Bo Liu - UT Austin

I am a Ph.D. student at UT Austin and I am very forunate to be co-advised by Prof. Peter Stone and Prof. Qiang Liu. My research interest lies in multitask/continual learning, reinforcement learning, imitation learning and their applications in robotics. Specifically, I am interested in designing theoretically sound algorithms and neural architectures for training large-scale multi-purpose agents. Before joining UT Austin, I completed my master's at Stanford University and my undergraduate studies at Johns Hopkins University.

Contact: Email / Github / Google Scholar / Twitter

Experience

Jun - Oct 2023

Google DeepMind, London – Research Intern
Mentor: Arthur Szlam, Marc'aurelio Ranzato

Jun - Sept 2020

Nvidia, Sunnyvale – Research Intern
Mentor: Animashree Anandkumar, Yuke Zhu

Jun - Sept 2018/2019

Baidu, Sunnyvale – Research Intern
Mentor: Ping Li

May - Aug 2015/2016

Google, Los Angeles – Software Engineer Intern

Selected Publications (* ⇒ equal contribution)

Asynchronous Local-SGD Training for Language Modeling

Bo Liu, Rachita Chhaparia, Arthur Douillard, Satyen Kale, Andrei A. Rusu, Jiajun Shen, Arthur Szlam, Marc'Aurelio Ranzato

paper / code

Lion Secretly Solves Constrained Optimization: As Lyapunov Predicts

*Lizhang Chen, *Bo Liu, *Kaizhao Liang, Qiang Liu

ICLR 2024 (Spotlight)

paper

LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

*Bo Liu, *Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, Peter Stone

paper / code

LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

*Bo Liu, *Yifeng Zhu, *Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, Peter Stone

NeurIPS 2023 (oral presentation at RAP4Robots, ICRA 2023, and TGR, CoRL 2023)

paper / code / website

FAMO: Fast Adaptive Multitask Optimization

Bo Liu, Yihao Feng, Peter Stone, Qiang Liu

NeurIPS 2023

paper / code

Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning

Bo Liu, Yihao Feng, Qiang Liu, Peter Stone

AAAI 2023 (oral presentation)

paper / code

BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach

*Bo Liu, *Mao Ye, Peter Stone, Steve Wright, Qiang Liu

NeurIPS 2022

paper / code

Continual Learning and Private Unlearning

Bo Liu, Qiang Liu, Peter Stone

CoLLAs 2022

paper / code

Conflict-Averse Gradient Descent for Multi-task Learning

Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu

NeurIPS 2021

paper / code

Human versus Machine Attention in Deep Reinforcement Learning Tasks

Sihang Guo, Ruohan Zhang, Bo Liu, Yifeng Zhu, Mary Hayhoe, Dana Ballard, Peter Stone

NeurIPS 2021

paper

Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition

Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Animashree Anandkumar

ICML 2021 (oral presentation)

paper / code

A lifelong learning approach to mobile robot navigation

*Bo Liu, *Xuesu Xiao, Peter Stone

RA-L / ICRA 2021

paper / code

Firefly neural architecture descent: a general approach for growing neural networks

*Lemeng Wu, *Bo Liu, Peter Stone, Qiang Liu

NeurIPS 2020

paper / code

APPLD: Adaptive Planner Parameter Learning from Demonstration

*Xuesu Xiao, *Bo Liu, Garrett Warnell, Jonathan Fink, Peter Stone

RA-L / IROS 2020

paper

DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs

*Yunbo Wang, *Bo Liu, Jiajun Wu, Yuke Zhu, Simon S. Du, Li Fei-Fei, Joshua B. Tenenbaum

IJCAI 2020

paper / code

Predicting pregnancy using large-scale data from a women’s health tracking mobile application

*Bo Liu, *Shuyang Shi, *Yongshang Wu, Daniel Thomas, Laura Symul, Emma Pierson, Jure Leskovec

WWW 2019

paper

Teaching

Fall 2019	University of Texas at Austin – Teaching Assistant CS 394: Reinforcement Learning
Winter 2019	Stanford University – Teaching Assistant CS 234: Reinforcement Learning
Fall 2016	Johns Hopkins University – Teaching Assistant EN 601.665: Natural Language Processing
Fall 2015	Johns Hopkins University – Teaching Assistant EN 600.463: Algorithms