Sledovat
Albert S. Berahas
Albert S. Berahas
Assistant Professor, University of Michigan
E-mailová adresa ověřena na: umich.edu - Domovská stránka
Název
Citace
Citace
Rok
A multi-batch L-BFGS method for machine learning
AS Berahas, J Nocedal, M Takáč
Advances in Neural Information Processing Systems, 1063-1071, 2016
932016
An Investigation of Newton-Sketch and Subsampled Newton Methods
AS Berahas, R Bollapragada, J Nocedal
Optimization Methods and Software 35 (4), 661-680, 2020
852020
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
692018
Quasi-Newton methods for machine learning: forget the past, just sample
AS Berahas, M Jahani, P Richtárik, M Takáč
Optimization Methods and Software, 1-37, 2021
562021
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
542022
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
532019
adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs
NS Keskar, AS Berahas
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2016
372016
A robust multi-batch L-BFGS method for machine learning
AS Berahas, M Takáč
Optimization Methods and Software 35 (1), 191-219, 2020
342020
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
33*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
132021
Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1
M Jahani, M Nazari, S Rusakov, AS Berahas, M Takáč
6th International Conference on Machine Learning, Optimization, and Data …, 2020
132020
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
122014
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
102019
A stochastic sequential quadratic optimization algorithm for nonlinear equality constrained optimization with rank-deficient jacobians
AS Berahas, FE Curtis, MJ O'Neill, DP Robinson
arXiv preprint arXiv:2106.13015, 2021
82021
SONIA: A symmetric blockwise truncated optimization algorithm
M Jahani, M Nazari, R Tappenden, A Berahas, M Takác
International Conference on Artificial Intelligence and Statistics, 487-495, 2021
82021
Nested Distributed Gradient Methods with Adaptive Quantized Communication
AS Berahas, C Iakovidou, E Wei
58th IEEE Conference on Decision and Control (CDC), 1519-1525, 2019
82019
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
72021
Accelerating Stochastic Sequential Quadratic Programming for Equality Constrained Optimization using Predictive Variance Reduction
AS Berahas, J Shi, Z Yi, B Zhou
arXiv preprint arXiv:2204.04161, 2022
32022
Limited-memory BFGS with displacement aggregation
AS Berahas, FE Curtis, B Zhou
Mathematical Programming, 1-37, 2021
32021
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
22020
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Články 1–20