About Me
I am currently a final-year PhD candidate in Computer Science at Princeton University, advised by Jason D. Lee. My research was previously supported by the NSF Graduate Research Fellowship.
I have previously studied at the University of California, Berkeley, where I received my undergraduate degree in Electrical Engineering and Computer Sciences. There, I was an undergraduate researcher in Prof. Sergey Levine's lab, working on model-based reinforcement learning together with Roberto Calandra and Rowan McAllister. I have also been involved with Facebook AI Research as a research intern.
I am broadly interested in exploring theoretical machine learning. Recently, I have been focusing on meta-learning and related areas, aiming to formalize structures shared among tasks that allow for sample-efficient learning.
You can reach me at kchua (at) princeton (dot) edu.
Publications and Preprints
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Provable Hierarchy-Based Meta-Reinforcement Learning
Kurtland Chua, Qi Lei, Jason D. Lee.
AISTATS 2023. -
How Fine-Tuning Allows for Effective Meta-Learning.
Kurtland Chua, Qi Lei, Jason D. Lee.
NeurIPS 2021. -
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning.
Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra.
AISTATS 2021. -
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models.
website | code
Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine.
NeurIPS 2018 (Spotlight presentation, ~4% of submitted papers).
Talks
- “Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models.” Bay Area Machine Learning Symposium (Baylearn). October 2018.
video
Teaching
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COS 435: Introduction to Reinforcement Learning, Princeton
Assistant in Instruction
Spring 2024 -
COS 240: Reasoning about Computation, Princeton
Assistant in Instruction
Fall 2023 -
EECS 126: Probability and Random Processes, UC Berkeley
Undergraduate Student Instructor (uGSI)
Spring 2019 | Fall 2018
Honors and Awards
- National Science Foundation Graduate Research Fellowship (2019).
- Gordon Y.S. Wu Fellowship in Engineering (2019).
- EECS Major Citation (2019).
- NVIDIA Pioneer Award (2018). Awarded for Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models at NeurIPS 2018.
- Phi Beta Kappa Honors Society (2018). Inducted as a junior.