I am interested in developing algorithms that endow robots with human-like problem-solving abilities. Most of my work falls under the umbrellas of exploration, imitation learning, and meta learning.

Short Bio

I am currently a postdoctoral researcher at the Vector Institute, an AI research institute with strong ties to the University of Toronto. In 2018, I completed my PhD at UC Berkeley. My advisor was Pieter Abbeel. In 2016 and 2017, I was a research scientist at Open AI, where I was advised by Ilya Sutskever. I received a BA in mathematics from the University of Chicago, where I spent four wonderful years. During this time, I had the honor of working under Paul Sally.

My Google Scholar page can be found here.

My CV is here.

           

Publication Feed

2020

Sampling Aware Reinforcement Learning
Lunjun Zhang, Bradly C. Stadie, Jimmy Ba
Conference on Uncertainty in Artificial Intelligence (UAI), July 2020. Paper here

One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation
Matthew Zhang, Bradly C. Stadie
In International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020. ArXiv link

2019

Transfer Learning for Estimating Causal Effects Using Neural Networks
Bradly C. Stadie, Soeren R. Kuenzel, Nikita Vemuri, Varsha Ramakrishnan, Jasjeet S. Sekhon, Pieter Abbeel
INFORMS Annual Meeting ML and causal inference workshop (2019). ArXiv link

2018

Evolved Policy Gradients
Rein Houthooft, Richard Y. Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, Pieter Abbeel
In Neural Information Processing Systems (NeurIPS) [Spotlight], Montreal, Canada, December 2018. ArXiv

Some Considerations on Learning to Explore via Meta-Reinforcement Learning
Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
In Neural Information Processing Systems (NeurIPS), Montreal, Canada, December 2018. ArXiv

Learning to Learn from Flawed, Failed, and Figurative Demonstrations
Ge Yang, Bradly C. Stadie, Roberto Calandra, Pieter Abbeel, Sergey Levine, Chelsea Finn
In Neural Information Processing Systems (NeurIPS) Deep RL workshop [Spotlight], Montreal, Canada, December 2018. Paper here.

2017

Third-Person Imitation Learning
Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever
In the proceedings of the International Conference on Learning Representations (ICLR), Toulon, France, April 2017. ArXiv

One-Shot Imitation Learning
Yan (Rocky) Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba
In Neural Information Processing Systems (NeurIPS), Long Beach, California, December 2017. ArXiv

2015

Incentivizing Exploration in Reinforcement Learning with Deep Predictive Models
Bradly C. Stadie, Sergey Levine, Pieter Abbeel
In Neural Information Processing Systems (NeurIPS) Deep RL Workshop, Montreal, Canada, December 2015 ArXiv