| 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 | 45 | 2019 |
| The hidden convex optimization landscape of regularized two-layer relu networks: an exact characterization of optimal solutions Y Wang, J Lacotte, M Pilanci International Conference on Learning Representations, 2021 | 40* | 2021 |
| Optimal Randomized First-Order Methods for Least-Squares Problems J Lacotte, M Pilanci Proceedings of the 37th International Conference on Machine Learning (ICML), 2020 | 28 | 2020 |
| Risk-Sensitive Generative Adversarial Imitation Learning J Lacotte, M Ghavamzadeh, Y Chow, M Pavone The 22nd International Conference on Artificial Intelligence and Statistics …, 2018 | 27 | 2018 |
| 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 | 25 | 2018 |
| 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 | 24* | 2020 |
| Faster least squares optimization J Lacotte, M Pilanci arXiv preprint arXiv:1911.02675, 2019 | 24 | 2019 |
| Adaptive and oblivious randomized subspace methods for high-dimensional optimization: Sharp analysis and lower bounds J Lacotte, M Pilanci IEEE Transactions on Information Theory 68 (5), 3281-3303, 2022 | 23* | 2022 |
| Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization J Lacotte, M Pilanci Advances in Neural Information Processing Systems 33, 2020 | 23 | 2020 |
| Newton-LESS: Sparsification without trade-offs for the sketched Newton update M Derezinski, J Lacotte, M Pilanci, MW Mahoney Advances in Neural Information Processing Systems 34, 2835-2847, 2021 | 18 | 2021 |
| Adaptive newton sketch: Linear-time optimization with quadratic convergence and effective hessian dimensionality J Lacotte, Y Wang, M Pilanci International Conference on Machine Learning, 5926-5936, 2021 | 10 | 2021 |
| Fast convex quadratic optimization solvers with adaptive sketching-based preconditioners J Lacotte, M Pilanci arXiv preprint arXiv:2104.14101, 2021 | 6 | 2021 |
| Globally optimal training of neural networks with threshold activation functions T Ergen, HI Gulluk, J Lacotte, M Pilanci arXiv preprint arXiv:2303.03382, 2023 | 5 | 2023 |
| Randomized Methods in Optimization: Fast Algorithms and Fundamental Limits JP Lacotte Stanford University, 2021 | | 2021 |
| Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update J Lacotte, M Pilanci, MW Mahoney | | |
Mert PilanciStanford UniversityVahvistettu sähköpostiosoite verkkotunnuksessa stanford.edu
