Shane T. Barratt
 |
Shane T. Barratt |
About me
My research focuses on convex optimization, and in particular its applications to machine learning and control. I received the Ph.D. degree in electrical engineering from Stanford University (advised by Professor Stephen Boyd) in 2021, the M.S. degree in electrical engineering from Stanford University in 2019, and the B.S. degree in electrical engineering and computer science from the University of California, Berkeley in 2017. Previously, I have interned at the Blackrock AI Labs, Lyft Level 5 (see the blog post), Google (in particular, Skybox Imaging/Terra Bella), Qualcomm-Atheros, and SoRoCo.
Papers
For papers listed by citation count, see Google Scholar.
2022
- Stochastic control with affine dynamics and extended quadratic costs (code)
S. Barratt and S. Boyd. IEEE Transactions on Automatic Control.
2021
- Learning convex optimization models (code)
A. Agrawal, S. Barratt, and S. Boyd. IEEE/CAA Journal of Automatica Sinica.
- Fitting feature-dependent Markov chains (code)
S. Barratt and S. Boyd. Manuscript.
- A distributed method for fitting Laplacian regularized stratified models (code, talk)
J. Tuck, S. Barratt, and S. Boyd. Journal of Machine Learning Research.
- Optimal representative sample weighting (code)
S. Barratt, G. Angeris, and S. Boyd. Statistics and Computing.
- Automatic repair of convex optimization problems (code)
S. Barratt, G. Angeris, and S. Boyd. Optimization and Engineering.
- Covariance prediction via convex optimization (code)
S. Barratt and S. Boyd. Manuscript.
- Portfolio construction using stratified models (code)
J. Tuck, S. Barratt, and S. Boyd. Machine Learning in Financial Markets: A Guide to Contemporary Practice.
- Convex optimization and implicit differentiation methods for control and estimation
S. Barratt. PhD Thesis.
- Least squares auto-tuning (code)
S. Barratt and S. Boyd. Engineering Optimization.
2020
- Minimizing a sum of clipped convex functions (code)
S. Barratt, G. Angeris, and S. Boyd. Optimization Letters.
- Convex optimization over risk-neutral probabilities (code)
S. Barratt, J. Tuck, and S. Boyd. Manuscript.
- Fitting a linear control policy to demonstrations with a Kalman constraint (code)
M. Palan, S. Barratt, A. McCauley, D. Sadigh, V. Sindhwani, and S. Boyd. Proceedings of Machine Learning Research.
- Multi-period liability clearing via convex optimal control (code)
S. Barratt and S. Boyd. Manuscript.
- Low rank forecasting (code)
S. Barratt, Y. Dong, and S. Boyd. Manuscript.
- Embedded convex optimization for control (video, code)
S. Boyd, A. Agrawal, and S. Barratt. Plenary lecture, IEEE Conference on Decision and Control.
- Learning convex optimization control policies (code)
A. Agrawal, S. Barratt, S. Boyd, and B. Stellato. Proceedings of Machine Learning Research.
2019
- Fitting a Kalman smoother to data (code)
S. Barratt and S. Boyd. Proceedings of the American Control Conference.
- Differentiable convex optimization layers (code, poster)
A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and J. Zico Kolter. Advances in Neural Information Processing Systems.
- Learning probabilistic trajectory models of aircraft in terminal airspace from position data (code)
S. Barratt, M. Kochenderfer, and S. Boyd. IEEE Transactions on Intelligent Transportation Systems.
- Differentiating through a cone program (code)
A. Agrawal, S. Barratt, S. Boyd, E. Busseti, and W. Moursi. Journal of Applied and Numerical Optimization.
2018
- Improved training with curriculum GANs
R. Sharma, S. Barratt, S. Ermon, and V. Pande. Manuscript.
- Systems and methods for discovering automatable tasks
Y. Kim, A. Qadir, A. Narayanaswamy, R. Murty, S. Barratt, and G. Nychis. US Patent.
- Optimizing for generalization in machine learning with cross-validation gradients
S. Barratt and R. Sharma. Manuscript.
- On the differentiability of the solution to convex optimization problems
S. Barratt. Manuscript.
- A note on the inception score
S. Barratt and R. Sharma. ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models.
- Cooperative multi-agent reinforcement learning for low-level wireless communication
C. de Vrieze, S. Barratt, D. Tsai, and A. Sahai. Manuscript.
- Direct model predictive control
S. Barratt. ICML Workshop on Planning and Learning.
2017
- InterpNET: neural introspection for interpretable deep learning
S. Barratt. Neurips Interpretable ML Symposium.
- Active robotic mapping through deep reinforcement learning
S. Barratt. Manuscript.
2015
- A non-rigid point and normal registration algorithm with applications to learning from demonstrations
A. Lee, M. Goldstein, S. Barratt, and P. Abbeel. International Conference on Robotics and Automation.