A Progressive Batching L-BFGS Method for Machine Learning. - Raghu Bollapragada; Dheevatsa Mudigere; Jorge Nocedal; Hao-Jun Michael Shi; Ping Tak Peter Tang (ICML, 2018)

Adaptive Sampling Strategies for Stochastic Optimization. - Raghu Bollapragada; Richard H. Byrd; Jorge Nocedal (SIAM J. Optim., 2018)

Nonlinear Acceleration of Primal-Dual Algorithms. - Raghu Bollapragada; Damien Scieur; Alexandre d'Aspremont (AISTATS, 2019)

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

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

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)

Learning a neural Pareto manifold extractor with constraints. - Soumyajit Gupta; Gurpreet Singh; Raghu Bollapragada; Matthew Lease (UAI, 2022)

On the fast convergence of minibatch heavy ball momentum. - Raghu Bollapragada; Tyler Chen; Rachel A. Ward (CoRR, 2022)

Nonlinear acceleration of momentum and primal-dual algorithms. - Raghu Bollapragada; Damien Scieur; Alexandre d'Aspremont (Math. Program., 2023)

Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization. - Raghu Bollapragada; Stefan M. Wild (Math. Program. Comput., 2023)

An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints. - Raghu Bollapragada; Cem Karamanli; Brendan Keith; Boyan S. Lazarov; Socratis Petrides; Jingyi Wang (Comput. Math. Appl., 2023)

Central Finite-Difference Based Gradient Estimation Methods for Stochastic Optimization. - Raghu Bollapragada; Cem Karamanli; Stefan M. Wild (WSC, 2024)

Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods. - Thomas O'Leary-Roseberry; Raghu Bollapragada (CoRR, 2024)

Adaptive Consensus: A Network Pruning Approach for Decentralized Optimization. - Suhail M. Shah; Albert S. Berahas; Raghu Bollapragada (SIAM J. Optim., 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 flexible gradient tracking algorithmic framework for decentralized optimization. - Albert S. Berahas; Raghu Bollapragada; Shagun Gupta (Comput. Optim. Appl., 2025)