I am currently a principal researcher at Tencent AI Lab, working on machine learning and natural language processing. Before joining Tencent in March 2018, I worked at Microsoft Research, Redmond, WA. I completed my PhD in Electrical Engineering at University of California, Los Angeles (UCLA), in June 2014, where I worked in Adaptive Systems Laboratory (ASL), supervised by Prof. Ali H. Sayed.
Email: chenjianshu at gmail dot com OR jianshuchen at global dot tencent dot com
My research interests lie at the intersection of machine learning, natural language processing, and large language models. I focus on understanding and optimizing the synergy between knowledge and reasoning to develop next-generation large language model architectures and effective learning paradigms, with the objective of achieving strong compositional generalization and reasoning capabilities. I am passionate about tackling large-scale AI research projects, collaborating with interdisciplinary teams to address complex challenges, and driving robust and effective innovations in AI. Additionally, I maintain an active interest in reinforcement learning and optimization.
For more details, see my publications (also google scholar)
- Jiaao Chen, Xiaoman Pan, Kaiqiang Song, Dian Yu, Dong Yu, Jianshu Chen, “Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models”, arXiv preprint [arXiv:2308.00304], August 2023.
- Jianshu Chen, “Learning Language Representations with Logical Inductive Bias”, Proc. International Conference on Learning Representations (ICLR), 2023.
- X. Pan, W. Yao, H. Zhang, D. Yu, D. Yu, Jianshu Chen, “Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models”, Proc. International Conference on Learning Representations (ICLR), 2023 (Spotlight).
- Y. Yang, W. Yao, H. Zhang, X. Wang, D. Yu, Jianshu Chen, “Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination”, Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.
- W. Chen, H. Wang, Jianshu Chen, Y. Zhang, H. Wang, S. Li, X. Zhou, W. Y. Wang, “TabFact: A Large-scale Dataset for Table-based Fact Verification”, Proc. International Conference on Learning Representations (ICLR), 2020
- A. Liu, Jianshu Chen, M. Yu, Y. Zhai, X. Zhou, and J. Liu, “Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search”, Proc. International Conference on Learning Representations (ICLR), April 2020. (Oral)
- B. Dai, A. Shaw, L. Li, L. Xiao, N. He, Z. Liu, Jianshu Chen, L. Song, “SBEED Learning: Convergent Control with Nonlinear Function Approximation”, Proc. International Conference on Machine Learning (ICML), 2018.
- S. Du, Jianshu Chen, L. Li, L. Xiao, D. Zhou, ``Stochastic Variance Reduction Methods for Policy Evaluation’’, Proc. International Conference on Machine Learning (ICML), 2017.