Shane T. Barratt

Publication Activity (10 Years)

Years Active: 2015-2023
Publications (10 Years): 27

Top Topics

Convex Optimization

Top Venues

Publications

Shane T. Barratt, Stephen P. Boyd
Fitting feature-dependent Markov chains. J. Glob. Optim. 87 (2) (2023)
Shane T. Barratt, Stephen P. Boyd
Stochastic Control With Affine Dynamics and Extended Quadratic Costs. IEEE Trans. Autom. Control. 67 (1) (2022)
Shane T. Barratt, Stephen P. Boyd
Covariance Prediction via Convex Optimization. CoRR (2021)
Akshay Agrawal, Shane T. Barratt, Stephen P. Boyd
Learning Convex Optimization Models. IEEE CAA J. Autom. Sinica 8 (8) (2021)
Shane T. Barratt, Guillermo Angeris, Stephen P. Boyd
Optimal representative sample weighting. Stat. Comput. 31 (3) (2021)
Jonathan Tuck, Shane T. Barratt, Stephen P. Boyd
A Distributed Method for Fitting Laplacian Regularized Stratified Models. J. Mach. Learn. Res. 22 (2021)
Shane T. Barratt, Yining Dong, Stephen P. Boyd
Low Rank Forecasting. CoRR (2021)
Akshay Agrawal, Shane T. Barratt, Stephen P. Boyd
Learning Convex Optimization Models. CoRR (2020)
Malayandi Palan, Shane T. Barratt, Alex McCauley, Dorsa Sadigh, Vikas Sindhwani, Stephen P. Boyd
Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint. L4DC (2020)
Akshay Agrawal, Shane T. Barratt, Stephen P. Boyd, Bartolomeo Stellato
Learning Convex Optimization Control Policies. L4DC (2020)
Shane T. Barratt, Stephen P. Boyd
Fitting a Kalman Smoother to Data. ACC (2020)
Shane T. Barratt, Guillermo Angeris, Stephen P. Boyd
Optimal Representative Sample Weighting. CoRR (2020)
Shane T. Barratt, Guillermo Angeris, Stephen P. Boyd
Minimizing a sum of clipped convex functions. Optim. Lett. 14 (8) (2020)
Akshay Agrawal, Shane T. Barratt, Stephen P. Boyd, Bartolomeo Stellato
Learning Convex Optimization Control Policies. CoRR (2019)
Shane T. Barratt, Stephen P. Boyd
Least Squares Auto-Tuning. CoRR (2019)
Jonathan Tuck, Shane T. Barratt, Stephen P. Boyd
A Distributed Method for Fitting Laplacian Regularized Stratified Models. CoRR (2019)
Akshay Agrawal, Brandon Amos, Shane T. Barratt, Stephen P. Boyd, Steven Diamond, J. Zico Kolter
Differentiable Convex Optimization Layers. CoRR (2019)
Akshay Agrawal, Brandon Amos, Shane T. Barratt, Stephen P. Boyd, Steven Diamond, J. Zico Kolter
Differentiable Convex Optimization Layers. NeurIPS (2019)
Shane T. Barratt, Mykel J. Kochenderfer, Stephen P. Boyd
Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace From Position Data. IEEE Trans. Intell. Transp. Syst. 20 (9) (2019)
Rishi Sharma, Shane T. Barratt, Stefano Ermon, Vijay S. Pande
Improved Training with Curriculum GANs. CoRR (2018)
Shane T. Barratt, Rishi Sharma
A Note on the Inception Score. CoRR (2018)
Shane T. Barratt, Mykel J. Kochenderfer, Stephen P. Boyd
Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data. CoRR (2018)
Colin de Vrieze, Shane T. Barratt, Daniel Tsai, Anant Sahai
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication. CoRR (2018)
Shane T. Barratt, Rishi Sharma
Optimizing for Generalization in Machine Learning with Cross-Validation Gradients. CoRR (2018)
Shane T. Barratt
Active Robotic Mapping through Deep Reinforcement Learning. CoRR (2017)
Shane T. Barratt
InterpNET: Neural Introspection for Interpretable Deep Learning. CoRR (2017)
Alex X. Lee, Max A. Goldstein, Shane T. Barratt, Pieter Abbeel
A non-rigid point and normal registration algorithm with applications to learning from demonstrations. ICRA (2015)