Google
Google Scholar
Semantic Scholar
MS Academic
CiteSeerX
ORCIDQuanquan Gu
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2021
[i61]Spencer Frei, Yuan Cao, Quanquan Gu:
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. CoRR abs/2101.01152 (2021)- [i60]Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints. CoRR abs/2101.02195 (2021) - 2020
- [j7]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduction for Nonconvex Optimization. J. Mach. Learn. Res. 21: 103:1-103:63 (2020) - [j6]Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu:
Gradient descent optimizes over-parameterized deep ReLU networks. Mach. Learn. 109(3): 467-492 (2020) - [c115]Yuan Cao, Quanquan Gu:
Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks. AAAI 2020: 3349-3356 - [c114]Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu:
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. AAAI 2020: 3486-3494 - [c113]Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud:
Rank Aggregation via Heterogeneous Thurstone Preference Models. AAAI 2020: 4353-4360 - [c112]Lingxiao Wang, Quanquan Gu:
A Knowledge Transfer Framework for Differentially Private Sparse Learning. AAAI 2020: 6235-6242 - [c111]Xiao Zhang, Jinghui Chen, Quanquan Gu, David Evans:
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models. AISTATS 2020: 3883-3893 - [c110]Dongruo Zhou, Quanquan Gu:
Stochastic Recursive Variance-Reduced Cubic Regularization Methods. AISTATS 2020: 3980-3990 - [c109]Dongruo Zhou, Yuan Cao, Quanquan Gu:
Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. AISTATS 2020: 4430-4440 - [c108]Pan Xu, Felicia Gao, Quanquan Gu:
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. ICLR 2020 - [c107]Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu:
Improving Adversarial Robustness Requires Revisiting Misclassified Examples. ICLR 2020 - [c106]Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu:
Improving Neural Language Generation with Spectrum Control. ICLR 2020 - [c105]Difan Zou, Philip M. Long, Quanquan Gu:
On the Global Convergence of Training Deep Linear ResNets. ICLR 2020 - [c104]Yonatan Dukler, Quanquan Gu, Guido Montúfar:
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers. ICML 2020: 2751-2760 - [c103]Pan Xu, Quanquan Gu:
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. ICML 2020: 10555-10565 - [c102]Dongruo Zhou, Lihong Li, Quanquan Gu:
Neural Contextual Bandits with UCB-based Exploration. ICML 2020: 11492-11502 - [c101]Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu:
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. IJCAI 2020: 3267-3275 - [c100]Jinghui Chen, Quanquan Gu:
RayS: A Ray Searching Method for Hard-label Adversarial Attack. KDD 2020: 1739-1747 - [c99]Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, Stanley J. Osher:
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. MSML 2020: 328-351 - [c98]Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang:
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. NeurIPS 2020 - [c97]Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of a Single Neuron with Gradient Descent. NeurIPS 2020 - [c96]Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu:
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods. NeurIPS 2020 - [c95]Fabrice Harel-Canada, Lingxiao Wang, Muhammad Ali Gulzar, Quanquan Gu, Miryung Kim:
Is neuron coverage a meaningful measure for testing deep neural networks? ESEC/SIGSOFT FSE 2020: 851-862 - [i59]Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang:
Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds. CoRR abs/2002.04026 (2020) - [i58]Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu:
Double Explore-then-Commit: Asymptotic Optimality and Beyond. CoRR abs/2002.09174 (2020) - [i57]Xiao Zhang, Jinghui Chen, Quanquan Gu, David Evans:
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models. CoRR abs/2003.00378 (2020) - [i56]Difan Zou, Philip M. Long, Quanquan Gu:
On the Global Convergence of Training Deep Linear ResNets. CoRR abs/2003.01094 (2020) - [i55]Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu:
MOTS: Minimax Optimal Thompson Sampling. CoRR abs/2003.01803 (2020) - [i54]Zhicong Liang, Bao Wang, Quanquan Gu, Stanley J. Osher, Yuan Yao:
Exploring Private Federated Learning with Laplacian Smoothing. CoRR abs/2005.00218 (2020) - [i53]Yue Wu, Weitong Zhang
, Pan Xu, Quanquan Gu:
A Finite Time Analysis of Two Time-Scale Actor Critic Methods. CoRR abs/2005.01350 (2020) - [i52]Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu:
Revisiting Membership Inference Under Realistic Assumptions. CoRR abs/2005.10881 (2020) - [i51]Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of a Single Neuron with Gradient Descent. CoRR abs/2005.14426 (2020) - [i50]Yonatan Dukler, Quanquan Gu, Guido Montúfar:
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers. CoRR abs/2006.06878 (2020) - [i49]Jinghui Chen, Quanquan Gu:
RayS: A Ray Searching Method for Hard-label Adversarial Attack. CoRR abs/2006.12792 (2020) - [i48]Dongruo Zhou, Jiafan He, Quanquan Gu:
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. CoRR abs/2006.13165 (2020) - [i47]Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. CoRR abs/2010.00539 (2020) - [i46]Jiafan He, Dongruo Zhou, Quanquan Gu:
Minimax Optimal Reinforcement Learning for Discounted MDPs. CoRR abs/2010.00587 (2020) - [i45]Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu:
Neural Thompson Sampling. CoRR abs/2010.00827 (2020) - [i44]Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu, Jingjing Liu:
Efficient Robust Training via Backward Smoothing. CoRR abs/2010.01278 (2020) - [i43]Boxi Wu, Jinghui Chen, Deng Cai, Xiaofei He, Quanquan Gu:
Does Network Width Really Help Adversarial Robustness? CoRR abs/2010.01279 (2020) - [i42]Difan Zou, Pan Xu, Quanquan Gu:
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. CoRR abs/2010.09597 (2020) - [i41]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu:
Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. CoRR abs/2011.02538 (2020) - [i40]Dongruo Zhou, Jiahao Chen, Quanquan Gu:
Provable Multi-Objective Reinforcement Learning with Generative Models. CoRR abs/2011.10134 (2020) - [i39]Jiafan He, Dongruo Zhou, Quanquan Gu:
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. CoRR abs/2011.11566 (2020) - [i38]Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu:
Neural Contextual Bandits with Deep Representation and Shallow Exploration. CoRR abs/2012.01780 (2020) - [i37]Dongruo Zhou, Quanquan Gu, Csaba Szepesvári:
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. CoRR abs/2012.08507 (2020)
2010 – 2019
- 2019
- [j5]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularization Methods. J. Mach. Learn. Res. 20: 134:1-134:47 (2019) - [c94]Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu:
Learning One-hidden-layer ReLU Networks via Gradient Descent. AISTATS 2019: 1524-1534 - [c93]Difan Zou, Pan Xu, Quanquan Gu:
Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics. AISTATS 2019: 2936-2945 - [c92]Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu:
On the Convergence and Robustness of Adversarial Training. ICML 2019: 6586-6595 - [c91]Dongruo Zhou, Quanquan Gu:
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. ICML 2019: 7574-7583 - [c90]Lingxiao Wang, Quanquan Gu:
Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning. IJCAI 2019: 3740-3747 - [c89]Difan Zou, Quanquan Gu:
An Improved Analysis of Training Over-parameterized Deep Neural Networks. NeurIPS 2019: 2053-2062 - [c88]Difan Zou, Pan Xu, Quanquan Gu:
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. NeurIPS 2019: 3830-3841 - [c87]Yuan Cao, Quanquan Gu:
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. NeurIPS 2019: 10611-10621 - [c86]Yuan Cao, Quanquan Gu:
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. NeurIPS 2019: 10835-10845 - [c85]Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu:
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. NeurIPS 2019: 11247-11256 - [c84]Spencer Frei, Yuan Cao, Quanquan Gu:
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. NeurIPS 2019: 14769-14779 - [c83]Pan Xu, Felicia Gao, Quanquan Gu:
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. UAI 2019: 541-551 - [i36]Dongruo Zhou, Quanquan Gu:
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. CoRR abs/1901.11224 (2019) - [i35]Dongruo Zhou, Quanquan Gu:
Stochastic Recursive Variance-Reduced Cubic Regularization Methods. CoRR abs/1901.11518 (2019) - [i34]Yuan Cao, Quanquan Gu:
A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks. CoRR abs/1902.01384 (2019) - [i33]Pan Xu, Felicia Gao, Quanquan Gu:
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. CoRR abs/1905.12615 (2019) - [i32]Yuan Cao, Quanquan Gu:
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. CoRR abs/1905.13210 (2019) - [i31]Difan Zou, Quanquan Gu:
An Improved Analysis of Training Over-parameterized Deep Neural Networks. CoRR abs/1906.04688 (2019) - [i30]Bao Wang, Quanquan Gu, March Boedihardjo, Farzin Barekat, Stanley J. Osher:
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. CoRR abs/1906.12056 (2019) - [i29]Lingxiao Wang, Quanquan Gu:
A Knowledge Transfer Framework for Differentially Private Sparse Learning. CoRR abs/1909.06322 (2019) - [i28]Pan Xu, Felicia Gao, Quanquan Gu:
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. CoRR abs/1909.08610 (2019) - [i27]Spencer Frei, Yuan Cao, Quanquan Gu:
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. CoRR abs/1910.02934 (2019) - [i26]Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu:
Efficient Privacy-Preserving Nonconvex Optimization. CoRR abs/1910.13659 (2019) - [i25]Bao Wang, Difan Zou, Quanquan Gu, Stanley J. Osher:
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. CoRR abs/1911.00782 (2019) - [i24]Dongruo Zhou, Lihong Li, Quanquan Gu:
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration. CoRR abs/1911.04462 (2019) - [i23]Yuan Cao, Quanquan Gu:
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. CoRR abs/1911.05059 (2019) - [i22]Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu:
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. CoRR abs/1911.07323 (2019) - [i21]Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu:
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? CoRR abs/1911.12360 (2019) - [i20]Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu:
Towards Understanding the Spectral Bias of Deep Learning. CoRR abs/1912.01198 (2019) - [i19]Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud:
Rank Aggregation via Heterogeneous Thurstone Preference Models. CoRR abs/1912.01211 (2019) - [i18]Pan Xu, Quanquan Gu:
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. CoRR abs/1912.04511 (2019) - 2018
- [c82]Pan Xu, Tianhao Wang, Quanquan Gu:
Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms. AISTATS 2018: 1087-1096 - [c81]Xiao Zhang, Lingxiao Wang, Quanquan Gu:
A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery. AISTATS 2018: 1097-1107 - [c80]Wenjun Jiang, Qi Li, Lu Su, Chenglin Miao, Quanquan Gu, Wenyao Xu:
Towards Personalized Learning in Mobile Sensing Systems. ICDCS 2018: 321-333 - [c79]Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu:
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. ICML 2018: 921-930 - [c78]Pan Xu, Tianhao Wang, Quanquan Gu:
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions. ICML 2018: 5488-5497 - [c77]Xiao Zhang, Simon S. Du, Quanquan Gu:
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow. ICML 2018: 5751-5760 - [c76]Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu:
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery. ICML 2018: 5857-5866 - [c75]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularized Newton Method. ICML 2018: 5985-5994 - [c74]Difan Zou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Hamilton Monte Carlo Methods. ICML 2018: 6023-6032 - [c73]Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu:
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. NeurIPS 2018: 3126-3137 - [c72]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization. NeurIPS 2018: 3925-3936 - [c71]Yaodong Yu, Pan Xu, Quanquan Gu:
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima. NeurIPS 2018: 4530-4540 - [c70]Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu:
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization. NeurIPS 2018: 6346-6357 - [c69]Yang Wang, Quanquan Gu, Donald E. Brown:
Differentially Private Hypothesis Transfer Learning. ECML/PKDD (2) 2018: 811-826 - [c68]Yang Yang, Quanquan Gu, Takayo Sasaki, Julianna Crivello, Rachel O'Neill, David M. Gilbert, Jian Ma:
Continuous-Trait Probabilistic Model for Comparing Multi-species Functional Genomic Data. RECOMB 2018: 293-294 - [c67]Difan Zou, Pan Xu, Quanquan Gu:
Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics. UAI 2018: 508-518 - [i17]Difan Zou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Hamilton Monte Carlo Methods. CoRR abs/1802.04791 (2018) - [i16]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularized Newton Method. CoRR abs/1802.04796 (2018) - [i15]Xiao Zhang, Simon S. Du, Quanquan Gu:
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow. CoRR abs/1803.01233 (2018) - [i14]Jinghui Chen, Quanquan Gu:
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. CoRR abs/1806.06763 (2018) - [i13]Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu:
Learning One-hidden-layer ReLU Networks via Gradient Descent. CoRR abs/1806.07808 (2018) - [i12]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduction for Nonconvex Optimization. CoRR abs/1806.07811 (2018) - [i11]Dongruo Zhou, Pan Xu, Quanquan Gu:
Finding Local Minima via Stochastic Nested Variance Reduction. CoRR abs/1806.08782 (2018) - [i10]Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu:
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. CoRR abs/1808.05671 (2018) - [i9]Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu:
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. CoRR abs/1811.08888 (2018) - [i8]Jinghui Chen, Jinfeng Yi, Quanquan Gu:
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. CoRR abs/1811.10828 (2018) - [i7]Dongruo Zhou, Pan Xu, Quanquan Gu:
Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method. CoRR abs/1811.11989 (2018) - 2017
- [c66]Dezhi Hong, Quanquan Gu, Kamin Whitehouse:
High-dimensional Time Series Clustering via Cross-Predictability. AISTATS 2017: 642-651 - [c65]Pan Xu, Tingting Zhang, Quanquan Gu:
Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent. AISTATS 2017: 923-932 - [c64]Lingxiao Wang, Xiao Zhang, Quanquan Gu:
A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation. AISTATS 2017: 981-990 - [c63]Lu Tian, Quanquan Gu:
Communication-efficient Distributed Sparse Linear Discriminant Analysis. AISTATS 2017: 1178-1187 - [c62]Aditya Chaudhry, Pan Xu, Quanquan Gu:
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference. ICML 2017: 684-693 - [c61]Lingxiao Wang, Quanquan Gu:
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption. ICML 2017: 3617-3626 - [c60]Lingxiao Wang, Xiao Zhang, Quanquan Gu:
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery. ICML 2017: 3712-3721 - [c59]Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu:
High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm. ICML 2017: 4180-4188 - [c58]Jinghui Chen, Quanquan Gu:
Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization. KDD 2017: 757-766 - [c57]Pan Xu, Jian Ma, Quanquan Gu:
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization. NIPS 2017: 1933-1944 - [i6]Pan Xu, Jian Ma, Quanquan Gu:
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations. CoRR abs/1702.08651 (2017) - [i5]Jinghui Chen, Lingxiao Wang, Xiao Zhang, Quanquan Gu:
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption. CoRR abs/1704.06256 (2017) - [i4]Pan Xu, Jinghui Chen, Quanquan Gu:
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. CoRR abs/1707.06618 (2017) - [i3]Yaodong Yu, Difan Zou, Quanquan Gu:
Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently. CoRR abs/1712.03950 (2017) - [i2]Yaodong Yu, Pan Xu, Quanquan Gu:
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima. CoRR abs/1712.06585 (2017) - 2016
- [c56]Lingxiao Wang, Xiang Ren, Quanquan Gu:
Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates. AISTATS 2016: 177-185 - [c55]Renkun Ni, Quanquan Gu:
Optimal Statistical and Computational Rates for One Bit Matrix Completion. AISTATS 2016: 426-434 - [c54]Quanquan Gu, Zhaoran Wang, Han Liu:
Low-Rank and Sparse Structure Pursuit via Alternating Minimization. AISTATS 2016: 600-609 - [c53]Zhaoran Wang, Quanquan Gu, Han Liu:
On the Statistical Limits of Convex Relaxations. ICML 2016: 1368-1377 - [c52]Huan Gui, Jiawei Han, Quanquan Gu:
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation. ICML 2016: 2300-2309 - [c51]Aston Zhang, Quanquan Gu:
Accelerated Stochastic Block Coordinate Descent with Optimal Sampling. KDD 2016: 2035-2044 - [c50]Pan Xu, Quanquan Gu:
Semiparametric Differential Graph Models. NIPS 2016: 1064-1072 - [c49]