66 citations · 196 across the 21 of their papers we have counts for
11 papers · 1 filter
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
Haoran Zhu, Pavankumar Murali, Dzung T. Phan +2
Several recent publications report advances in training optimal decision trees (ODT) using mixed-integer programs (MIP), due to algorithmic advances in integer programming and a gr…
SMG: A Shuffling Gradient-Based Method with Momentum
Trang H. Tran, Lam M. Nguyen, Quoc Tran-Dinh
We combine two advanced ideas widely used in optimization for machine learning: shuffling strategy and momentum technique to develop a novel shuffling gradient-based method with mo…
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes
Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen +3
Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where multiple threads in parallel access a common repository containing training data, perform SGD iterations an…
An Optimal Hybrid Variance-Reduced Algorithm for Stochastic Composite Nonconvex Optimization
Deyi Liu, Lam M. Nguyen, Quoc Tran-Dinh
In this note we propose a new variant of the hybrid variance-reduced proximal gradient method in [7] to solve a common stochastic composite nonconvex optimization problem under sta…
Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise
Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen +3
The feasibility of federated learning is highly constrained by the server-clients infrastructure in terms of network communication. Most newly launched smartphones and IoT devices…
Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems
Quoc Tran-Dinh, Deyi Liu, Lam M. Nguyen
We develop a novel and single-loop variance-reduced algorithm to solve a class of stochastic nonconvex-convex minimax problems involving a nonconvex-linear objective function, whic…