
Risk Bounds for Overparameterized Maximum Margin Classification on SubGaussian Mixtures
Modern machine learning systems such as deep neural networks are often h...
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Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
We analyze the properties of adversarial training for learning adversari...
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Benign Overfitting of ConstantStepsize SGD for Linear Regression
There is an increasing realization that algorithmic inductive biases are...
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Batched Neural Bandits
In many sequential decisionmaking problems, the individuals are split i...
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Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation
We study the reinforcement learning for finitehorizon episodic Markov d...
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Almost Optimal Algorithms for Twoplayer Markov Games with Linear Function Approximation
We study reinforcement learning for twoplayer zerosum Markov games wit...
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Nearly Minimax Optimal Regret for Learning Infinitehorizon Averagereward MDPs with Linear Function Approximation
We study reinforcement learning in an infinitehorizon averagereward se...
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Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints
We study reinforcement learning (RL) with linear function approximation ...
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Provable Generalization of SGDtrained Neural Networks of Any Width in the Presence of Adversarial Label Noise
We consider a onehiddenlayer leaky ReLU network of arbitrary width tra...
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Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
We study reinforcement learning (RL) with linear function approximation ...
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Neural Contextual Bandits with Deep Representation and Shallow Exploration
We study a general class of contextual bandits, where each contextactio...
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Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
Reinforcement learning (RL) with linear function approximation has recei...
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Provable MultiObjective Reinforcement Learning with Generative Models
Multiobjective reinforcement learning (MORL) is an extension of ordinar...
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Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate
Understanding the algorithmic regularization effect of stochastic gradie...
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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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Quanquan Gu
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Assistant Professor in the Department of Computer Science at the University of Virginia