156 citations · 362 across the 22 of their papers we have counts for
10 papers · 1 filter
Structural Re-weighting Improves Graph Domain Adaptation
Shikun Liu, Tianchun Li, Yongbin Feng +4
In many real-world applications, graph-structured data used for training and testing have differences in distribution, such as in high energy physics (HEP) where simulation data us…
Algorithms and Theory for Supervised Gradual Domain Adaptation
Jing Dong, Shiji Zhou, Baoxiang Wang +1
The phenomenon of data distribution evolving over time has been observed in a range of applications, calling the needs of adaptive learning algorithms. We thus study the problem of…
On Learning Invariant Representation for Domain Adaptation
Han Zhao, Remi Tachet des Combes, Kun Zhang +1
Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achi…
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations
Chen Liang, Jianbo Ye, Han Zhao +2
Strict partial order is a mathematical structure commonly seen in relational data. One obstacle to extracting such type of relations at scale is the lack of large-scale labels for…
Learning Markov Chain in Unordered Dataset
Yao-Hung Hubert Tsai, Han Zhao, Ruslan Salakhutdinov +1
The assumption that data samples are independently identically distributed is the backbone of many learning algorithms. Nevertheless, datasets often exhibit rich structure in pract…
Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint
Han Zhao, Geoff Gordon
Symmetric nonnegative matrix factorization has found abundant applications in various domains by providing a symmetric low-rank decomposition of nonnegative matrices. In this paper…
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks
Han Zhao, Shanghang Zhang, Guanhang Wu +3
While domain adaptation has been actively researched in recent years, most theoretical results and algorithms focus on the single-source-single-target adaptation setting. Naive app…
Principled Hybrids of Generative and Discriminative Domain Adaptation
Han Zhao, Zhenyao Zhu, Junjie Hu +2
We propose a probabilistic framework for domain adaptation that blends both generative and discriminative modeling in a principled way. Under this framework, generative and discrim…
Linear Time Computation of Moments in Sum-Product Networks
Han Zhao, Geoff Gordon
Bayesian online algorithms for Sum-Product Networks (SPNs) need to update their posterior distribution after seeing one single additional instance. To do so, they must compute mome…
A Sober Look at Spectral Learning
Han Zhao, Pascal Poupart
Spectral learning recently generated lots of excitement in machine learning, largely because it is the first known method to produce consistent estimates (under suitable conditions…