Qingsong Gu
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Qingsong Gucomputer-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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(Suggest an Edit or Addition)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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