| A theoretical and empirical comparison of gradient approximations in derivative-free optimization AS Berahas, L Cao, K Choromanski, K Scheinberg Foundations of Computational Mathematics 22 (2), 507-560, 2022 | 179 | 2022 |
| A multi-batch L-BFGS method for machine learning AS Berahas, J Nocedal, M Takác Advances in Neural Information Processing Systems 29, 2016 | 169 | 2016 |
| Balancing communication and computation in distributed optimization AS Berahas, R Bollapragada, NS Keskar, E Wei IEEE Transactions on Automatic Control 64 (8), 3141-3155, 2018 | 127 | 2018 |
| An investigation of Newton-sketch and subsampled Newton methods AS Berahas, R Bollapragada, J Nocedal Optimization Methods and Software 35 (4), 661-680, 2020 | 126 | 2020 |
| Derivative-free optimization of noisy functions via quasi-Newton methods AS Berahas, RH Byrd, J Nocedal SIAM Journal on Optimization 29 (2), 965-993, 2019 | 110 | 2019 |
| Quasi-Newton methods for machine learning: forget the past, just sample AS Berahas, M Jahani, P Richtárik, M Takáč Optimization Methods and Software 37 (5), 1668-1704, 2022 | 101 | 2022 |
| Global convergence rate analysis of a generic line search algorithm with noise AS Berahas, L Cao, K Scheinberg SIAM Journal on Optimization 31 (2), 1489-1518, 2021 | 81 | 2021 |
| Sequential quadratic optimization for nonlinear equality constrained stochastic optimization AS Berahas, FE Curtis, D Robinson, B Zhou SIAM Journal on Optimization 31 (2), 1352-1379, 2021 | 64 | 2021 |
| adaQN: An adaptive quasi-Newton algorithm for training RNNs NS Keskar, AS Berahas Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016 | 47 | 2016 |
| A stochastic sequential quadratic optimization algorithm for nonlinear-equality-constrained optimization with rank-deficient Jacobians AS Berahas, FE Curtis, MJ O’Neill, DP Robinson Mathematics of Operations Research, 2023 | 33 | 2023 |
| First-and second-order high probability complexity bounds for trust-region methods with noisy oracles L Cao, AS Berahas, K Scheinberg Mathematical Programming 207 (1), 55-106, 2024 | 28 | 2024 |
| Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction AS Berahas, J Shi, Z Yi, B Zhou Computational Optimization and Applications 86 (1), 79-116, 2023 | 23 | 2023 |
| Modeling and Predicting Heavy-Duty Vehicle Engine-Out and Tailpipe Nitrogen Oxide (NO x ) Emissions Using Deep Learning R Pillai, V Triantopoulos, AS Berahas, M Brusstar, R Sun, T Nevius, ... Frontiers in Mechanical Engineering 8, 840310, 2022 | 23 | 2022 |
| Linear interpolation gives better gradients than gaussian smoothing in derivative-free optimization AS Berahas, L Cao, K Choromanski, K Scheinberg arXiv preprint arXiv:1905.13043, 2019 | 22 | 2019 |
| Scaling up quasi-newton algorithms: Communication efficient distributed sr1 M Jahani, M Nazari, S Rusakov, AS Berahas, M Takáč Machine Learning, Optimization, and Data Science: 6th International …, 2020 | 20 | 2020 |
| On the convergence of nested decentralized gradient methods with multiple consensus and gradient steps AS Berahas, R Bollapragada, E Wei IEEE Transactions on Signal Processing 69, 4192-4203, 2021 | 18 | 2021 |
| Finite difference neural networks: Fast prediction of partial differential equations Z Shi, NS Gulgec, AS Berahas, SN Pakzad, M Takáč 2020 19th IEEE International Conference on Machine Learning and Applications …, 2020 | 15 | 2020 |
| Nested distributed gradient methods with adaptive quantized communication AS Berahas, C Iakovidou, E Wei 2019 IEEE 58th Conference on Decision and Control (CDC), 1519-1525, 2019 | 15 | 2019 |
| Gradient descent in the absence of global lipschitz continuity of the gradients V Patel, AS Berahas SIAM Journal on Mathematics of Data Science 6 (3), 602-626, 2024 | 14 | 2024 |
| Sparse representation and least squares-based classification in face recognition M Iliadis, L Spinoulas, AS Berahas, H Wang, AK Katsaggelos 2014 22nd European Signal Processing Conference (EUSIPCO), 526-530, 2014 | 13 | 2014 |
Raghu BollapragadaThe University of Texas at Austin在 utexas.edu 的电子邮件经过验证
