Shane T. Barratt

About me

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Ph.D. candidate,
Electrical Engineering, Stanford University
E-mail: stbarratt [@] gmail [dot] com
Resume/CV

I am currently working towards the Ph.D. degree in electrical engineering at Stanford University, advised by Professor Stephen Boyd. My research focuses on convex optimization, and in particular its applications to machine learning and control. I received 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 Blackrock, Lyft Level 5 (see the blog post), Google (in particular, Skybox Imaging/Terra Bella), Qualcomm-Atheros, and SoRoCo. Until April, I am consulting for Blackrock one day a week. I am starting to get interested in Ethereum and DeFi; more to come.

Papers

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

2021

Fitting feature-dependent Markov chains (code)

S. Barratt and S. Boyd. Manuscript.

Covariance prediction via convex optimization (code)

S. Barratt and S. Boyd. Manuscript.

Stochastic control with affine dynamics and extended quadratic costs (code)

S. Barratt and S. Boyd. To appear, IEEE Transactions on Automatic Control, February 2022.

Optimal representative sample weighting (code)

S. Barratt, G. Angeris, and S. Boyd. To appear, Statistics & Computing.

Covariance prediction via convex optimization (code)

S. Barratt and S. Boyd. Manuscript.

Portfolio construction using stratified models

J. Tuck, S. Barratt, and S. Boyd. Manuscript.

2020

A distributed method for fitting Laplacian regularized stratified models (code, talk)

J. Tuck, S. Barratt, and S. Boyd. Journal of Machine Learning Research.

Low rank forecasting (code)

S. Barratt, Y. Dong, and S. Boyd. Manuscript.

Learning convex optimization models (code)

A. Agrawal, S. Barratt, and S. Boyd*. Manuscript.

Multi-period liability clearing via convex optimal control (code)

S. Barratt and S. Boyd. Manuscript.

Learning convex optimization control policies (PMLR version, code, talk)

A. Agrawal, S. Barratt, S. Boyd, and B. Stellato*. 2020 Conference on Learning for Dynamics and Control (L4DC), Oral.

Fitting a linear control policy to demonstrations with a Kalman constraint (PMLR version, code)

M. Palan, S. Barratt*, A. McCauley, D. Sadigh, V. Sindhwani, and S. Boyd. 2020 Conference on Learning for Dynamics and Control (L4DC), Poster.

Convex optimization over risk-neutral probabilities (code)

S. Barratt, J. Tuck, and S. Boyd. Manuscript.

Minimizing a sum of clipped convex functions (code)

S. Barratt, G. Angeris, and S. Boyd. To appear, Optimization Letters.

Least squares auto-tuning (code)

S. Barratt and S. Boyd. To appear, Engineering Optimization.

Automatic repair of convex optimization problems (code)

S. Barratt, G. Angeris, and S. Boyd. Optimization & Engineering.

The Georeg regularizer

G. Angeris, S. Barratt, and J. Tuck*. Manuscript.

Fitting a Kalman smoother to data (code, talk)

S. Barratt and S. Boyd. 2020 American Control Conference.

2019

Differentiable convex optimization layers (code)

A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, Z. Kolter*. 2019 Neural Information Processing Systems (NeurIPS).

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

Direct model predictive control

S. Barratt. ICML 2018 Workshop on Planning and Learning.

A note on the inception score

S. Barratt, R. Sharma*. ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models.

Improved training with curriculum GANs

R. Sharma, S. Barratt, S. Ermon, and V. Pande. Manuscript.

2017

Cooperative multi-agent reinforcement learning for low-level wireless communication

C. de Vrieze, S. Barratt, D. Tsai, and A. Sahai. Manuscript.

InterpNET: neural introspection for interpretable deep learning

S. Barratt. Interpretable ML Symposium, 2017 Neural Information Processing Systems (NeurIPS).

2015

A non-rigid point and normal registration algorithm with applications to learning from demonstrations (videos)

A. Lee, M. Goldstein, S. Barratt, and P. Abbeel. 2015 International Conference on Robotics and Automation (ICRA).