| Accelerated Information Gradient flow Y Wang, W Li arXiv preprint arXiv:1909.02102, 2019 | 62 | 2019 |
| Information Newton's flow: second-order optimization method in probability space Y Wang, W Li arXiv preprint arXiv:2001.04341, 2020 | 36 | 2020 |
| Projected wasserstein gradient descent for high-dimensional bayesian inference Y Wang, P Chen, W Li SIAM/ASA Journal on Uncertainty Quantification 10 (4), 1513-1532, 2022 | 25 | 2022 |
| 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 | 22 | 2021 |
| Parallel deep neural networks have zero duality gap Y Wang, T Ergen, M Pilanci arXiv preprint arXiv:2110.06482, 2021 | 17 | 2021 |
| A decomposition augmented lagrangian method for low-rank semidefinite programming Y Wang, K Deng, H Liu, Z Wen SIAM Journal on Optimization 33 (3), 1361-1390, 2023 | 16 | 2023 |
| The convex geometry of backpropagation: Neural network gradient flows converge to extreme points of the dual convex program Y Wang, M Pilanci arXiv preprint arXiv:2110.06488, 2021 | 14 | 2021 |
| A stochastic stein variational newton method A Leviyev, J Chen, Y Wang, O Ghattas, A Zimmerman arXiv preprint arXiv:2204.09039, 2022 | 11 | 2022 |
| Optimal neural network approximation of wasserstein gradient direction via convex optimization Y Wang, P Chen, M Pilanci, W Li SIAM Journal on Mathematics of Data Science 6 (4), 978-999, 2024 | 10 | 2024 |
| The hidden convex optimization landscape of two-layer relu neural networks: an exact characterization of the optimal solutions Y Wang, J Lacotte, M Pilanci arXiv preprint arXiv:2006.05900, 2020 | 8 | 2020 |
| Beyond the best: Estimating distribution functionals in infinite-armed bandits Y Wang, TZ Baharav, Y Han, J Jiao, D Tse arXiv preprint arXiv:2211.01743, 2022 | 4 | 2022 |
| A library of mirrors: Deep neural nets in low dimensions are convex lasso models with reflection features E Zeger, Y Wang, A Mishkin, T Ergen, E Candès, M Pilanci arXiv preprint arXiv:2403.01046, 2024 | 3 | 2024 |
| Polynomial-time solutions for relu network training: A complexity classification via max-cut and zonotopes Y Wang, M Pilanci arXiv preprint arXiv:2311.10972, 2023 | 3 | 2023 |
| Overparameterized relu neural networks learn the simplest models: Neural isometry and exact recovery Y Wang, Y Hua, E Candés, M Pilanci arXiv preprint arXiv:2209.15265, 2022 | 3 | 2022 |
| Search direction correction with normalized gradient makes first-order methods faster Y Wang, Z Jia, Z Wen SIAM Journal on Scientific Computing 43 (5), A3184-A3211, 2021 | 3 | 2021 |
| The search direction correction makes first-order methods faster Y Wang, Z Jia, Z Wen arXiv preprint arXiv:1905.06507, 2019 | 3 | 2019 |
| Randomized geometric algebra methods for convex neural networks Y Wang, S Kim, P Chu, I Subramaniam, M Pilanci arXiv preprint arXiv:2406.02806, 2024 | 2 | 2024 |
| Overparameterized ReLU Neural Networks Learn the Simplest Model: Neural Isometry and Phase Transitions Y Wang, Y Hua, EJ Candes, M Pilanci IEEE Transactions on Information Theory, 2025 | 1 | 2025 |
| A circuit approach to constructing blockchains on blockchains EN Tas, D Tse, Y Wang arXiv preprint arXiv:2402.00220, 2024 | 1 | 2024 |
| Sketching the Krylov subspace: faster computation of the entire ridge regularization path Y Wang, M Pilanci The Journal of Supercomputing 79 (16), 18748-18776, 2023 | 1 | 2023 |
Mert PilanciStanford UniversityPatvirtintas el. paštas stanford.edu