Albert S. Berahas
Albert S. Berahas
Postdoctoral Research Fellow, Lehigh University
Verified email at u.northwestern.edu - Homepage
TitleCited byYear
A multi-batch L-BFGS method for machine learning
AS Berahas, J Nocedal, M Takác
Advances in Neural Information Processing Systems, 1055-1063, 2016
482016
An investigation of Newton-sketch and subsampled Newton methods
AS Berahas, R Bollapragada, J Nocedal
Optimization Methods and Software, 1-20, 2020
432020
adaqn: An adaptive quasi-newton algorithm for training rnns
NS Keskar, AS Berahas
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2016
242016
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
222018
A robust multi-batch l-bfgs method for machine learning
AS Berahas, M Takáč
Optimization Methods and Software 35 (1), 191-219, 2020
92020
Quasi-newton methods for deep learning: Forget the past, just sample
AS Berahas, M Jahani, M Takáč
arXiv preprint arXiv:1901.09997, 2019
92019
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
92019
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
72014
A theoretical and empirical comparison of gradient approximations in derivative-free optimization
AS Berahas, L Cao, K Choromanski, K Scheinberg
arXiv preprint arXiv:1905.01332, 2019
42019
Scaling up quasi-newton algorithms: Communication efficient distributed sr1
M Jahani, M Nazari, S Rusakov, AS Berahas, M Takáč
arXiv preprint arXiv:1905.13096, 2019
22019
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
22019
Multi-model robust error correction for face recognition
M Iliadis, L Spinoulas, AS Berahas, H Wang, AK Katsaggelos
2016 IEEE International Conference on Image Processing (ICIP), 3229-3233, 2016
22016
Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise
AS Berahas, L Cao, K Scheinberg
arXiv preprint arXiv:1910.04055, 2019
2019
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
2019
Limited-Memory BFGS with Displacement Aggregation
AS Berahas, FE Curtis, B Zhou
arXiv preprint arXiv:1903.03471, 2019
2019
Sampled Quasi-Newton Methods for Deep Learning
AS Berahas, M Jahani, M Takác
OPT 2019: Optimization for Machine Learning Workshop (NeurIPS 2019), 2019
2019
Methods for Large Scale Nonlinear and Stochastic Optimization
AS Berahas
Northwestern University, 2018
2018
adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs
N Shirish Keskar, AS Berahas
arXiv preprint arXiv:1511.01169, 2015
2015
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Articles 1–18