Guan-Horng Liu

Machine Learning PhD @ Georgia Tech ūüöÄ

Hi, I am Guan-Horng Liu (I go by "Guan" ), a third-year Machine Learning PhD in Georgia Tech advised by Evangelos A. Theodorou.

My research aims to develop a new paradigm of Deep Learning Optimization grounded on Optimal Control Theory. This mathematical framework enables rich analysis from stochastic process, game theory, optimal transport, and information duality. It also facilitates principled algorithmic design, better characterization of the training process, and architecture optimization.

I finished my M.S. in Robotics at Carnegie Mellon University, working with George Kantor and Manuela M. Veloso on off-road autonomous navigation and deep reinforcement learning. I also owned a B.S. in MechE at National Taiwan University, with an one-year research exchange at Tokyo Institute of Technology.

I worked in Uber Advanced Technology Group as a Robotics Autonomy Engineer for 1.5 years before joining Georgia Tech, developing motion planning algorithm for self-driving vehicles under the team led by Tony Stentz.

See my full CV here (updated in Feb 2022).

Contact: ghliu [at] gatech [dot] edu
Follow: Google Scholar | LinkedIn | ghliu | @guanhorng_liu

Updates

Feb, 2022 :fire: Check out our SB-FBSDE, (accepted to ICLR 2022) on generalizing score-based models with Schrödinger Bridge. Our code is released here!
Jan, 2022 :books: I am very fortunate to receive GaTech AE Graduate Fellowship. Thanks GaTech AE!
Dec, 2021 :mega: I give a talk on Higher-order Optimization of Neural ODEs at DataSig.
Nov, 2021 :man_technologist: The code for our NeurIPS spotlight is released here!
Oct, 2021 :mega: I give a talk on Optimal Control Theoretic Neural Optimizer at GaTech ML PhD Seminar.
Sep, 2021 :fire: I have one paper, SNOpt accepted to NeurIPS 2021 as a Spotlight (acceptance rate 3.0%).
May, 2021 :fire: I have one paper, DGNOpt, accepted to ICML 2021 as an Oral (acceptance rate 3.0%).
Jan, 2021 :fire: I have one paper, DDPNOpt, accepted to ICLR 2021 as a Spotlight (acceptance rate 3.8%).

Publications

2022

  1. ICLR 2022
    Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory
    Tianrong Chen*, Guan-Horng Liu*, and Evangelos A Theodorou
    International Conference on Learning Representations (* Equal contribution), 2022

2021

  1. NeurIPS 2021
    Second-order Neural ODE Optimizer
    Guan-Horng Liu, Tianrong Chen, and Evangelos A Theodorou
    Advances in Neural Information Processing Systems, 2021
    Spotlight presentation (acceptance rate 3.0%)
  2. ICML 2021
    Dynamic Game Theoretic Neural Optimizer
    Guan-Horng Liu, Tianrong Chen, and Evangelos A Theodorou
    International Conference on Machine Learning, 2021
    Oral presentation (acceptance rate 3.0%)
  3. ICLR 2021
    DDPNOpt: Differential Dynamic Programming Neural Optimizer
    Guan-Horng Liu, Tianrong Chen, and Evangelos A Theodorou
    International Conference on Learning Representations, 2021
    Spotlight presentation (acceptance rate 3.8%)
  4. arxiv
    Spatio-Temporal Differential Dynamic Programming for Control of Fields
    Ethan N Evans, Oswin So, Andrew P Kendall, Guan-Horng Liu, and Evangelos A Theodorou
    arXiv preprint (in submission), 2021
  5. RSS 2021
    Variational Inference MPC using Tsallis Divergence
    Ziyi Wang, Oswin So, Jason Gibson, Bogdan Vlahov, Manan S Gandhi, Guan-Horng Liu, and Evangelos A Theodorou
    Robotics: Science and Systems, 2021

2020

  1. arxiv
    A Differential Game Theoretic Neural Optimizer for Training Residual Networks
    Guan-Horng Liu, Tianrong Chen, and Evangelos A Theodorou
    arXiv preprint, 2020

2019

  1. arxiv
    Deep learning theory review: An optimal control and dynamical systems perspective
    Guan-Horng Liu, and Evangelos A Theodorou
    arXiv preprint (in submission), 2019

2017

  1. CoRL 2017
    Learning end-to-end multimodal sensor policies for autonomous navigation
    Guan-Horng Liu, Avinash Siravuru, Sai Prabhakar, Manuela Veloso, and George Kantor
    Conference on Robot Learning, 2017
  2. CMU Thesis
    High Dimensional Planning and Learning for Off-Road Driving
    Guan-Horng Liu
    CMU Robotics Institute Master Thesis, 2017
  3. RLDM 2017
    Multi-modal Deep Reinforcement Learning with a Novel Sensor-based Dropout
    Guan-Horng Liu, Avinash Siravuru, Sai Prabhakar, George Kantor, and Manuela Veloso
    Multi-disciplinary Conference on Reinforcement Learning and Decision Making, 2017

2014

  1. JBE 2014
    A bio-inspired hopping kangaroo robot with an active tail
    Guan-Horng Liu, Hou-Yi Lin, Huai-Yu Lin, Shao-Tuan Chen, and Pei-Chun Lin
    Journal of Bionic Engineering, 2014
  2. RSJ 2014
    Autonomous Control of the WAM-V Catamaran Type Unmanned Surface Vehicle: Propulsion System Design
    Guan-Horng Liu, Andre Yuji YASUTOMI, Alexis HOLGADO, and Edwardo F FUKUSHIMA
    Annual Conference of the Robotics Society of Japan, 2014

2013

  1. SII 2013
    Design of a kangaroo robot with dynamic jogging locomotion
    Guan-Horng Liu, Hou-Yi Lin, Huai-Yu Lin, Shao-Tuan Chen, and Pei-Chun Lin
    Proceedings of the 2013 IEEE/SICE International Symposium on System Integration, 2013
    Best Paper Award