A. Berahas

发表

An investigation of Newton-Sketch and subsampled Newton methods

Jorge Nocedal, Albert S. Berahas, Raghu Bollapragada, 2017, Optim. Methods Softw..

Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization

K. Scheinberg, K. Choromanski, A. Berahas, 2019, 1905.13043.

Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise

Albert S. Berahas, Katya Scheinberg, Liyuan Cao, 2019, SIAM J. Optim..

A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization

K. Scheinberg, K. Choromanski, A. Berahas, 2019, Foundations of Computational Mathematics.

Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods

Jorge Nocedal, Albert S. Berahas, Richard H. Byrd, 2018, SIAM J. Optim..

Limited-memory BFGS with displacement aggregation

Frank E. Curtis, A. Berahas, Baoyu Zhou, 2019, Mathematical Programming.

DERIVATIVE-FREE OPTIMIZATION OF NOISY FUNCTIONS VIA

J. Nocedal, R. Byrd, A. Berahas, 2018 .

First- and Second-Order High Probability Complexity Bounds for Trust-Region Methods with Noisy Oracles

K. Scheinberg, A. Berahas, Liyuan Cao, 2022, 2205.03667.

An Adaptive Sampling Sequential Quadratic Programming Method for Equality Constrained Stochastic Optimization

Raghu Bollapragada, A. Berahas, Baoyu Zhou, 2022, 2206.00712.

Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients: Convergence, Divergence and Limitations of its Continuous Approximation

A. Berahas, V. Patel, 2022, 2210.02418.

Full-low evaluation methods for derivative-free optimization

L. Vicente, A. Berahas, Oumaima Sohab, 2021, Optim. Methods Softw..

A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization

K. Scheinberg, K. Choromanski, A. Berahas, 2021, Foundations of Computational Mathematics.

A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimization

A. Berahas, Baoyu Zhou, Miao Xie, 2023, 2301.00477.

adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs pdf

Albert S. Berahas, Nitish Shirish Keskar, N. Keskar, 2015, ECML/PKDD.

A Multi-Batch L-BFGS Method for Machine Learning

Jorge Nocedal, Albert S. Berahas, Martin Takác, 2016, NIPS.

Quasi-Newton methods for machine learning: forget the past, just sample

Albert S. Berahas, Majid Jahani, Martin Tak'avc, 2019, Optim. Methods Softw..

A robust multi-batch L-BFGS method for machine learning*

Albert S. Berahas, Martin Takác, Martin Takác, 2017, Optim. Methods Softw..

First- and Second-Order High Probability Complexity Bounds for Trust-Region Methods with Noisy Oracles

K. Scheinberg, A. Berahas, Liyuan Cao, 2022, 2205.03667.

Balancing Communication and Computation in Distributed Optimization

Albert S. Berahas, Ermin Wei, Nitish Shirish Keskar, 2017, IEEE Transactions on Automatic Control.

Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization pdf

Daniel P. Robinson, Frank E. Curtis, Albert Berahas, 2020, SIAM J. Optim..

An Adaptive Sampling Sequential Quadratic Programming Method for Equality Constrained Stochastic Optimization

Raghu Bollapragada, A. Berahas, Baoyu Zhou, 2022, 2206.00712.

Modeling and Predicting Heavy-Duty Vehicle Engine-Out and Tailpipe Nitrogen Oxide (NOx) Emissions Using Deep Learning

Matthew J. Brusstar, Tim A. Nevius, A. Berahas, 2022, Frontiers in Mechanical Engineering.

Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization

Raghu Bollapragada, A. Berahas, Shagun Gupta, 2023, 2303.14289.

On the Convergence of Nested Decentralized Gradient Methods With Multiple Consensus and Gradient Steps

Raghu Bollapragada, A. Berahas, Ermin Wei, 2020, IEEE Transactions on Signal Processing.

Nested Distributed Gradient Methods with Adaptive Quantized Communication

Albert S. Berahas, Charikleia Iakovidou, Ermin Wei, 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).

SONIA: A Symmetric Blockwise Truncated Optimization Algorithm

Albert S. Berahas, Mohammadreza Nazari, Majid Jahani, 2020, AISTATS.

Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction

A. Berahas, Baoyu Zhou, Jiahao Shi, 2022, Computational Optimization and Applications.

A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear-Equality-Constrained Optimization with Rank-Deficient Jacobians

Daniel P. Robinson, Frank E. Curtis, Michael O'Neill, 2021, Mathematics of Operations Research.

Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise

K. Scheinberg, A. Berahas, Liyuan Cao, 2019, SIAM J. Optim..

Quasi-Newton Methods for Deep Learning: Forget the Past, Just Sample pdf

Albert S. Berahas, Martin Takác, Majid Jahani, 2019, ArXiv.

Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design

R. Kontar, Xubo Yue, A. Berahas, 2023, ArXiv.

Scaling Up Quasi-newton Algorithms: Communication Efficient Distributed SR1

Albert S. Berahas, Mohammadreza Nazari, Majid Jahani, 2019, LOD.

Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients

A. Berahas, V. Patel, 2022, 2210.02418.

Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations pdf

Shamim N. Pakzad, Albert S. Berahas, Nur Sila Gulgec, 2020, 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA).

Sampled Quasi-Newton Methods for Deep Learning

Martin Takác, A. Berahas, Majid Jahani, 2019 .

First- and second-order high probability complexity bounds for trust-region methods with noisy oracles

K. Scheinberg, A. Berahas, Liyuan Cao, 2022, Mathematical Programming.