Papers

For papers listed by citation count, see Google Scholar. * denotes equal contribution.

2022

Stochastic control with affine dynamics and extended quadratic costs (code): S. Barratt and S. Boyd. IEEE Transactions on Automatic Control.

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.

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.

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.

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.

InterpNET: neural introspection for interpretable deep learning: S. Barratt. Neurips Interpretable ML Symposium.
Active robotic mapping through deep reinforcement learning: S. Barratt. Manuscript.

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.