For a more complete list, please visit my [Google Scholar]
Zijing Ou, Qinliang Su, Jianxing Yu, Bang Liu, Jingwen Wang, Ruihui Zhao, Changyou Chen, Yefeng Zheng. Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval. Annual Meeting of the Association for Computational Linguistics (ACL), 2021.
Zexuan Qiu, Qinliang Su, Zijing Ou, Jianxing Yu, Changyou Chen. Unsupervised Hashing with Contrastive Information Bottleneck, International Joint Conference on Artificial Intelligence (IJCAI), 2021.
Yang Zhao, Changyou Chen. Unpaired Image-to-Image Translation via Latent Energy Transport, Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
Yufan Zhou, Zhenyi Wang, Changyou Chen, Jinhui Xu. Meta-Learning with Neural Tangent Kernels, International Conference on Learning Representations (ICLR), 2021.
Kevin J Liang, Weituo Hao, Dinghan Shen, Yufan Zhou, Weizhu Chen, Changyou Chen, Lawrence Carin. MixKD: Towards Efficient Distillation of Large-scale Language Models, International Conference on Learning Representations (ICLR), 2021.
Yufan Zhou, Changyou Chen, Jinhui Xu. Learning Manifold Implicitly via Explicit Heat Kernel Learning, Advances in Neural Information Processing Systems (NeurIPS), 2020.
Fan Yang, Alina Vereshchaka, Changyou Chen, Wen Dong. Bayesian Multi-type Mean Field Multi-agent Imitation Learning, Advances in Neural Information Processing Systems (NeurIPS), 2020.
Ping Yu, Yang Zhao, Chunyuan Li, Junsong Yuan, Changyou Chen. Structure-Aware Human-Action Generation, European Conference on Computer Vision (ECCV), 2020.
Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, Changyou Chen. Feature Quantization Improves GAN Training, International Conference on Machine Learning (ICML), 2020.
Jianyi Zhang, Yang Zhao, Changyou Chen. Variance Reduction in Stochastic Particle-Optimization Sampling, International Conference on Machine Learning (ICML), 2020.
Zhenyi Wang, Xiaoyang Wang, Bang An, Dong Yu, Changyou Chen. Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints, Annual Meeting of the Association for Computational Linguistics (ACL), 2020.
Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Dinghan Shen, Guoyin Wang, Zheng Wen, Lawrence Carin. Improving Adversarial Text Generation by Modeling the Distant Future, Annual Meeting of the Association for Computational Linguistics (ACL), 2020.
Lin Zheng, Qinliang Su, Dinghan Shen, Changyou Chen. Generative Semantic Hashing Enhanced via Boltzmann Machines, Annual Meeting of the Association for Computational Linguistics (ACL), 2020.
Bang An, Jie Lyu, Zhenyi Wang, Chunyuan Li, Changwei Hu, Fei Tan, Ruiyi Zhang, Yifan Hu, Changyou Chen. Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
Ruiyi Zhang, Changyou Chen, Xinyuan Zhang, Ke Bai and Lawrence Carin. Semantic Matching for Sequence-to-Sequence Learning, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
Jianyi Zhang, Ruiyi Zhang, Lawrence Carin, Changyou Chen. Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
Ruiyi Zhang, Changyou Chen, Zhe Gan, Zheng Wen, Wenlin Wang, Lawrence Carin. Nested-Wasserstein Self-Imitation Learning for Sequence Generation, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
Yufan Zhou, Zheshuo Li, Chunwei Ma, Mingchen Gao, Changyou Chen, Hong Zhu, Jinhui Xu. Weakly-supervised Brain Tumor Classification with Global Diagnosis Label, IEEE International Symposium on Biomedical Imaging (ISBI), 2020.
Zhenyi Wang, Yang Zhao, Ping Yu, Ruiyi Zhang, Changyou Chen. Bayesian Meta Sampling for Fast Uncertainty Adaptation, International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson. Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning, International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.
Zhenyi Wang, Ping Yu, Yang Zhao, Ruiyi Zhang, Yufan Zhou, Junsong Yuan, Changyou Chen. Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions, AAAI Conference of Artificial Intelligence (AAAI), New York, USA, 2020.
Fan Yang, Alina Vereshchaka, Yufan Zhou, Changyou Chen, Wen Dong. Variational Adversarial Kernel Learned Imitation Learning, AAAI Conference of Artificial Intelligence (AAAI), New York, USA, 2020.
Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin, Changyou Chen.
Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning,
Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019.
Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin.
Certified Adversarial Robustness with Additive Noise,
Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019.
Le Fang, Chunyuan Li, Jianfeng Gao, Wen Dong, Changyou Chen.
Implicit Deep Latent Variable Models for Text Generation,
Empirical Methods in Natural Language Processing (EMNLP), Hong Kong, China, 2019.
Wei Dong, Qinliang Su, Dinghan Shen, Changyou Chen.
Document Hashing with Mixture-Prior Generative Models,
Empirical Methods in Natural Language Processing (EMNLP), Hong Kong, China, 2019.
Tianhang Zheng, Changyou Chen, Junsong Yuan, Bo Li, Kui Ren.
PointCloud Saliency Map,
International Conference on Computer Vision (ICCV), Seoul, South Korea, 2019. [ArXiv]
Di Wang, Changyou Chen, Jinhui Xu.
Differentially Private Empirical Risk Minimization with Non-convex Loss Functions,
International Conference on Machine Learning (ICML), Long Beach, California, USA, 2019. [Online]
Jun Wen, Nenggan Zheng, Junsong Yuan, Zhefeng Gong, Changyou Chen.
Bayesian Uncertainty Matching for Unsupervised Domain Adaptation,
International Joint Conference on Artificial Intelligence (IJCAI), Macau, China, 2019. [ArXiv]
Mengdi Huai, Hongfei Xue, Chenglin Miao, Liuyi Yao, Lu Su, Changyou Chen, Aidong Zhang.
Deep Metric Learning: The Generalization Analysis and an Adaptive Algorithm,
International Joint Conference on Artificial Intelligence (IJCAI), Macau, China, 2019.
Qi Wei, Kai Fan, Wenlin Wang, Tianhang Zheng, Chakraborty Amit, Katherine Heller, Changyou Chen, Kui Ren.
InverseNet: Solving Inverse Problems of Multimedia Data with Splitting Networks,
IEEE International Conference on Multimedia and Expo (ICME), Shanghai, China, 2019.
Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, Lawrence Carin.
Topic-Guided Variational Auto-Encoder for Text Generation,
Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), Minneapolis, USA, 2019.
Chuangchuang Liu, Xianfang Sun, Changyou Chen, Paul L. Rosin, Yitong Yan, Longcun Jin, Xinyi Peng.
Multi-Scale Residual Hierarchical Dense Networks for Single Image Super-Resolution,
IEEE Access, vol. 7, pp. 60572–60583, 2019.
[Online]
Liqun Chen, Yizhe Zhang, Ruiyi Zhang, Chenyang Tao, Zhe Gan, Haichao Zhang, Bai Li, Dinghan Shen, Changyou Chen, Lawrence Carin.
Improving Sequence-to-Sequence Learning via Optimal Transport,
International Conference on Learning Representations (ICLR), New Orleans, USA, 2019. [ArXiv]
Changyou Chen, Wenlin Wang, Yizhe Zhang, Qinliang Su, Lawrence Carin.
A convergence analysis for a class of practical variance-reduction stochastic gradient MCMC,
Science China Information Science, vol.62, 2019. [ArXiv]
Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin.
Adversarial Learning of a Sampler Based on an Unnormalized Distribution,
International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Okinawa, Japan, 2019. [ArXiv]
Bai Li, Changyou Chen, Hao Liu, Lawrence Carinn.
On Connecting Stochastic Gradient MCMC and Differential Privacy,
International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Okinawa, Japan, 2019. [ArXiv]
Ruiyi Zhang, Zheng Wen, Changyou Chen, Chen Fang.
Scalable Thompson Sampling via Optimal Transport,
International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Okinawa, Japan, 2019. [PDF coming soon]
Yang Zhao, Jianyi Zhang, Changyou Chen.
Self-Adversarially Learned Bayesian Sampling,
AAAI Conference of Artificial Intelligence (AAAI), Honolulu, Hawaii, USA, 2019. [ArXiv]
Tianhang Zheng, Changyou Chen, Kui Ren.
Distributionally Adversarial Attack,
AAAI Conference of Artificial Intelligence (AAAI), Honolulu, Hawaii, USA, 2019. [ArXiv]
Chunyuan Li, Changyou Chen, Yunchen Pu, Ricardo Henao, Lawrence Carin.
Communication-Efficient Stochastic Gradient MCMC for Neural Networks,
AAAI Conference of Artificial Intelligence (AAAI), Honolulu, Hawaii, USA, 2019. [PDF coming soon]
Yifang Liu, Seyed Mahdi Shamsi, Le Fang, Changyou Chen, Nils Napp.
Deep Q-Learning for Dry Stacking Irregular Objects,
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 2018. [PDF coming soon]
Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen.
A Unified Particle-Optimization Framework for Scalable Bayesian Sampling,
Conference on Uncertainty in Artificial Intelligence (UAI), Monterey, California, USA, 2018. [PDF] [Appendix] [arxiv]
Ruiyi Zhang, Changyou Chen, Chunyuan Li, Lawrence Carin.
Policy Optimization as Wasserstein Gradient Flows,
International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. [PDF] [Appendix]
Changyou Chen, Chunyuan Li, Liqun Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin.
Continuous-Time Flows for Efficient Inference and Density Estimation,
International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. [PDF] [Appendix] [arxiv]
Liqun Chen, Shuyang Dai, Yunchen Pu, Erjin Zhou, Chunyuan Li, Qinliang Su, Changyou Chen, Lawrence Carin.
Symmetric Variational Autoencoder and Connections to Adversarial Learning,
International Conference on
Artificial Intelligence and Statistics (AISTATS), Playa Blanca, Lanzarote, Canary Islands, 2018.
[PDF] [Appendix]
Ruiyi Zhang, Chunyuan Li, Changyou Chen, Lawrence Carin.
Learning Structural Weight Uncertainty for Sequential Decision-Making,
International Conference on
Artificial Intelligence and Statistics (AISTATS), Playa Blanca, Lanzarote, Canary Islands, 2018.
[PDF] [Appendix]
Wenlin Wang, Yunchen Pu, Vinay Kumar Verma, Kai Fan, Yizhe Zhang, Changyou Chen, Piyush Rai, Lawrence Carin.
Zero-Shot Learning via Class-Conditioned Deep Generative Models,
Association for the Advancement of Artificial
Intelligence (AAAI), New Orleans, Louisiana, USA, 2018.
[PDF]
Chunyuan Li, Hao Liu, Changyou Chen, Yunchen Pu, Liqun Chen, Ricardo Henao, Lawrence Carin.
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching,
Neural Information Processing Systems (NIPS), Los Angeles, USA, 2017.
[PDF] [Appendix] [Code]
Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin.
Stochastic Gradient Monomial Gamma Sampler,
International Conference on Machine Learning (ICML), Sydney, Australia, 2017.
[PDF] [Appendix]
Zhe Gan, Chunyuan Li, Changyou Chen, Yunchen Pu, Qinliang Su, Lawrence Carin.
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling,
Annual meeting of the Association for Computational Linguistics (ACL), Vancouver, Canada, 2017.
[PDF] [Appendix] [Code]
Shengyang Sun, Changyou Chen, Lawrence Carin.
Learning Structured Weight Uncertainty in Bayesian Neural Networks,
International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, Florida, USA, 2017.
[PDF] [Appendix] [Poster]
Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin.
Stochastic Gradient MCMC with Stale Gradients,
Neural Information Processing Systems (NIPS), Barcelona, Spain, 2016.
[PDF] [Appendix] [Code]
Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Lawrence Carin.
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling,
Neural Information Processing Systems (NIPS), Barcelona, Spain, 2016.
[PDF] [Appendix]
Kar Wai Lim, Wray Buntine, Changyou Chen, Lan Du.
Nonparametric Bayesian topic modelling with the hierarchical Pitman–Yor processes,
International Journal of Approximate Reasoning (IJAR), vol. 78, pp. 172–191, 2016.
[PDF] [Appendix]
Wenlin Wang, Changyou Chen, Wenlin Chen, Lawrence Carin.
Deep Metric Learning with Data Summarization,
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Riva del Garda, Italy, 2016.
[PDF] [Code]
Yizhe Zhang, Changyou Chen, Ricardo Henao, Lawrence Carin.
Laplacian Hamiltonian Monte Carlo,
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Riva del Garda, Italy, 2016. [PDF]
Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin.
Nonlinear Statistical Learning with Truncated Gaussian Graphical Models,
International Conference on Machine Learning (ICML), New York, USA, 2016.
[PDF] [Slides] [Poster]
Chunyuan Li, Andrew Steven, Changyou Chen, Lawrence Carin.
Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification,
Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016.
[PDF] [Slides] [Poster]
Changyou Chen, David Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin.
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization,
International
Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, 2016.
[PDF] [Appendix] [Slides] [Poster] [Code]
Chunyuan Li, Changyou Chen, David Carlson, Lawrence Carin.
Preconditioned Stochastic
Gradient Langevin Dynamics for Deep Neural Networks,
AAAI Conference on Artificial Intelligence (AAAI), Phoenix, Arizona USA, 2016.
[PDF] [Appendix] [Slides]
Chunyuan Li, Changyou Chen, Kai Fan, Lawrence Carin.
High-Order Stochastic Gradient Thermostats
for Bayesian Learning of Deep Models,
AAAI Conference on Artificial Intelligence (AAAI), Phoenix, Arizona USA, 2016.
[PDF] [Appendix] [Poster]
Changyou Chen, Nan Ding, Lawrence Carin.
On the Convergence of Stochastic Gradient
MCMC Algorithms with High-Order Integrators,
Neural Information
Processing Systems (NIPS), Montreal, Canada, 2015.
[PDF] [Appendix] [Code]
Changwei Hu, Piyush Rai, Changyou Chen, Matthew Harding, Lawrence Carin.
Scalable
Bayesian Non-Negative Tensor Factorization for Massive Count Data.
European Conference on Machine Learning (ECML), 2015. (Best Student Paper Award).
[PDF] [ArXiv] [Code]
Zhe Gan, Changyou Chen, Ricardo Henao, David Carlson, Lawrence Carin.
Scalable Deep
Poisson Factor Analysis for Topic Modeling.
International Conference on Machine Learning (ICML), Lille, France, 2015.
[PDF] [Appendix] [Slides] [Poster] [Code]
Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert Skeel, Hartmut Neven.
Bayesian Sampling Using Stochastic Gradient Thermostats.
Neural Information Processing Systems (NIPS), Montreal, Canada, 2014.
[PDF] [Appendix]
Changyou Chen, Jun Zhu, Xinhua Zhang.
Bayesian Nonparametric Max-margin Clustering.
Neural Information Processing Systems (NIPS), Montreal, Canada, 2014.
[PDF] [Appendix]
Changyou Chen, Wray Buntine, Nan Ding, Lexing Xie, Lan Du.
Differential Topic Models.
IEEE Transactions
on Pattern Recognition and Machine Intelligence (TPAMI), vol. 37, no. 2, pp. 230–242, 2015.
[PDF] [Appendix]
Changyou Chen, Vinayak Rao, Wray Buntine, Yee Whye Teh.
Dependent Normalized Random
Measures.
International Conference on Machine Learning (ICML), Atlanta, USA, 2013.
[PDF] [Appendix] [Slides] [Slides (Vinayak)] [Poster]
Changyou Chen, Nan Ding, Wray Buntine.
Dependent Hierarchical Normalized Random Measures
for Dynamic Topic Modeling.
International Conference on Machine Learning (ICML),
Edinburgh, Scotland, 2012.
[PDF] [Slides] [Poster]
Lan Du, Wray Buntine, Huidong Jin, Changyou Chen.
Sequential Latent Dirichlet Allocation.
Knowledge and Information Systems, vol. 31, no. 3, pp. 475–503, 2012.
[PDF] [Link]
Changyou Chen, Lan Du, Wray Buntine.
Sampling Table Configurations for the Hierarchical
Poisson-Dirichlet Process.
The European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2011.
[PDF] [Slides] [Poster] [Errata]
Changyou Chen, Junping Zhang, Xuefang He, Zhi-hua Zhou.
Efficient Non-Parametric Kernel
Learning with Arbitrary Number of Pairwise Constraints.
International Journal of Machine Learning and Cybernetics, 2011.
[Link]
Junping Zhang, Jian Pu, Changyou Chen, Rudolf Fleischer.
Low Resolution Gait Recognition.
IEEE Transaction on System, Man, Cybernetic, Part B, 2009.
[Link]
Changyou Chen, Junping Zhang, Rudolf Fleischer.
Distance Approximating Dimension Reduction
of Riemannian Manifolds.
IEEE Transaction on System, Man, Cybernetic, Part B,
2009.
[Link]
Changyou Chen, Junping Zhang, Rudolf Fleischer.
Multilinear Tensor-based Nonparametric
Dimension Reduction.
The 3rd IAPR/IEEE International Conference on Biometrics (ICB 2009).
[Link]
Junping Zhang, Yuan Cheng, Changyou Chen.
Low Resolution Gait Recognition with High
Frequency Super Resolution.
The Tenth Pacific Rim International Conference on Artificial Intelligence (PRICAI 08).
[Link]
Changyou Chen, Junping Zhang.
An Iterative Gait Prototype Learning Algorithm based on
Tangent Distance (in Chinese).
Journal of Computer Research and Development, 2008.
[Link]
Changyou Chen, Ruiyi Zhang.
Particle Optimization in Stochastic Gradient MCMC, arXiv:1711.10927.
Ruiyi Zhang, Changyou Chen, Chunyuan Li, Lawrence Carin.
Bayesian Deep Q-Learning via Continuous-Time Flows,
Deep Reinforcement Learning Symposium, NIPS 2017. [PDF]
Changyou Chen, Wenlin Wang, Yizhe Zhang, Qinliang Su, Lawrence Carin.
A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC, arXiv:1709.01180.
Wenlin Wang, Changyou Chen, Wenqi Wang, Piyush Rai, Lawrence Carin.
Earliness-Aware Deep Convolutional Networks for Early Time Series Classification, arXiv:1611.04578.
Karwai Lim, Changyou Chen, Wray Buntine.
Twitter-Network Topic Model: A Full Bayesian
Treatment for Social Network and Text Modeling.
NIPS Workshop on Topics Model: Computation, Application, and Evaluation, 2013. [PDF] [Appendix]
Changyou Chen, Wray Buntine, Nan Ding.
Theory of Dependent Hierarchical Normalized
Random Measures.
Technical Report arXiv:1205.4159, NICTA and ANU, 2012.
[PDF] [ArXiv]