181 citations · 1.7k across the 107 of their papers we have counts for
39 papers · 1 filter
Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling
Tianqi Chen, Mingyuan Zhou
Learning to denoise has emerged as a prominent paradigm to design state-of-the-art deep generative models for natural images. How to use it to model the distributions of both conti…
POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models
Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng +2
Through prompting, large-scale pre-trained models have become more expressive and powerful, gaining significant attention in recent years. Though these big models have zero-shot ca…
A Prototype-Oriented Clustering for Domain Shift with Source Privacy
Korawat Tanwisuth, Shujian Zhang, Pengcheng He +1
Unsupervised clustering under domain shift (UCDS) studies how to transfer the knowledge from abundant unlabeled data from multiple source domains to learn the representation of the…
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport
Dandan Guo, Long Tian, He Zhao +2
Few-shot classification aims to learn a classifier to recognize unseen classes during training, where the learned model can easily become over-fitted based on the biased distributi…
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
Dandan Guo, Zhuo Li, Meixi Zheng +3
Imbalanced data pose challenges for deep learning based classification models. One of the most widely-used approaches for tackling imbalanced data is re-weighting, where training s…
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning
Zhendong Wang, Jonathan J Hunt, Mingyuan Zhou
Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously collected static dataset, is an important paradigm of RL. Standard RL methods often pe…
Diffusion-GAN: Training GANs with Diffusion
Zhendong Wang, Huangjie Zheng, Pengcheng He +2
Generative adversarial networks (GANs) are challenging to train stably, and a promising remedy of injecting instance noise into the discriminator input has not been very effective…
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
Dongsheng Wang, Dandan Guo, He Zhao +4
A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. It is…
Bayesian Graph Contrastive Learning
Arman Hasanzadeh, Mohammadreza Armandpour, Ehsan Hajiramezanali +3
Contrastive learning has become a key component of self-supervised learning approaches for graph-structured data. Despite their success, existing graph contrastive learning methods…
TopicNet: Semantic Graph-Guided Topic Discovery
Zhibin Duan, Yishi Xu, Bo Chen +3
Existing deep hierarchical topic models are able to extract semantically meaningful topics from a text corpus in an unsupervised manner and automatically organize them into a topic…
Alignment Attention by Matching Key and Query Distributions
Shujian Zhang, Xinjie Fan, Huangjie Zheng +2
The neural attention mechanism has been incorporated into deep neural networks to achieve state-of-the-art performance in various domains. Most such models use multi-head self-atte…
A Prototype-Oriented Framework for Unsupervised Domain Adaptation
Korawat Tanwisuth, Xinjie Fan, Huangjie Zheng +4
Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space. To avoid the sampl…
Learning Prototype-oriented Set Representations for Meta-Learning
Dandan Guo, Long Tian, Minghe Zhang +2
Learning from set-structured data is a fundamental problem that has recently attracted increasing attention, where a series of summary networks are introduced to deal with the set…
Bayesian Attention Belief Networks
Shujian Zhang, Xinjie Fan, Bo Chen +1
Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks. Most such models use deterministic attention while stochastic attention is less exp…
Probabilistic Margins for Instance Reweighting in Adversarial Training
Qizhou Wang, Feng Liu, Bo Han +5
Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more cr…
ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables
Alek Dimitriev, Mingyuan Zhou
Estimating the gradients for binary variables is a task that arises frequently in various domains, such as training discrete latent variable models. What has been commonly used is…
Contrastive Attraction and Contrastive Repulsion for Representation Learning
Huangjie Zheng, Xu Chen, Jiangchao Yao +7
Contrastive learning (CL) methods effectively learn data representations in a self-supervision manner, where the encoder contrasts each positive sample over multiple negative sampl…
Partition-Guided GANs
Mohammadreza Armandpour, Ali Sadeghian, Chunyuan Li +1
Despite the success of Generative Adversarial Networks (GANs), their training suffers from several well-known problems, including mode collapse and difficulties learning a disconne…
Contextual Dropout: An Efficient Sample-Dependent Dropout Module
Xinjie Fan, Shujian Zhang, Korawat Tanwisuth +2
Dropout has been demonstrated as a simple and effective module to not only regularize the training process of deep neural networks, but also provide the uncertainty estimation for…
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
Haoang Chi, Feng Liu, Bo Han +6
In novel class discovery (NCD), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes. However,…
Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference
Ali Lotfi Rezaabad, Rahi Kalantari, Sriram Vishwanath +2
Efficient modeling of relational data arising in physical, social, and information sciences is challenging due to complicated dependencies within the data. In this work, we build o…
Convex Polytope Trees
Mohammadreza Armandpour, Mingyuan Zhou
A decision tree is commonly restricted to use a single hyperplane to split the covariate space at each of its internal nodes. It often requires a large number of nodes to achieve h…
MCMC-Interactive Variational Inference
Quan Zhang, Huangjie Zheng, Mingyuan Zhou
Leveraging well-established MCMC strategies, we propose MCMC-interactive variational inference (MIVI) to not only estimate the posterior in a time constrained manner, but also faci…
Implicit Distributional Reinforcement Learning
Yuguang Yue, Zhendong Wang, Mingyuan Zhou
To improve the sample efficiency of policy-gradient based reinforcement learning algorithms, we propose implicit distributional actor-critic (IDAC) that consists of a distributiona…