224 citations · 3.2k across the 170 of their papers we have counts for
24 papers · 1 filter
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
Shuyu Cheng, Guoqiang Wu, Jun Zhu
Zeroth-order (ZO) optimization is widely used to handle challenging tasks, such as query-based black-box adversarial attacks and reinforcement learning. Various attempts have been…
Quasi-Bayesian Dual Instrumental Variable Regression
Ziyu Wang, Yuhao Zhou, Tongzheng Ren +1
Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quant…
Implicit Normalizing Flows
Cheng Lu, Jianfei Chen, Chongxuan Li +2
Normalizing flows define a probability distribution by an explicit invertible transformation . In this work, we present implicit…
Further Analysis of Outlier Detection with Deep Generative Models
Ziyu Wang, Bin Dai, David Wipf +1
The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applica…
Nonparametric Score Estimators
Yuhao Zhou, Jiaxin Shi, Jun Zhu
Estimating the score, i.e., the gradient of log density function, from a set of samples generated by an unknown distribution is a fundamental task in inference and learning of prob…
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models
Ziyu Wang, Shuyu Cheng, Yueru Li +2
Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative. In this pa…
VFlow: More Expressive Generative Flows with Variational Data Augmentation
Jianfei Chen, Cheng Lu, Biqi Chenli +2
Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations. However, tractability imposes architec…
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu
Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points. We explore an al…
Function Space Particle Optimization for Bayesian Neural Networks
Ziyu Wang, Tongzheng Ren, Jun Zhu +1
While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature. To…
Semi-crowdsourced Clustering with Deep Generative Models
Yucen Luo, Tian Tian, Jiaxin Shi +2
We consider the semi-supervised clustering problem where crowdsourcing provides noisy information about the pairwise comparisons on a small subset of data, i.e., whether a sample p…
A Spectral Approach to Gradient Estimation for Implicit Distributions
Jiaxin Shi, Shengyang Sun, Jun Zhu
Recently there have been increasing interests in learning and inference with implicit distributions (i.e., distributions without tractable densities). To this end, we develop a gra…
Learning Random Fourier Features by Hybrid Constrained Optimization
Jianqiao Wangni, Jingwei Zhuo, Jun Zhu
The kernel embedding algorithm is an important component for adapting kernel methods to large datasets. Since the algorithm consumes a major computation cost in the testing phase,…
Message Passing Stein Variational Gradient Descent
Jingwei Zhuo, Chang Liu, Jiaxin Shi +3
Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation…
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Jianfei Chen, Jun Zhu, Le Song
Graph convolutional networks (GCNs) are powerful deep neural networks for graph-structured data. However, GCN computes the representation of a node recursively from its neighbors,…
ZhuSuan: A Library for Bayesian Deep Learning
Jiaxin Shi, Jianfei Chen, Jun Zhu +4
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep…
Kernel Implicit Variational Inference
Jiaxin Shi, Shengyang Sun, Jun Zhu
Recent progress in variational inference has paid much attention to the flexibility of variational posteriors. One promising direction is to use implicit distributions, i.e., distr…
Scalable Inference for Nested Chinese Restaurant Process Topic Models
Jianfei Chen, Jun Zhu, Jie Lu +1
Nested Chinese Restaurant Process (nCRP) topic models are powerful nonparametric Bayesian methods to extract a topic hierarchy from a given text corpus, where the hierarchical stru…
Kernel Bayesian Inference with Posterior Regularization
Yang Song, Jun Zhu, Yong Ren
We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution. This equ…
Scaling up Dynamic Topic Models
Arnab Bhadury, Jianfei Chen, Jun Zhu +1
Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data. To do posterior inference of DTMs, existing methods a…
WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation
Jianfei Chen, Kaiwei Li, Jun Zhu +1
Developing efficient and scalable algorithms for Latent Dirichlet Allocation (LDA) is of wide interest for many applications. Previous work has developed an O(1) Metropolis-Hasting…
Fast Sampling for Bayesian Max-Margin Models
Wenbo Hu, Jun Zhu, Bo Zhang
Bayesian max-margin models have shown superiority in various practical applications, such as text categorization, collaborative prediction, social network link prediction and crowd…
Gibbs Max-margin Topic Models with Data Augmentation
Jun Zhu, Ning Chen, Hugh Perkins +1
Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated i…
MedLDA: A General Framework of Maximum Margin Supervised Topic Models
Jun Zhu, Amr Ahmed, Eric P. Xing
Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents. Existing models apply the likelihood-based esti…
Maximum Entropy Discrimination Markov Networks
Jun Zhu, Eric P. Xing
In this paper, we present a novel and general framework called {\it Maximum Entropy Discrimination Markov Networks} (MaxEnDNet), which integrates the max-margin structured learning…