Welcome to Jiameng's Webpage!
About Me
I am a fifth-year Ph.D. student in Department of Electrical and Computer Engineering at Boston University, advised by Prof. Wenchao Li.
My research interests lie in the intersection of Machine Learning (ML), Formal Methods (Verification and Synthesis) and Robotics.
[2022/05/15] Our paper “DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck” has been accepted to ICML'22.
[2021/06/25] New preprint of “POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems” is available. [code]
[2021/05/21] Honored to receive the second-place award at ACM SIGBED Student Research Competition 2021!
[2021/03/01] New preprint of “DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck” is available. [code]
[2020/12/02] Paper for efficiently training verifiably robust deep neural networks: “Adversarial Training and Provable Robustness: A Tale of Two objectives” is accepted to AAAI'21.
[2020/08/13] Arxiv draft of “Adversarial Training and Provable Robustness: A Tale of Two objectives” is available.
[2020/07/07] Our paper “Divide and Slide: Layer-Wise Refinement for Output Range Analysis of Deep Neural Networks” has been accepted to EMSOFT'20.
[2020/06/23] Our new tool paper "ReachNN*: A Tool for Reachability Analysis ofNeural-Network Controlled Systems" will appear at ATVA'20.
[2019/08/9] Our new paper "Towards Verification-Aware Knowledge Distillation for Neural-Network Controlled Systems" will appear at ICCAD'19.
[2019/07/10] Our paper “ReachNN: Reachability Analysis of Neural-Network Controlled Systems” has been accepted to appear at EMSOFT’19.
[2019/03/20] Our Paper "Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation" has been accepted at ICLR’19 Workshop on SafeML.
More...
Updates
Selected Papers
Jiameng Fan and Wenchao Li
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck
International Conference on Machine Learning (ICML), July 2022
(to appear)[code]
A robust representation learning approach for deep reinforcement learning to extract only task-relevant from raw pixels using the multi-view information bottleneck principle.
Jiameng Fan and Wenchao Li
Adversarial Training and Provable Robustness: A Tale of Two Objectives
AAAI Conference on Artificial Intelligence (AAAI), February 2021
A joint training scheme to efficiently train verifiably robust deep neural networks that achieves state-of-art verified robustness in many settings.
Jiameng Fan, Chao Huang, Wenchao Li, Xin Chen and Qi Zhu
Towards Verification-Aware Knowledge Distillation for Neural-Network Controlled Systems
International Conference on Computer-Aided Design (ICCAD), November 2019
A novel verification-aware knowledge distillation framework that transfers the knowledge of a trained network to a new and easier-to-verify network.
Chao Huang, Jiameng Fan, Wenchao Li, Xin Chen and Qi Zhu
ReachNN: Reachability Analysis of Neural-Network Controlled Systems
International Conference on Embedded Software (EMSOFT), October 2019
A new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions.
Jiameng Fan and Wenchao Li
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation
International Conference on Learning Representation (ICLR), Workshop on Safe Machine Learning: Specification, Robustness, and Assurance, May 2019
[pdf]
Formulate the state-action value function of a notion of trajectory-based safety as a candidate Lyapunov function and extend control-theoretic results to approximate its derivative using online Gaussian Process estimation.