
Federated Learning with Randomized DouglasRachford Splitting Methods
In this paper, we develop two new algorithms, called, FedDR and asyncFed...
read it

Differential Private Hogwild! over Distributed Local Data Sets
We consider the Hogwild! setting where clients use local SGD iterations ...
read it

Shuffling GradientBased Methods with Momentum
We combine two advanced ideas widely used in optimization for machine le...
read it

A Scalable MIPbased Method for Learning Optimal Multivariate Decision Trees
Several recent publications report advances in training optimal decision...
read it

Hogwild! over Distributed Local Data Sets with Linearly Increasing MiniBatch Sizes
Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where...
read it

An Optimal Hybrid VarianceReduced Algorithm for Stochastic Composite Nonconvex Optimization
In this note we propose a new variant of the hybrid variancereduced pro...
read it

Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise
The feasibility of federated learning is highly constrained by the serve...
read it

Hybrid VarianceReduced SGD Algorithms For NonconvexConcave Minimax Problems
We develop a novel variancereduced algorithm to solve a stochastic nonc...
read it

FiniteTime Analysis of Stochastic Gradient Descent under Markov Randomness
Motivated by broad applications in reinforcement learning and machine le...
read it

A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
We propose a novel hybrid stochastic policy gradient estimator by combin...
read it

A Unified Convergence Analysis for ShufflingType Gradient Methods
In this paper, we provide a unified convergence analysis for a class of ...
read it

Stochastic GaussNewton Algorithms for Nonconvex Compositional Optimization
We develop two new stochastic GaussNewton algorithms for solving a clas...
read it

BUZz: BUffer Zones for defending adversarial examples in image classification
We propose a novel defense against all existing gradient based adversari...
read it

A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization
In this paper, we introduce a new approach to develop stochastic optimiz...
read it

Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization
We introduce a hybrid stochastic estimator to design stochastic gradient...
read it

ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
In this paper, we propose a new stochastic algorithmic framework to solv...
read it

Optimal FiniteSum Smooth NonConvex Optimization with SARAH
The total complexity (measured as the total number of gradient computati...
read it

DTN: A Learning Rate Scheme with Convergence Rate of O(1/t) for SGD
We propose a novel diminishing learning rate scheme, coined DecreasingT...
read it

PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
With deep neural networks providing stateoftheart machine learning mo...
read it

Inexact SARAH Algorithm for Stochastic Optimization
We develop and analyze a variant of variance reducing stochastic gradien...
read it

Tight Dimension Independent Lower Bound on Optimal Expected Convergence Rate for Diminishing Step Sizes in SGD
We study convergence of Stochastic Gradient Descent (SGD) for strongly c...
read it

Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
We study Stochastic Gradient Descent (SGD) with diminishing step sizes f...
read it

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Stochastic gradient descent (SGD) is the optimization algorithm of choic...
read it

When Does Stochastic Gradient Algorithm Work Well?
In this paper, we consider a general stochastic optimization problem whi...
read it

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization
In this paper, we study and analyze the minibatch version of StochAstic...
read it

SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
In this paper, we propose a StochAstic Recursive grAdient algoritHm (SAR...
read it
Lam M. Nguyen
verfied profile