Sparse representation and least squares-based classification in face recognition. - Michael Iliadis; Leonidas Spinoulas; Albert S. Berahas; Haohong Wang; Aggelos K. Katsaggelos (EUSIPCO, 2014)

Multi-model robust error correction for face recognition. - Michael Iliadis; Leonidas Spinoulas; Albert S. Berahas; Haohong Wang; Aggelos K. Katsaggelos (ICIP, 2016)

A Multi-Batch L-BFGS Method for Machine Learning. - Albert S. Berahas; Jorge Nocedal; Martin Takác 001 (NIPS, 2016)

adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs. - Nitish Shirish Keskar; Albert S. Berahas (ECML/PKDD (1), 2016)

Nested Distributed Gradient Methods with Adaptive Quantized Communication. - Albert S. Berahas; Charikleia Iakovidou; Ermin Wei (CDC, 2019)

Quasi-Newton Methods for Deep Learning: Forget the Past, Just Sample. - Albert S. Berahas; Majid Jahani; Martin Takác 001 (CoRR, 2019)

Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods. - Albert S. Berahas; Richard H. Byrd; Jorge Nocedal (SIAM J. Optim., 2019)

Balancing Communication and Computation in Distributed Optimization. - Albert S. Berahas; Raghu Bollapragada; Nitish Shirish Keskar; Ermin Wei (IEEE Trans. Autom. Control., 2019)

Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations. - Zheng Shi; Nur Sila Gulgec; Albert S. Berahas; Shamim N. Pakzad; Martin Takác 001 (ICMLA, 2020)

Scaling Up Quasi-newton Algorithms: Communication Efficient Distributed SR1. - Majid Jahani; MohammadReza Nazari; Sergey Rusakov 001; Albert S. Berahas; Martin Takác 001 (LOD (1), 2020)

An investigation of Newton-Sketch and subsampled Newton methods. - Albert S. Berahas; Raghu Bollapragada; Jorge Nocedal (Optim. Methods Softw., 2020)

A robust multi-batch L-BFGS method for machine learning. - Albert S. Berahas; Martin Takác 001 (Optim. Methods Softw., 2020)

SONIA: A Symmetric Blockwise Truncated Optimization Algorithm. - Majid Jahani; MohammadReza Nazari; Rachael Tappenden; Albert S. Berahas; Martin Takác 001 (AISTATS, 2021)

Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization. - Albert S. Berahas; Frank E. Curtis; Daniel P. Robinson; Baoyu Zhou (SIAM J. Optim., 2021)

Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise. - Albert S. Berahas; Liyuan Cao; Katya Scheinberg (SIAM J. Optim., 2021)

On the Convergence of Nested Decentralized Gradient Methods With Multiple Consensus and Gradient Steps. - Albert S. Berahas; Raghu Bollapragada; Ermin Wei (IEEE Trans. Signal Process., 2021)

A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization. - Albert S. Berahas; Liyuan Cao; Krzysztof Choromanski; Katya Scheinberg (Found. Comput. Math., 2022)

Limited-memory BFGS with displacement aggregation. - Albert S. Berahas; Frank E. Curtis; Baoyu Zhou (Math. Program., 2022)

Quasi-Newton methods for machine learning: forget the past, just sample. - Albert S. Berahas; Majid Jahani; Peter Richtárik; Martin Takác 001 (Optim. Methods Softw., 2022)

Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design. - Xubo Yue; Raed Al Kontar; Albert S. Berahas; Yang Liu; Zhenghao Zai; Kevin Edgar; Blake N. Johnson (CoRR, 2023)

Full-low evaluation methods for derivative-free optimization. - Albert S. Berahas; Oumaima Sohab; Luís Nunes Vicente (Optim. Methods Softw., 2023)

Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction. - Albert S. Berahas; Jiahao Shi; Zihong Yi; Baoyu Zhou (Comput. Optim. Appl., 2023)

Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients. - Sachin Garg; Albert S. Berahas; Michal Derezinski (CoRR, 2024)

A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear-Equality-Constrained Optimization with Rank-Deficient Jacobians. - Albert S. Berahas; Frank E. Curtis; Michael O'Neill 002; Daniel P. Robinson (Math. Oper. Res., 2024)

Adaptive Consensus: A Network Pruning Approach for Decentralized Optimization. - Suhail M. Shah; Albert S. Berahas; Raghu Bollapragada (SIAM J. Optim., 2024)

Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients. - Vivak Patel; Albert S. Berahas (SIAM J. Math. Data Sci., 2024)

Non-Uniform Smoothness for Gradient Descent. - Albert S. Berahas; Lindon Roberts; Fred Roosta (Trans. Mach. Learn. Res., 2024)

First- and second-order high probability complexity bounds for trust-region methods with noisy oracles. - Liyuan Cao; Albert S. Berahas; Katya Scheinberg (Math. Program., 2024)

Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization. - Albert S. Berahas; Raghu Bollapragada; Shagun Gupta (J. Optim. Theory Appl., 2024)

A Sequential Quadratic Programming Method With High-Probability Complexity Bounds for Nonlinear Equality-Constrained Stochastic Optimization. - Albert S. Berahas; Miaolan Xie; Baoyu Zhou (SIAM J. Optim., 2025)

Collaborative and Distributed Bayesian Optimization via Consensus. - Xubo Yue; Yang Liu; Albert S. Berahas; Blake N. Johnson; Raed Al Kontar (IEEE Trans Autom. Sci. Eng., 2025)

A flexible gradient tracking algorithmic framework for decentralized optimization. - Albert S. Berahas; Raghu Bollapragada; Shagun Gupta (Comput. Optim. Appl., 2025)