Zhitang Chen

发表

AYESIAN T RANSFER L EARNING FOR S EQUENTIAL D ATA M ODELING

P. Poupart, M. Schaekermann, E. Law, 2017 .

Appendix for Multidomain Discriminant Analysis

Zhitang Chen, Kun Zhang, Shoubo Hu, 2019 .

DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images

Zhitang Chen, Furui Liu, Tianshuo Cong, 2019 .

Reframed GES with a Neural Conditional Dependence Measure

Jiji Zhang, Zhitang Chen, Xinwei Shen, 2022, ArXiv.

CausalVAE: Structured Causal Disentanglement in Variational Autoencoder

Jianye Hao, Zhitang Chen, Xinwei Shen, 2020, ArXiv.

Reframed GES with a Neural Conditional Dependence Measure

Jiji Zhang, Zhitang Chen, Xinwei Shen, 2022, UAI.

A Unified Framework for Layout Pattern Analysis with Deep Causal Estimation

Zhitang Chen, Bei Yu, Shoubo Hu, 2021, 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD).

Online Bayesian Transfer Learning for Sequential Data Modeling

Zhitang Chen, Pascal Poupart, George Trimponias, 2016, ICLR.

Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space Embeddings

Zhitang Chen, Pascal Poupart, Yanhui Geng, 2016, AISTATS.

A Kernel Embedding–Based Approach for Nonstationary Causal Model Inference

Zhitang Chen, Lai-Wan Chan, Shoubo Hu, 2018, Neural Computation.

Causality in Linear Nongaussian Acyclic Models in the Presence of Latent Gaussian Confounders

Zhitang Chen, Laiwan Chan, Zhitang Chen, 2013, Neural Computation.

Causal discovery with scale-mixture model for spatiotemporal variance dependencies

Zhitang Chen, Lai-Wan Chan, Kun Zhang, 2012, NIPS.

Causal Discovery for Linear Non-Gaussian Acyclic Models in the Presence of Latent Gaussian Confounders

Zhitang Chen, Lai-Wan Chan, L. Chan, 2012, LVA/ICA.

A causal direction test for heterogeneous populations

Zhitang Chen, Yanhui Geng, Vahid Partovi Nia, 2020, Machine Learning with Applications.

Causal World Models by Unsupervised Deconfounding of Physical Dynamics pdf

Zhitang Chen, Xu Chen, Jun Wang, 2020, ArXiv.

High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning pdf

Zhitang Chen, Haitham Bou-Ammar, Jan Peters, 2021, ArXiv.

Masked Gradient-Based Causal Structure Learning

Zhitang Chen, Ignavier Ng, Shengyu Zhu, 2019, SDM.

Asymptotically Optimal One- and Two-Sample Testing With Kernels pdf

Zhitang Chen, Pengfei Yang, Biao Chen, 2019, IEEE Transactions on Information Theory.

Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit pdf

Zhitang Chen, Biao Chen, Pengfei Yang, 2018, AISTATS.

Clustering Causal Additive Noise Models pdf

Zhitang Chen, Yanhui Geng, Vahid Partovi Nia, 2020, ArXiv.

Generalizable Information Theoretic Causal Representation

Jianye Hao, Zhitang Chen, Furui Liu, 2022, ArXiv.

Out-of-distribution Generalization with Causal Invariant Transformations

Mingyang Yi, Zhitang Chen, Shengyu Zhu, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Domain Generalization via Multidomain Discriminant Analysis

Zhitang Chen, Lai-Wan Chan, Kun Zhang, 2019, UAI.

Causal Discovery with Reinforcement Learning pdf

Zhitang Chen, Shengyu Zhu, Zhitang Chen, 2019, ICLR.

Ordering-Based Causal Discovery with Reinforcement Learning

Zhitang Chen, Jianye Hao, Liangjun Ke, 2021, IJCAI.

CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models

Zhitang Chen, Jun Wang, Xinwei Shen, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Model-free inference of diffusion networks using RKHS embeddings

L. Chan, Zhitang Chen, Yanhui Geng, 2019, Data Mining and Knowledge Discovery.

Seq2Img: A sequence-to-image based approach towards IP traffic classification using convolutional neural networks

Zhitang Chen, Jian Li, Yanhui Geng, 2017, 2017 IEEE International Conference on Big Data (Big Data).

On Low-Rank Directed Acyclic Graphs and Causal Structure Learning

Yangbo He, Jiji Zhang, Yue Liu, 2020, IEEE Transactions on Neural Networks and Learning Systems.

gCastle: A Python Toolbox for Causal Discovery

Zhitang Chen, Ignavier Ng, Marcus Kalander, 2021, ArXiv.

A Graph Autoencoder Approach to Causal Structure Learning pdf

Zhitang Chen, Ignavier Ng, Shengyu Zhu, 2019, ArXiv.

Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks

P. Poupart, Zhitang Chen, Shihan Xiao, 2021, ArXiv.

A Collaborative Learning Based Approach for Parameter Configuration of Cellular Networks

Zhitang Chen, Xueying Guo, Xin Liu, 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

Disentangled Generative Causal Representation Learning

Zhitang Chen, Xinwei Shen, Furui Liu, 2020, ArXiv.

Stable Learning via Differentiated Variable Decorrelation

Tong Zhang, Zheyan Shen, Peng Cui, 2020, KDD.

Reweighted Interacting Langevin Diffusions: an Accelerated Sampling Methodfor Optimization

Junlong Lyu, Wenlong Lyu, Jianye Hao, 2023, arXiv.org.

Weakly Supervised Disentangled Generative Causal Representation Learning pdf

Zhitang Chen, Hanze Dong, Tong Zhang, 2020, J. Mach. Learn. Res..

Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning pdf

Thomas G. Dietterich, Zhitang Chen, George Trimponias, 2018, ICML.

Predicting future traffic using Hidden Markov Models

Zhitang Chen, Yanhui Geng, Jiayao Wen, 2016, 2016 IEEE 24th International Conference on Network Protocols (ICNP).

Causal Inference and Mechanism Clustering of a Mixture of Additive Noise Models

Zhitang Chen, Lai-Wan Chan, Yanhui Geng, 2018, NeurIPS.

Contrastive-ACE: Domain Generalization Through Alignment of Causal Mechanisms pdf

Zhitang Chen, Jianye Hao, Shoubo Hu, 2021, IEEE Transactions on Image Processing.

Exponentially Consistent Kernel Two-Sample Tests pdf

Zhitang Chen, Biao Chen, Shengyu Zhu, 2018, ArXiv.

Causal Discovery via Reproducing Kernel Hilbert Space Embeddings

Bernhard Schölkopf, Zhitang Chen, Lai-Wan Chan, 2014, Neural Computation.

Discriminative training of feed-forward and recurrent sum-product networks by extended Baum-Welch

Zhitang Chen, Pascal Poupart, Abdullah Rashwan, 2020, Int. J. Approx. Reason..

Efficient Bayesian Optimization with Deep Kernel Learning and Transformer Pre-trained on Multiple Heterogeneous Datasets

Wenlong Lyu, Zhitang Chen, Shoubo Hu, 2023, ArXiv.

Online flow size prediction for improved network routing

Zhitang Chen, Pascal Poupart, Yanhui Geng, 2016, 2016 IEEE 24th International Conference on Network Protocols (ICNP).

CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models

Zhitang Chen, Jianye Hao, Jun Wang, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

RCANet: Root Cause Analysis via Latent Variable Interaction Modeling for Yield Improvement

Evangeline F. Y. Young, Jianye Hao, Zhitang Chen, 2022, 2022 IEEE International Test Conference (ITC).

Convergence guarantee for consistency models

Junlong Lyu, Zhitang Chen, Shoubo Feng, 2023, ArXiv.

Causal Discovery by Kernel Intrinsic Invariance Measure pdf

Zhitang Chen, Yue Liu, Shengyu Zhu, 2019, ArXiv.

Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method

Zhitang Chen, Lai-Wan Chan, Kun Zhang, 2013, 2013 IEEE 13th International Conference on Data Mining.

Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration

Xin Liu, Zhitang Chen, Xueying Guo, 2018, 2019 IEEE International Conference on Big Data (Big Data).