Jonathan Lacotte
Jonathan Lacotte
Verified email at stanford.edu - Homepage
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Year
A risk-sensitive finite-time reachability approach for safety of stochastic dynamic systems
MP Chapman, J Lacotte, A Tamar, D Lee, KM Smith, V Cheng, JF Fisac, ...
2019 American Control Conference (ACC), 2958-2963, 2019
192019
Risk-Sensitive Generative Adversarial Imitation Learning
J Lacotte, M Ghavamzadeh, Y Chow, M Pavone
The 22nd International Conference on Artificial Intelligence and Statistics …, 2018
152018
Faster least squares optimization
J Lacotte, M Pilanci
arXiv preprint arXiv:1911.02675, 2019
122019
Optimal Randomized First-Order Methods for Least-Squares Problems
J Lacotte, M Pilanci
Proceedings of the 37th International Conference on Machine Learning (ICML), 2020
112020
Risk-sensitive inverse reinforcement learning via semi-and non-parametric methods
S Singh, J Lacotte, A Majumdar, M Pavone
The International Journal of Robotics Research 37 (13-14), 1713-1740, 2018
82018
High-Dimensional Optimization in Adaptive Random Subspaces
J Lacotte, M Pilanci, M Pavone
Advances in Neural Information Processing Systems, 2019., 2019
72019
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform
J Lacotte, S Liu, E Dobriban, M Pilanci
Advances in Neural Information Processing Systems 33, 2020
6*2020
Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization
J Lacotte, M Pilanci
Advances in Neural Information Processing Systems 33, 2020
52020
All local minima are global for two-layer relu neural networks: The hidden convex optimization landscape
J Lacotte, M Pilanci
arXiv preprint arXiv:2006.05900, 2020
32020
Fast Convex Quadratic Optimization Solvers with Adaptive Sketching-based Preconditioners
J Lacotte, M Pilanci
arXiv preprint arXiv:2104.14101, 2021
2021
Adaptive and Oblivious Randomized Subspace Methods for High-Dimensional Optimization: Sharp Analysis and Lower Bounds
J Lacotte, M Pilanci
arXiv preprint arXiv:2012.07054, 2020
2020
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Articles 1–11