational Inference
Qiang Liu, Yihao Feng
Variational inference provides a powerful tool for approximate probabilistic in- ference on complex, structured models. Typical variational inference methods, however, require to u…
Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
Dilin Wang, Qiang Liu
We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively ad…
Black-box Importance Sampling
Qiang Liu, Jason D. Lee
Importance sampling is widely used in machine learning and statistics, but its power is limited by the restriction of using simple proposals for which the importance weights can be…
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Qiang Liu, Dilin Wang
We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. Our method iteratively transports a set of parti…
Bootstrap Model Aggregation for Distributed Statistical Learning
Jun Han, Qiang Liu
In distributed, or privacy-preserving learning, we are often given a set of probabilistic models estimated from different local repositories, and asked to combine them into a singl…
Local Perturb-and-MAP for Structured Prediction
Gedas Bertasius, Qiang Liu, Lorenzo Torresani +1
Conditional random fields (CRFs) provide a powerful tool for structured prediction, but cast significant challenges in both the learning and inference steps. Approximation techniqu…