Yizhe Zhang 张轶哲
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ContactResearcher at Microsoft Research yizhe.zhang (at) microsoft.com Room 3739, Building 99 One Microsoft way, Redmond, WA 98052, United States. |
Research interest
I am working on natural language processing and deep generative models. I have particular interests in neural conversation system and text generation.
Specifically,
1). Deep generative model for text generation.
2). Improving sequence-to-sequence training with sentence-level objectives.
3). Generating neural responses with speaker consistency and external knowledge.
4). Intent-tracking mechanism for open-domain conversational agent.
5). Interplays between deep learning, sampling and NLP.
Experience
Researcher @ MSR NLP group. 2018 - present
Microsoft Research, Redmond, WA
Education
Ph.D on Machine Learning. 2013-2018
Advisor: Lawrence Carin
M.S. on Statistical Science. 2016-2018
Advisors: David Dunson, Scott Schmidler and Katherine Heller
Duke University, Durham, NC
Preprints
- A bird's-eye view on coherence, and a worm's-eye view on cohesion.
Woon Sang Cho, Pengchuan Zhang, Yizhe Zhang, Xiujun Li, Michel Galley, Mengdi Wang, Jianfeng Gao
- Consistent Dialogue Generation with Self-supervised Feature Learning.
Yizhe Zhang, Xiang Gao, Sungjin Lee, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan
Conference and workshop publications
- Improving Textual Network Embedding with Global Attention via Optimal Transport
Liqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Yizhe Zhang and Lawrence Carin — ACL 2019
- Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models
Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liqun Chen, Xin Wang, Jianfeng Gao, Lawrence Carin — ACL 2019
- Jointly Optimizing Diversity and Relevance in Neural Response Generation.
Xiang Gao, Sungjin Lee, Yizhe Zhang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan — NAACL 2019
- Improving Sequence-to-Sequence Learning via Optimal Transport.
Liqun Chen, Yizhe Zhang, Ruiyi Zhang, Chenyang Tao, Zhe Gan, Haichao Zhang, Bai Li, Dinghan Shen, Changyou Chen, Lawrence Carin — ICLR 2019
- Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization.
Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett, Bill Dolan — NIPS 2018
- Adversarial Text Generation via Feature-Mover's Distance.
Liqun Chen, Shuyang Dai, Chenyang Tao, Dinghan Shen, Zhe Gan, Haichao Zhang, Yizhe Zhang, Lawrence Carin — NIPS 2018
- Multi-Domain Joint Distribution Learning with Generative Adversarial Nets.
Yunchen Pu, Shuyang Dai, Zhe Gan, Weiyao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao, Lawrence Carin — ICML 2018
- On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms.
Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao and Lawrence Carin. — ACL 2018
- Joint Embedding of Words and Labels for Text Classification.
Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao and Lawrence Carin. — ACL 2018
- Deconvolutional Latent-Variable Model for Text Sequence Matching
Dinghan Shen, Yizhe Zhang, Ricardo Henao, Qinliang Su, Lawrence Carin. — AAAI 2018
- Zero-Shot Learning via Class-Conditioned Deep Generative Models
Wenlin Wang, Yunchen Pu, Vinay Kumar Verma, Kai Fan, Yizhe Zhang, Changyou Chen, Piyush Rai, Lawrence Carin. — AAAI 2018
- Deconvolutional Paragraph Representation Learning [supplements] [code] [data]
Yizhe Zhang, Dinghan Shen, Guoyin Wang, Ricardo Henao, Zhe Gan, Lawrence Carin — NIPS 2017.
- Triangle Generative Adversarial Networks
Zhe Gan, Liqun Chen, Weiyao Wang, Yunchen Pu, Yizhe Zhang, Lawrence Carin — NIPS 2017.
- Stochastic Gradient Monomial Gamma Sampler [supplements] [code]
Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin. — ICML 2017.
- Adversarial Feature Matching for Text Generation [supplements] [code] [param] [data]
Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Lawrence Carin. — ICML 2017.
- Towards Unifying Hamiltonian Monte Carlo and Slice Sampling [supplements] [code]
Yizhe Zhang, Xiangyu Wang, Changyou Chen, Lawrence Carin. — NIPS 2016.
- Distributed Bayesian Learning with Stochastic Gradient MCMC.
Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin. — NIPS 2016.
- Generating Text via Adversarial Training.
Yizhe Zhang, Zhe Gan, Lawrence Carin. — Workshop on Adversarial Training, NIPS, 2016.
- Learning a Hybrid Architecture for Sequence Regression and Annotation. [supplements]
Yizhe Zhang, Ricardo Henao, Jianling Zhong, Lawrence Carin, Alexander Hartemink — AAAI 2016.
- Bayesian Dictionary Learning with Gaussian Processes and Sigmoid Belief Networks. [code]
Yizhe Zhang, Ricardo Henao, Chunyuan Li, Lawrence Carin. — IJCAI 2016.
- Triply Stochastic Variational Inference for Non-linear Beta Process Factor Analysis.
Kai Fan, Yizhe Zhang, Lawrence Carin, Katherine Heller. — ICDM 2016.
- Dynamic Poisson Factor Analysis [code]
Yizhe Zhang, Ricardo Henao, Lawrence Carin. — ICDM 2016
- Laplacian Hamiltonian Monte Carlo
Yizhe Zhang, Changyou Chen, Ricardo Henao, Lawrence Carin — ECML 2016.
- Learning Dictionary with Spatial and Inter-dictionary Dependency.
Yizhe Zhang, Ricardo Henao, Chunyuan Li, Lawrence Carin. — Workshop on representation learning, NIPS, 2015.
Journal publications
- MOST+: a Motif Finding Approach Combining Genomic Sequence and Heterogeneous Genome-wide Signatures. [Source code in C++]
Yizhe Zhang, Yupeng He and Chaochun Wei. BMC Genomics, 2015.
- CRF-based Transcription Factor Binding Site Finding System. [Source code in C++]
Yupeng He, Yizhe Zhang, Guangyong Zheng and Chaochun Wei. BMC Genomics, 2012.
- Composition-based Classification of Short Metagenomic Sequences Elucidates the Landscapes of Taxonomic and Functional Enrichment of Microorganisms.
Jiemeng Liu, Haifeng Wang, Hongxing Yang, Yizhe Zhang, Jinfeng Wang, Fangqing Zhao and Ji Qi. Nucleic Acids Research, 2012.
Other projects
- Learning Infinite Mixture of Directed Acyclic Graphs
- Understanding Regulatory Element Topic via Relational Topic Modeling
Presentations
- Towards Improving the Efficiency and Scalability of MCMC inference
Ph.D. defense presentation at Duke. Unifying HMC and slice sampling and beyond.
Teaching experiences
STA 561@Duke Probabilistic Machine Learning
STA 571@Duke Advanced Machine Learning
Professional services.
PC member of NIPS, ICML, AAAI, IJCAI, AISTATS, ACL, EMNLP, NAACL, SIAM.
Students
- Ziyu Yao, Ohio State University, Research intern 2018
- Woon Sang Cho, Princeton University, Research intern 2018 (co-mentoring)
- Dinghan Shen, Duke University, Research intern 2018 (co-mentoring)
News
[May. 2019] Two papers accepted by ACL 2019
[May. 2019] Going to New Orlean for ICLR 2019
[Oct. 2018] Going to Montreal for NeurIPS 2018 this December. Reach out to me if you will also go!
[Aug. 2018] Our recent papers “Adversarial Text Generation via Feature-Mover’s Distance” and “Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization” are accepted by NIPS 2018.
[Mar. 2018] Joined Microsoft Research as a full-time researcher.