Jun Wang Photo 

Jun Wang

Professor, Computer Science, University College London

Programme Director, MSc Web Science and Big Data Analytics

Email: jun.wang (at) cs.ucl.ac.uk

Office: 3.08 66-72 Gower Street, London

Co-founder, Chief Scientist, MediaGamma Ltd.

UCL Centre for Artificial Intelligence

The latest talk on Multi-agent Learning at Peking University, Shanghai Tech, and Nanjing University.

A postdoc on Multiagent Machine Learning is wanted! See the post.

Research Interests

  • AI and Machine Learning, Multi-agent Reinforcement Learning, and Neural Generative Models;

  • Statistical Modeling of Information Retrieval, and Dynamic Information Retrieval;

  • Data Mining, Personalization, and Collaborative Filtering (Recommender Systems);

  • Computational Advertising and Real-time Bidding.

Selected Recent Papers

Explainable AI

  

Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems
Georgina Peake and Jun Wang
KDD 2018

Bayesian Learning

arXiv:1711.11511v3  

Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
Rui Luo and Yaodong Yang and Jianhong Wang and Zhanxing Zhu and Jun Wang
arXiv:1711.11511v3, 2018

Multi-agent AI

arXiv:1802.05438 

Mean Field Multi-Agent Reinforcement Learning
Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang
arXiv:1802.05438, ICML 2018

arXiv:1707.01310v3 

Learning to Design Games: Strategic Environments in Deep Reinforcement Learning
Haifeng Zhang, Jun Wang, Zhiming Zhou, Weinan Zhang, Ying Wen, Yong Yu, Wenxin Li
arXiv:1707.01310v3, IJCAI 2018

arXiv:1705.10513v1 

An Empirical Study of AI Population Dynamics with Million-agent Reinforcement Learning
Lianmin Zheng, Jiacheng Yang, Han Cai, Weinan Zhang, Jun Wang, and Yong Yu
arXiv:1709.04511v3, AAMAS 18

Text/Discrete GANs

arXiv:1709.08624v1 

Long Text Generation via Adversarial Training with Leaked Information
Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, and Jun Wang
arXiv:1709.08624v1, AAAI-2018

arXiv:1705.10513v1 

IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
Wang, Jun, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang
SIGIR (Best Paper Award Honorable Mention), 2017

arXiv:1707.01310v3 

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu
arXiv:1609.05473v6, AAAI, 2017

Neural Generative Models

arXiv:1703.02000v7 

Activation Maximization Generative Adversarial Nets
Zhiming Zhou, Han Cai, Shu Rong, Yuxuan Song, Kan Ren, Weinan Zhang, Yong Yu, and Jun Wang
arXiv:1703.02000v7, ICLR-2018

arXiv:1712.00504 

A Neural Stochastic Volatility Model
Rui Luo, Jun Wang, Weinan Zhang and Xiaojun Xu
arXiv:1712.00504, AAAI-2018

arXiv:1706.05446v3 

Adversarial Variational Inference for Tweedie Compound Poisson Models
Yaodong Yang, Sergey Demyanov, Yunayuan Liu, Jun Wang
arXiv:1706.05446v3, ICML Workshop on Implicit Models, 2017

AutoML

arXiv:1707.04873v1 

Reinforcement Learning for Architecture Search by Network Transformation
Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang
arXiv:1707.04873v1, AAAI-2018

For a full list, see my Google Scholar page.

Demos

Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems
MAgent: A Many-Agent Reinforcement Learning Research Platform for Artificial Collective Intelligence
Lianmin Zheng, Jiacheng Yang, Han Cai, Weinan Zhang, Jun Wang, and Yong Yu
NIPS17 demo
arXiv:1712.00600, 2017
Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
Peng Peng, Ying Wen, Yaodong Yang, Quan Yuan, Zhenkun Tang, Haitao Long, Jun Wang
arXiv:1703.10069v4, 2017
City Traffic Simulator/Optimizer
Each moving dot is driven by a neural network-based reinforcement learning agent
Collaborative Bots Sorting Parcels

Books

 

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
Jun Wang, Weinan Zhang and Shuai Yuan (2017)
Foundations and TrendsĀ® in Information Retrieval: Vol. 11: No. 4-5, pp 297-435.

 

Dynamic Information Retrieval Modeling
Grace Hui Yang, Marc Sloan, and Jun Wang (2016)
Synthesis Lectures on Information Concepts, Retrieval, and Services, 2016.

Social Media