
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
With deep neural networks providing stateoftheart machine learning mo...
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Stochastic Recursive Gradient Algorithm for Nonconvex Optimization
In this paper, we study and analyze the minibatch version of StochAstic...
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SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
In this paper, we propose a StochAstic Recursive grAdient algoritHm (SAR...
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When Does Stochastic Gradient Algorithm Work Well?
In this paper, we consider a general stochastic optimization problem whi...
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SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Stochastic gradient descent (SGD) is the optimization algorithm of choic...
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Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
We study Stochastic Gradient Descent (SGD) with diminishing step sizes f...
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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...
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Inexact SARAH Algorithm for Stochastic Optimization
We develop and analyze a variant of variance reducing stochastic gradien...
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DTN: A Learning Rate Scheme with Convergence Rate of O(1/t) for SGD
We propose a novel diminishing learning rate scheme, coined DecreasingT...
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Optimal FiniteSum Smooth NonConvex Optimization with SARAH
The total complexity (measured as the total number of gradient computati...
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ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
In this paper, we propose a new stochastic algorithmic framework to solv...
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Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization
We introduce a hybrid stochastic estimator to design stochastic gradient...
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A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization
In this paper, we introduce a new approach to develop stochastic optimiz...
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BUZz: BUffer Zones for defending adversarial examples in image classification
We propose a novel defense against all existing gradient based adversari...
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Stochastic GaussNewton Algorithms for Nonconvex Compositional Optimization
We develop two new stochastic GaussNewton algorithms for solving a clas...
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A Unified Convergence Analysis for ShufflingType Gradient Methods
In this paper, we provide a unified convergence analysis for a class of ...
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A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
We propose a novel hybrid stochastic policy gradient estimator by combin...
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FiniteTime Analysis of Stochastic Gradient Descent under Markov Randomness
Motivated by broad applications in reinforcement learning and machine le...
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Hybrid VarianceReduced SGD Algorithms For NonconvexConcave Minimax Problems
We develop a novel variancereduced algorithm to solve a stochastic nonc...
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Lam M. Nguyen
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