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2020 – today
- 2022
[c159]Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu, Jingjing Liu:
Efficient Robust Training via Backward Smoothing. AAAI 2022: 6222-6230- [c158]Yue Wu, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. AISTATS 2022: 3883-3913 - [c157]Jiafan He, Dongruo Zhou, Quanquan Gu:
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. AISTATS 2022: 4259-4280 - [c156]Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu:
Self-training Converts Weak Learners to Strong Learners in Mixture Models. AISTATS 2022: 8003-8021 - [c155]Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu:
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. AISTATS 2022: 11014-11036 - [c154]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. ALT 2022: 176-204 - [c153]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games. ALT 2022: 227-261 - [c152]Zhe Wu, Aisha Alnajdi, Quanquan Gu, Panagiotis D. Christofides:
Machine-Learning-based Predictive Control of Nonlinear Processes with Uncertainty. ACC 2022: 2810-2816 - [c151]Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu:
Neural Contextual Bandits with Deep Representation and Shallow Exploration. ICLR 2022 - [c150]Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang:
Learning Neural Contextual Bandits through Perturbed Rewards. ICLR 2022 - [c149]Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh:
On the Convergence of Certified Robust Training with Interval Bound Propagation. ICLR 2022 - [c148]Yuanzhou Chen, Jiafan He, Quanquan Gu:
On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs. ICML 2022: 3149-3183 - [c147]Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu:
Learning Stochastic Shortest Path with Linear Function Approximation. ICML 2022: 15584-15629 - [c146]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. ICML 2022: 24280-24314 - [c145]Dongruo Zhou, Quanquan Gu:
Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization. ICML 2022: 27143-27158 - [i102]Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang:
Learning Contextual Bandits Through Perturbed Rewards. CoRR abs/2201.09910 (2022) - [i101]Yuan Cao, Zixiang Chen, Mikhail Belkin, Quanquan Gu:
Benign Overfitting in Two-layer Convolutional Neural Networks. CoRR abs/2202.06526 (2022) - [i100]Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu:
Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds. CoRR abs/2202.13603 (2022) - [i99]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. CoRR abs/2203.03159 (2022) - [i98]Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh:
On the Convergence of Certified Robust Training with Interval Bound Propagation. CoRR abs/2203.08961 (2022) - [i97]Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. CoRR abs/2205.06811 (2022) - [i96]Dongruo Zhou, Quanquan Gu:
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs. CoRR abs/2205.11507 (2022) - [i95]Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu:
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. CoRR abs/2207.03106 (2022) - [i94]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. CoRR abs/2208.01857 (2022) - [i93]Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li:
Towards Understanding Mixture of Experts in Deep Learning. CoRR abs/2208.02813 (2022) - [i92]Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. CoRR abs/2208.05363 (2022) - [i91]Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael I. Jordan:
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. CoRR abs/2209.15634 (2022) - [i90]Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang:
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes. CoRR abs/2212.05949 (2022) - [i89]Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. CoRR abs/2212.06132 (2022) - 2021
- [j9]Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, David Evans:
Revisiting Membership Inference Under Realistic Assumptions. Proc. Priv. Enhancing Technol. 2021(2): 348-368 (2021) - [j8]Bao Wang
, Difan Zou, Quanquan Gu, Stanley J. Osher:
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. SIAM J. Sci. Comput. 43(1): A26-A53 (2021) - [c144]Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu:
Double Explore-then-Commit: Asymptotic Optimality and Beyond. COLT 2021: 2584-2633 - [c143]Dongruo Zhou, Quanquan Gu, Csaba Szepesvári:
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. COLT 2021: 4532-4576 - [c142]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. COLT 2021: 4633-4635 - [c141]Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu:
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? ICLR 2021 - [c140]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu:
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. ICLR 2021 - [c139]Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu:
Neural Thompson Sampling. ICLR 2021 - [c138]Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. ICML 2021: 3417-3426 - [c137]Spencer Frei, Yuan Cao, Quanquan Gu:
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. ICML 2021: 3427-3438 - [c136]Jiafan He, Dongruo Zhou, Quanquan Gu:
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. ICML 2021: 4171-4180 - [c135]Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, Quanquan Gu:
Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. ICML 2021: 5065-5073 - [c134]Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu:
MOTS: Minimax Optimal Thompson Sampling. ICML 2021: 5074-5083 - [c133]Dongruo Zhou, Jiafan He, Quanquan Gu:
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. ICML 2021: 12793-12802 - [c132]Difan Zou, Spencer Frei, Quanquan Gu:
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. ICML 2021: 13002-13011 - [c131]Difan Zou, Quanquan Gu:
On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients. ICML 2021: 13012-13022 - [c130]Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu:
Towards Understanding the Spectral Bias of Deep Learning. IJCAI 2021: 2205-2211 - [c129]Lingxiao Wang, Kevin Huang, Tengyu Ma, Quanquan Gu, Jing Huang:
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks. NAACL-HLT 2021: 2609-2615 - [c128]Weitong Zhang, Dongruo Zhou, Quanquan Gu:
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. NeurIPS 2021: 1582-1593 - [c127]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade:
The Benefits of Implicit Regularization from SGD in Least Squares Problems. NeurIPS 2021: 5456-5468 - [c126]Hanxun Huang, Yisen Wang, Sarah M. Erfani, Quanquan Gu, James Bailey, Xingjun Ma:
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks. NeurIPS 2021: 5545-5559 - [c125]Boxi Wu, Jinghui Chen, Deng Cai, Xiaofei He, Quanquan Gu:
Do Wider Neural Networks Really Help Adversarial Robustness? NeurIPS 2021: 7054-7067 - [c124]Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Variance-Aware Off-Policy Evaluation with Linear Function Approximation. NeurIPS 2021: 7598-7610 - [c123]Spencer Frei, Quanquan Gu:
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent. NeurIPS 2021: 7937-7949 - [c122]Yuan Cao, Quanquan Gu, Mikhail Belkin:
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. NeurIPS 2021: 8407-8418 - [c121]Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert Nowak:
Pure Exploration in Kernel and Neural Bandits. NeurIPS 2021: 11618-11630 - [c120]Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints. NeurIPS 2021: 13524-13536 - [c119]Jiafan He, Dongruo Zhou, Quanquan Gu:
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. NeurIPS 2021: 14188-14199 - [c118]Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs. NeurIPS 2021: 22288-22300 - [c117]Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu:
Iterative Teacher-Aware Learning. NeurIPS 2021: 29231-29245 - [c116]Difan Zou, Pan Xu, Quanquan Gu:
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. UAI 2021: 1152-1162 - [i88]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) - [i87]Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints. CoRR abs/2101.02195 (2021) - [i86]Yue Wu, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. CoRR abs/2102.07301 (2021) - [i85]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation. CoRR abs/2102.07404 (2021) - [i84]Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation. CoRR abs/2102.08940 (2021) - [i83]Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab S. Mirrokni, Dongruo Zhou:
Batched Neural Bandits. CoRR abs/2102.13028 (2021) - [i82]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. CoRR abs/2103.12692 (2021) - [i81]Difan Zou, Spencer Frei, Quanquan Gu:
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. CoRR abs/2104.09437 (2021) - [i80]Yuan Cao, Quanquan Gu, Mikhail Belkin:
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. CoRR abs/2104.13628 (2021) - [i79]Jiafan He, Dongruo Zhou, Quanquan Gu:
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. CoRR abs/2106.11612 (2021) - [i78]Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu:
Provably Efficient Representation Learning in Low-rank Markov Decision Processes. CoRR abs/2106.11935 (2021) - [i77]Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Variance-Aware Off-Policy Evaluation with Linear Function Approximation. CoRR abs/2106.11960 (2021) - [i76]Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert D. Nowak:
Pure Exploration in Kernel and Neural Bandits. CoRR abs/2106.12034 (2021) - [i75]Spencer Frei, Quanquan Gu:
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent. CoRR abs/2106.13792 (2021) - [i74]Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu:
Self-training Converts Weak Learners to Strong Learners in Mixture Models. CoRR abs/2106.13805 (2021) - [i73]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade:
The Benefits of Implicit Regularization from SGD in Least Squares Problems. CoRR abs/2108.04552 (2021) - [i72]Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu:
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. CoRR abs/2108.11371 (2021) - [i71]Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu:
Iterative Teacher-Aware Learning. CoRR abs/2110.00137 (2021) - [i70]Hanxun Huang, Yisen Wang, Sarah Monazam Erfani, Quanquan Gu, James Bailey, Xingjun Ma:
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks. CoRR abs/2110.03825 (2021) - [i69]Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu:
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. CoRR abs/2110.04136 (2021) - [i68]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. CoRR abs/2110.06198 (2021) - [i67]Weitong Zhang, Dongruo Zhou, Quanquan Gu:
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. CoRR abs/2110.06394 (2021) - [i66]Xiaoxia Wu, Lingxiao Wang, Irina Cristali, Quanquan Gu, Rebecca Willett:
Adaptive Differentially Private Empirical Risk Minimization. CoRR abs/2110.07435 (2021) - [i65]Chonghua Liao, Jiafan He, Quanquan Gu:
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes. CoRR abs/2110.10133 (2021) - [i64]Heyang Zhao, Dongruo Zhou, Quanquan Gu:
Linear Contextual Bandits with Adversarial Corruptions. CoRR abs/2110.12615 (2021) - [i63]Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu:
Learning Stochastic Shortest Path with Linear Function Approximation. CoRR abs/2110.12727 (2021) - [i62]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. CoRR abs/2110.13144 (2021) - [i61]Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu:
On the Convergence and Robustness of Adversarial Training. CoRR abs/2112.08304 (2021) - [i60]Jinghui Chen, Yuan Cao, Quanquan Gu:
Benign Overfitting in Adversarially Robust Linear Classification. CoRR abs/2112.15250 (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]