(This list includes preprints.)
2022
- Rinav Pillai, Vassilis Triantopoulos, Albert S. Berahas, Matthew Brusstar, Ruonan Sun, Tim Nevius and André L Boehman. Modeling and Predicting Heavy-Duty Vehicle Engine-Out and Tailpipe Nitrogen Oxide (NOx) Emissions Using Deep Learning. Frontiers in Mechanical Engineering, 2022, DOI: 10.3389/fmech.2022.840310. (Download PDF, Frontiers Online)
2021
- Albert S. Berahas, Frank E. Curtis, Daniel P. Robinson and Baoyu Zhou. Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization. SIAM Journal on Optimization, 2021, 31(2), pp. 1352-1379. (Download PDF, SIOPT Online)
- Albert S. Berahas, Liyuan Cao and Katya Scheinberg. Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise. SIAM Journal on Optimization, 2021, 31(2), pp. 1489-1518. (Download PDF, SIOPT Online)
- Albert S. Berahas, Liyuan Cao, Krzysztof Choromanski and Katya Scheinberg. A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization. Foundations of Computational Mathematics, 2021, DOI: 10.1007/s10208-021-09513-z. (Download PDF, FoCM Online)
- Albert S. Berahas, Frank E. Curtis and Baoyu Zhou. Limited-Memory BFGS with Displacement Aggregation. Mathematical Programming, 2021, DOI: 10.1007/s10107-021-01621-6. (Download PDF, MAPR Online)
- Albert S. Berahas, Raghu Bollapragada and Ermin Wei. On the Convergence of Nested Distributed Gradient Methods with Multiple Consensus and Gradient Steps. IEEE Transactions on Signal Processing, 2021, DOI: 10.1109/TSP.2021.3094906. (Download PDF, IEEE Xplore)
- Majid Jahani, Mohammadreza Nazari, Rachael Tappenden, Albert S. Berahas and Martin Takáč. SONIA: A Symmetric Blockwise Truncated Optimization Algorithm. 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021, pp. 487-495. (Download PDF, Supplementary Material)
- Sudeep Metha, Ved Patel and Albert S. Berahas. Auction-Based Preferential Shift Scheduling: A Case Study on the Lehigh University Libraries. Institute of Industrial and Systems Engineers (IISE) Conference and Expo, 2021. (Download PDF)
- (preprint) Albert S. Berahas, Frank E. Curtis, Michael J. ONeill and Daniel P. Robinson. A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear Equality Constrained Optimization with Rank- Deficient Jacobians. arXiv preprint arXiv:2106.13015, 2021. (Download PDF)
- (preprint) Albert S. Berahas, Oumaima Sohab, and Luis Nunes Vicente. Full-low evaluation methods for derivative-free optimization. arXiv preprint arXiv:2107.11908, 2021. (Download PDF)
2020
- Albert S. Berahas, Raghu Bollapragada and Jorge Nocedal. An Investigation of Newton-Sketch and Subsampled Newton Methods. Optimization Methods and Software, 2020, 35(4), pp. 661–680. (Download PDF, OMS Online, Supplementary Material)
- Albert S. Berahas and Martin Takáč, A Robust Multi-Batch L-BFGS Method for Machine Learning. Optimization Methods and Software, 2020, 35(1), pp. 191-219. (Download PDF, OMS Online, Supplementary Material)
- Majid Jahani, Mohammadreza Nazari, Sergey Rusakov, Albert S. Berahas and Martin Takáč. Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1. 6th Annual Conference on Machine Learning, Optimization and Data Science (LOD), 2020. (Download PDF)
- Zheng Shi, Nur Sila Gulgec, Albert S. Berahas, Shamim N. Pakzad, Martin Takáč. Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations. 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020, pp. 130-135. (Download PDF)
2019
- Albert S. Berahas, Richard Byrd and Jorge Nocedal. Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods. SIAM Journal on Optimization, 2019, 29(2), pp. 965-993. (Download PDF, SIOPT Online)
- Albert S. Berahas, Raghu Bollapragada, Nitish Shirish Keskar and Ermin Wei. Balancing Communication and Computation in Distributed Optimization. IEEE Transactions on Automatic Control, 2019, 64(8), pp. 3141-3155. (Download PDF, IEEE Xplore)
- Albert S. Berahas, Charikleia Iakovidou and Ermin Wei. Nested Distributed Gradient Methods with Adaptive Quantized Communication. 58th IEEE Conference on Decision and Control (CDC), Nice, France, 2019, pp. 1519-1525. (Download PDF, IEEE Xplore)
- Albert S. Berahas, Liyuan Cao, Krzysztof Choromanski and Katya Scheinberg. Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization. Technical Report, Lehigh University 2019. (Download PDF)
- Albert S. Berahas, Majid Jahani and Martin Takáč. Sampled Quasi-Newton Methods for Deep Learning. OPT 2019: Optimization for Machine Learning Workshop (NeurIPS 2019) (Download PDF)
- (Status: Accepted, Optimization Methods and Software) Albert S. Berahas, Majid Jahani, Peter Richtárik and Martin Takáč. Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample. (Download PDF)
2016
- Albert S. Berahas, Jorge Nocedal and Martin Takáč, A Multi-Batch L-BFGS Method for Machine Learning. 2016 Advances In Neural Information Processing Systems (NeurIPS), Barcelona, Spain, Dec 2016, pp. 1055-1063. (Download PDF, Supplementary Material)
- Nitish Shirish Keskar and Albert S. Berahas, adaQN: An adaptive quasi-Newton algorithm for training RNNs. 2016 European Conference Machine Learning and Knowledge Discovery in Databases (ECML PKDD), Riva del Garda, Italy, Sept 2016, pp. 1-16. (Download PDF)
- Michael Iliadis, Leonidas Spinoulas, Albert S. Berahas, Haohong Wang, and Aggelos K. Katsaggelos, Multi-model robust error correction for face recognition. 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, Arizona, Sept 2016, pp. 3229-3233. (IEEE Xplore)
2014
- Michael Iliadis, Leonidas Spinoulas, Albert S. Berahas, Haohong Wang, and Aggelos K. Katsaggelos, Sparse representation and least squares-based classification in face recognition. 2014 IEEE 22nd European Signal Processing Conference (EUSIPCO), Lisbon, Portugal, Sept 2014, pp. 526-530. (IEEE Xplore)