
Faster Convergence of Stochastic Gradient Langevin Dynamics for NonLogConcave Sampling
We establish a new convergence analysis of stochastic gradient Langevin ...
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Does Network Width Really Help Adversarial Robustness?
Adversarial training is currently the most powerful defense against adve...
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Efficient Robust Training via Backward Smoothing
Adversarial training is so far the most effective strategy in defending ...
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Neural Thompson Sampling
Thompson Sampling (TS) is one of the most effective algorithms for solvi...
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Minimax Optimal Reinforcement Learning for Discounted MDPs
We study the reinforcement learning problem for discounted Markov Decisi...
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Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins
We analyze the properties of gradient descent on convex surrogates for t...
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Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
Modern tasks in reinforcement learning are always with large state and a...
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RayS: A Ray Searching Method for Hardlabel Adversarial Attack
Deep neural networks are vulnerable to adversarial attacks. Among differ...
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Optimization Theory for ReLU Neural Networks Trained with Normalization Layers
The success of deep neural networks is in part due to the use of normali...
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Agnostic Learning of a Single Neuron with Gradient Descent
We consider the problem of learning the bestfitting single neuron as me...
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Revisiting Membership Inference Under Realistic Assumptions
Membership inference attacks on models trained using machine learning ha...
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A Finite Time Analysis of Two TimeScale Actor Critic Methods
Actorcritic (AC) methods have exhibited great empirical success compare...
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Exploring Private Federated Learning with Laplacian Smoothing
Federated learning aims to protect data privacy by collaboratively learn...
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MOTS: Minimax Optimal Thompson Sampling
Thompson sampling is one of the most widely used algorithms for many onl...
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On the Global Convergence of Training Deep Linear ResNets
We study the convergence of gradient descent (GD) and stochastic gradien...
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Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
Starting with Gilmer et al. (2018), several works have demonstrated the ...
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Double ExplorethenCommit: Asymptotic Optimality and Beyond
We study the twoarmed bandit problem with subGaussian rewards. The expl...
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A FiniteTime Analysis of QLearning with Neural Network Function Approximation
Qlearning with neural network function approximation (neural Qlearning...
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Rank Aggregation via Heterogeneous Thurstone Preference Models
We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranke...
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Towards Understanding the Spectral Bias of Deep Learning
An intriguing phenomenon observed during training neural networks is the...
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How Much Overparameterization Is Sufficient to Learn Deep ReLU Networks?
A recent line of research on deep learning focuses on the extremely over...
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LayerDependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Graph convolutional networks (GCNs) have recently received wide attentio...
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Tight Sample Complexity of Learning Onehiddenlayer Convolutional Neural Networks
We study the sample complexity of learning onehiddenlayer convolutiona...
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Neural Contextual Bandits with Upper Confidence BoundBased Exploration
We study the stochastic contextual bandit problem, where the reward is g...
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Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo
As an important Markov Chain Monte Carlo (MCMC) method, stochastic gradi...
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Efficient PrivacyPreserving Nonconvex Optimization
While many solutions for privacypreserving convex empirical risk minimi...
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AlgorithmDependent Generalization Bounds for Overparameterized Deep Residual Networks
The skipconnections used in residual networks have become a standard ar...
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Sample Efficient Policy Gradient Methods with Recursive Variance Reduction
Improving the sample efficiency in reinforcement learning has been a lon...
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A Knowledge Transfer Framework for Differentially Private Sparse Learning
We study the problem of estimating high dimensional models with underlyi...
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DPLSSGD: A Stochastic Optimization Method to Lift the Utility in PrivacyPreserving ERM
Machine learning (ML) models trained by differentially private stochasti...
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An Improved Analysis of Training Overparameterized Deep Neural Networks
A recent line of research has shown that gradientbased algorithms with ...
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Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
We study the training and generalization of deep neural networks (DNNs) ...
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An Improved Convergence Analysis of Stochastic VarianceReduced Policy Gradient
We revisit the stochastic variancereduced policy gradient (SVRPG) metho...
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A Generalization Theory of Gradient Descent for Learning Overparameterized Deep ReLU Networks
Empirical studies show that gradient based methods can learn deep neural...
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Stochastic Recursive VarianceReduced Cubic Regularization Methods
Stochastic VarianceReduced Cubic regularization (SVRC) algorithms have ...
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Lower Bounds for Smooth Nonconvex FiniteSum Optimization
Smooth finitesum optimization has been widely studied in both convex an...
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Sample Efficient Stochastic VarianceReduced Cubic Regularization Method
We propose a sample efficient stochastic variancereduced cubic regulari...
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A FrankWolfe Framework for Efficient and Effective Adversarial Attacks
Depending on how much information an adversary can access to, adversaria...
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Stochastic Gradient Descent Optimizes Overparameterized Deep ReLU Networks
We study the problem of training deep neural networks with Rectified Lin...
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On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
Adaptive gradient methods are workhorses in deep learning. However, the ...
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Finding Local Minima via Stochastic Nested Variance Reduction
We propose two algorithms that can find local minima faster than the sta...
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Stochastic Nested Variance Reduction for Nonconvex Optimization
We study finitesum nonconvex optimization problems, where the objective...
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Learning Onehiddenlayer ReLU Networks via Gradient Descent
We study the problem of learning onehiddenlayer neural networks with R...
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Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Adaptive gradient methods, which adopt historical gradient information t...
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Fast and Sample Efficient Inductive Matrix Completion via MultiPhase Procrustes Flow
We revisit the inductive matrix completion problem that aims to recover ...
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Stochastic VarianceReduced Cubic Regularized Newton Method
We propose a stochastic variancereduced cubic regularized Newton method...
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Stochastic VarianceReduced Hamilton Monte Carlo Methods
We propose a fast stochastic Hamilton Monte Carlo (HMC) method, for samp...
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Thirdorder Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima
We propose stochastic optimization algorithms that can find local minima...
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Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently
We propose a family of nonconvex optimization algorithms that are able t...
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Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
We present a unified framework to analyze the global convergence of Lang...
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Quanquan Gu
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Assistant Professor in the Department of Computer Science at the University of Virginia