Ruohan Wang

About Me

I’m a research scientist at Institute for Infocomm Research, A*STAR. Previously, I was a postdoctoral researcher at UCL’s Intelligent Systems Group, supervised by Prof. Massimiliano Pontil. I completed my Ph.D. in machine learning at Imperial College, supervised by Prof. Yiannis Demiris and Prof. Carlo Ciliberto. I was funded by Singapore National Science Scholarship.

Research Interests

I have broad interests in topics of machine learning, including representation learning, meta-learning, and imitation learning. The goal of my research is to create robust ML systems that could efficiently leverage past experiences and existing knowledge for future tasks.

Publications

• The Role of Global Labels in Few-Shot Classification and How to Infer Them, R Wang, M Pontil, C Ciliberto, NeurIPS 2021
• Structured prediction for conditional meta-learning, R Wang, Y Demiris, C Ciliberto, NeurIPS 2020
• Random expert distillation: Imitation learning via expert policy support estimation, R Wang, C Ciliberto, P Amadori, Y Demiris, ICML 2019
• Support-weighted adversarial imitation learning, R Wang, C Ciliberto, P Amadori, Y Demiris, Neurips 2019 LIRE Workshop
• Real-time workload classification during driving using hypernetworks, R Wang, PV Amadori, Y Demiris, IROS 2018
• Multi-modal robot apprenticeship: imitation learning using linearly decayed dmp+ in a human-robot dialogue system, Y Wu, R Wang, LF D’Haro, RE Banchs, KP Tee, IROS 2018
• Magan: Margin adaptation for generative adversarial networks, R Wang, A Cully, HJ Chang, Y Demiris, arXiv preprint 2017
• Dynamic movement primitives plus: For enhanced reproduction quality and efficient trajectory modification using truncated kernels and local biases, R Wang, Y Wu, WL Chan, KP Tee, IROS 2016
• Human-robot partnership: A study on collaborative storytelling, CJ Wong, YL Tay, R Wang, Y Wu, HRI 2016