Qingsong Gu

Qingsong Gu's AcademicInfluence.com Rankings

Qingsong Gu

Computer Science

#6745

World Rank

#7109

Historical Rank

Data Mining

#155

World Rank

#155

Historical Rank

Machine Learning

#2291

World Rank

#2319

Historical Rank

Database

#3830

World Rank

#3985

Historical Rank

computer-science Degrees

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Computer Science

Qingsong Gu's Degrees

PhD Computer Science University of California, Riverside
Masters Computer Science University of California, Riverside
Bachelors Computer Science University of Science and Technology of China

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Qingsong Gu's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Generalized Fisher Score for Feature Selection (2011) (653)
Personalized entity recommendation: a heterogeneous information network approach (2014) (584)
Gradient descent optimizes over-parameterized deep ReLU networks (2018) (506)
Improving Adversarial Robustness Requires Revisiting Misclassified Examples (2020) (362)
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks (2019) (272)
Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs (2010) (252)
On the Convergence and Robustness of Adversarial Training (2021) (235)
Co-clustering on manifolds (2009) (224)
Joint Feature Selection and Subspace Learning (2011) (203)
An Improved Analysis of Training Over-parameterized Deep Neural Networks (2019) (167)
Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification (2009) (163)
Recommendation in heterogeneous information networks with implicit user feedback (2013) (153)
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization (2017) (150)
Active Learning: A Survey (2014) (150)
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks (2018) (147)
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks (2019) (138)
ClusCite: effective citation recommendation by information network-based clustering (2014) (132)
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization (2018) (124)
Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks (2019) (118)
Neural Contextual Bandits with UCB-based Exploration (2019) (117)
Citation Prediction in Heterogeneous Bibliographic Networks (2012) (117)
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization (2018) (117)
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes (2020) (115)
Learning One-hidden-layer ReLU Networks via Gradient Descent (2018) (115)
Towards Understanding the Spectral Bias of Deep Learning (2019) (112)
Stochastic Nested Variance Reduction for Nonconvex Optimization (2018) (111)
Clustered Support Vector Machines (2013) (111)
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? (2019) (98)
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping (2020) (95)
Contextual Bandits in a Collaborative Environment (2016) (94)
Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling (2012) (92)
A Finite Time Analysis of Two Time-Scale Actor Critic Methods (2020) (86)
Collaborative Filtering with Entity Similarity Regularization in Heterogeneous Information Networks (2013) (86)
Correlated multi-label feature selection (2011) (85)
Linear Discriminant Dimensionality Reduction (2011) (84)
A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression (2014) (81)
Is neuron coverage a meaningful measure for testing deep neural networks? (2020) (78)
A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks (2019) (77)
RayS: A Ray Searching Method for Hard-label Adversarial Attack (2020) (74)
Revisiting Membership Inference Under Realistic Assumptions (2020) (72)
Towards feature selection in network (2011) (69)
Local Learning Regularized Nonnegative Matrix Factorization (2009) (68)
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient (2019) (68)
A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation (2016) (67)
Low-Rank and Sparse Structure Pursuit via Alternating Minimization (2016) (63)
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction (2019) (61)
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation (2020) (60)
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks (2018) (57)
Do Wider Neural Networks Really Help Adversarial Robustness? (2020) (55)
Learning a Kernel for Multi-Task Clustering (2011) (53)
Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization (2018) (52)
Identifying gene regulatory network rewiring using latent differential graphical models (2016) (50)
High Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality (2014) (48)
Differentially Private Hypothesis Transfer Learning (2018) (47)
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation (2019) (46)
Trustworthiness analysis of sensor data in cyber-physical systems (2013) (46)
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality (2015) (46)
Towards Active Learning on Graphs: An Error Bound Minimization Approach (2012) (45)
Neighborhood Preserving Nonnegative Matrix Factorization (2009) (44)
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks (2021) (43)
On Trivial Solution and Scale Transfer Problems in Graph Regularized NMF (2011) (43)
Improving Neural Language Generation with Spectrum Control (2020) (42)
Neural Thompson Sampling (2020) (41)
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks (2020) (37)
Robust Tensor Decomposition with Gross Corruption (2014) (36)
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization (2019) (34)
Agnostic Learning of a Single Neuron with Gradient Descent (2020) (33)
Stochastic Variance-Reduced Hamilton Monte Carlo Methods (2018) (32)
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures (2021) (32)
Benign Overfitting of Constant-Stepsize SGD for Linear Regression (2021) (32)
Sparse PCA with Oracle Property (2014) (31)
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery (2018) (30)
Batch-Mode Active Learning via Error Bound Minimization (2014) (30)
Selective sampling on graphs for classification (2013) (29)
Accelerated Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Nonconvex Optimization (2016) (28)
Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing (2015) (28)
Efficient Robust Training via Backward Smoothing (2020) (27)
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks (2019) (26)
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation (2015) (26)
Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates (2016) (26)
A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery (2018) (26)
Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics (2018) (25)
Neural Contextual Bandits with Deep Representation and Shallow Exploration (2020) (25)
Stochastic Variance-Reduced Cubic Regularized Newton Methods (2018) (24)
Wireless sensor network data collection by connected cooperative UAVs (2013) (24)
Accelerated Stochastic Block Coordinate Descent with Optimal Sampling (2016) (24)
Local and Global Inference for High Dimensional Gaussian Copula Graphical Models (2015) (24)
COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support (2020) (23)
On the Global Convergence of Training Deep Linear ResNets (2020) (23)
Selective Labeling via Error Bound Minimization (2012) (22)
Stochastic Recursive Variance-Reduced Cubic Regularization Methods (2019) (22)
Stochastic Variance-Reduced Cubic Regularized Newton Method (2018) (22)
Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation (2021) (21)
Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms (2018) (21)
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization (2017) (20)
Finding Local Minima via Stochastic Nested Variance Reduction (2018) (20)
Optimal Statistical and Computational Rates for One Bit Matrix Completion (2016) (20)
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow (2018) (20)
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs (2020) (20)
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption (2017) (20)
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling (2020) (20)
Local and Global Inference for High Dimensional Nonparanormal Graphical Models (2015) (20)
Variance-Aware Off-Policy Evaluation with Linear Function Approximation (2021) (19)
Benign Overfitting in Two-layer Convolutional Neural Networks (2022) (19)
Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning (2019) (19)
Stochastic Variance-Reduced Cubic Regularization Methods (2019) (19)
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction (2019) (18)
Communication-efficient Distributed Sparse Linear Discriminant Analysis (2016) (18)
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks (2019) (18)
Semiparametric Differential Graph Models (2016) (18)
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM (2019) (18)
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers (2020) (17)
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation (2021) (16)
MOTS: Minimax Optimal Thompson Sampling (2020) (16)
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate (2020) (16)
Statistical Machine Learning in Model Predictive Control of Nonlinear Processes (2021) (16)
Learning Stochastic Shortest Path with Linear Function Approximation (2021) (16)
IntruMine: Mining Intruders in Untrustworthy Data of Cyber-physical Systems (2012) (16)
Classification with Active Learning and Meta-Paths in Heterogeneous Information Networks (2015) (16)
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions (2018) (16)
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models (2020) (15)
Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics (2019) (15)
High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm (2017) (15)
Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization (2017) (15)
Exploring Private Federated Learning with Laplacian Smoothing (2020) (15)
High-dimensional Time Series Clustering via Cross-Predictability (2017) (15)
Provably Efficient Representation Learning in Low-rank Markov Decision Processes (2021) (15)
Continuous-trait probabilistic model for comparing multi-species functional genomic data (2018) (14)
Transductive Classification via Dual Regularization (2009) (14)
HTF: a novel feature for general crack detection (2010) (14)
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima (2017) (14)
Efficient Privacy-Preserving Nonconvex Optimization (2019) (13)
Batched Neural Bandits (2021) (13)
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise (2021) (13)
Sharp Computational-Statistical Phase Transitions via Oracle Computational Model (2015) (13)
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent (2021) (13)
Aggregating Private Sparse Learning Models Using Multi-Party Computation (2016) (13)
Subspace maximum margin clustering (2009) (12)
The Benefits of Implicit Regularization from SGD in Least Squares Problems (2021) (12)
GIN: A Clustering Model for Capturing Dual Heterogeneity in Networked Data (2015) (12)
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression (2021) (12)
Local Relevance Weighted Maximum Margin Criterion for Text Classification (2009) (12)
A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery (2017) (12)
A Nonconvex Free Lunch for Low-Rank plus Sparse Matrix Recovery (2017) (12)
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration (2019) (12)
A Framework of Mining Trajectories from Untrustworthy Data in Cyber-Physical System (2015) (11)
A similarity measure under Log-Euclidean metric for stereo matching (2008) (11)
Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games (2021) (11)
Efficient Privacy-Preserving Stochastic Nonconvex Optimization. (2019) (11)
A new formula for the L norm (2021) (11)
Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds (2020) (10)
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption (2017) (10)
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions (2022) (10)
Does Network Width Really Help Adversarial Robustness? (2020) (10)
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization (2018) (10)
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization (2021) (10)
Locality Preserving Feature Learning (2012) (10)
A Unified Framework for Low-Rank plus Sparse Matrix Recovery (2017) (9)
Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently (2017) (9)
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins (2020) (9)
Rank Aggregation via Heterogeneous Thurstone Preference Models (2019) (9)
Statistical Machine‐Learning ‐based Predictive Control of Uncertain Nonlinear Processes (2022) (9)
Towards Understanding Mixture of Experts in Deep Learning (2022) (9)
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs (2021) (8)
Self-training Converts Weak Learners to Strong Learners in Mixture Models (2021) (8)
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs (2022) (8)
Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent (2017) (8)
Linear Contextual Bandits with Adversarial Corruptions (2021) (8)
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation (2021) (8)
Double Explore-then-Commit: Asymptotic Optimality and Beyond (2020) (8)
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise (2021) (8)
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo (2019) (8)
On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients (2021) (7)
Differentially Private Federated Learning with Laplacian Smoothing (2020) (7)
A Knowledge Transfer Framework for Differentially Private Sparse Learning (2019) (7)
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference (2017) (7)
Online Spectral Learning on a Graph with Bandit Feedback (2014) (7)
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery (2017) (7)
Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate (2020) (7)
Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates (2016) (7)
Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements (2017) (6)
Towards Personalized Learning in Mobile Sensing Systems (2018) (6)
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes (2021) (6)
Belief propagation on Riemannian manifold for stereo matching (2008) (6)
A novel similarity measure under Riemannian metric for stereo matching (2008) (6)
Two dimensional Maximum Margin Criterion (2009) (6)
Pure Exploration in Kernel and Neural Bandits (2021) (6)
Learning Neural Contextual Bandits through Perturbed Rewards (2022) (6)
Two Dimensional Nonnegative Matrix Factorization (2009) (6)
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits (2022) (6)
Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization (2020) (6)
Mining lines in the sand: on trajectory discovery from untrustworthy data in cyber-physical system (2013) (6)
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes (2022) (5)
Multi-task cox proportional hazard model for predicting risk of unplanned hospital readmission (2017) (5)
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks (2021) (5)
A practical algorithm for learning scene information from monocular video. (2008) (5)
On the Statistical Limits of Convex Relaxations (2015) (5)
Communication-efficient Distributed Estimation and Inference for Transelliptical Graphical Models (2016) (4)
Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method (2018) (4)
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift (2022) (4)
Mixed linear modeling techniques for predicting fatalities in vehicle crashes (2017) (4)
Multiframe Motion Segmentation via Penalized MAP Estimation and Linear Programming (2009) (4)
Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds (2022) (4)
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation (2021) (4)
Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation (2021) (4)
Provable Multi-Objective Reinforcement Learning with Generative Models (2020) (4)
On the Convergence of Certified Robust Training with Interval Bound Propagation (2022) (4)
Adaptive Differentially Private Empirical Risk Minimization (2021) (4)
Benign Overfitting in Adversarially Robust Linear Classification (2021) (3)
On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs (2022) (3)
Online and active learning of big networks: theory and algorithms (2014) (3)
Minimax Optimal Reinforcement Learning for Discounted MDPs (2020) (3)
Regular simplex criterion: A novel feature extraction criterion (2009) (2)
Learning Contextual Bandits Through Perturbed Rewards (2022) (2)
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons (2021) (2)
Multiple Kernel Maximum Margin Criterion (2009) (2)
Unsupervised Link Selection in Networks (2013) (2)
A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression (2014) (2)
Iterative Teacher-Aware Learning (2021) (2)
Normalized Tight Frame Wavelet Sets in R d (2001) (2)
Exploring the use of adaptive gradient methods in effective deep learning systems (2018) (2)
Prediction in Heterogeneous Bibliographic Networks (2012) (1)
HOW MUCH OVER-PARAMETERIZATION IS SUFFI- (2020) (1)
Towards Feature Selection in Networks (2011) (1)
Benign Overfitting for Two-layer ReLU Networks (2023) (1)
Robust Classification of Information Networks by Consistent Graph Learning (2015) (1)
Active Ranking without Strong Stochastic Transitivity (2022) (1)
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning (2022) (1)
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime (2022) (1)
Almost Optimal Algorithms for Two-player Zero-Sum Markov Games with Linear Function Approximation (2021) (1)
On the statistical limits of convex relaxations: a case study (2016) (0)
Local Learning Regularized Nonnegative Matrix Factorization Quanquan Gu (2009) (0)
Local Learning Regularized Nonnegative Matrix (2009) (0)
Communication-e � cient Distributed Sparse Linear Discriminant Analysis (2017) (0)
NCIS: A NETWORK-ASSISTED CO-CLUSTERING ALGORITHM TO DISCOVER CANCER SUBTYPES BASED ON GENE EXPRESSION BY (2013) (0)
Developing a spatial clustering model to identify dangerous intersections in Virginia (2018) (0)
Sampling from Non-Log-Concave Distributions via Stochastic Variance-Reduced Gradient Langevin Dynamics (2019) (0)
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency (2023) (0)
NeuralUCB: Contextual Bandits with Neural Network-Based Exploration (2019) (0)
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits (2022) (0)
On the Dynamics and Convergence of Weight Normalization for Training Neural Networks (2019) (0)
Anisotropic versions of the Brezis-Van Schaftingen-Yung approach at s = 1 and s = 0 (2022) (0)
O N THE C ONVERGENCE OF C ERTIFIED R OBUST T RAINING WITH I NTERVAL B OUND P ROPAGATION (2022) (0)
DP-LSSGD: An Optimization Method to Lift the Utility in Privacy-Preserving ERM (2019) (0)
Borda Regret Minimization for Generalized Linear Dueling Bandits (2023) (0)
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs (2023) (0)
Fairness-Preserving Empirical Risk Minimization (2019) (0)
Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples (2023) (0)
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium (2022) (0)
Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization (2022) (0)
Personalized Federated Learning under Mixture of Distributions (2023) (0)
Training Deep Neural Networks with Partially Adaptive Momentum (2019) (0)
Neighborhood Preserving Nonnegative Matrix (2009) (0)
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes (2022) (0)
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium (2022) (0)
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima (2021) (0)
Towards Understanding the Mixture-of-Experts Layer in Deep Learning (2022) (0)
High Dimensional Multivariate Regression and Precision Matrix Estimation via Nonconvex Optimization (2016) (0)
The Benefits of Mixup for Feature Learning (2023) (0)

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Qingsong Gu's AcademicInfluence.com Rankings