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2020 – today
- 2024
[j11]Semih Cayci
, Niao He, R. Srikant:
Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation. SIAM J. Optim. 34(3): 2729-2755 (2024)- [j10]Semih Cayci, Niao He, R. Srikant:
Finite-Time Analysis of Natural Actor-Critic for POMDPs. SIAM J. Math. Data Sci. 6(4): 869-896 (2024) - [j9]Semih Cayci, Niao He, R. Srikant:
Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm. Trans. Mach. Learn. Res. 2024 (2024) - [j8]Saber Salehkaleybar, Mohammadsadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran:
Momentum-Based Policy Gradient with Second-Order Information. Trans. Mach. Learn. Res. 2024 (2024) - [c58]Michael J. Curry, Vinzenz Thoma, Darshan Chakrabarti, Stephen McAleer, Christian Kroer, Tuomas Sandholm, Niao He, Sven Seuken:
Automated Design of Affine Maximizer Mechanisms in Dynamic Settings. AAAI 2024: 9626-9635 - [c57]Jiawei Huang, Batuhan Yardim, Niao He:
On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation. AISTATS 2024: 289-297 - [c56]Siqi Zhang, Yifan Hu, Liang Zhang, Niao He:
Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization. AISTATS 2024: 694-702 - [c55]Ilyas Fatkhullin, Niao He:
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence. AISTATS 2024: 3493-3501 - [c54]Philip Jordan, Anas Barakat, Niao He:
Independent Learning in Constrained Markov Potential Games. AISTATS 2024: 4024-4032 - [c53]Florian Hübler, Junchi Yang, Xiang Li, Niao He:
Parameter-Agnostic Optimization under Relaxed Smoothness. AISTATS 2024: 4861-4869 - [c52]Pragnya Alatur, Giorgia Ramponi, Niao He, Andreas Krause:
Provably Learning Nash Policies in Constrained Markov Potential Games. AAMAS 2024: 31-39 - [c51]Batuhan Yardim, Artur Goldman, Niao He:
When is Mean-Field Reinforcement Learning Tractable and Relevant? AAMAS 2024: 2038-2046 - [c50]Jiawei Huang, Niao He, Andreas Krause:
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL. ICML 2024 - [c49]Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He:
Truly No-Regret Learning in Constrained MDPs. ICML 2024 - [c48]Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He:
DPZero: Private Fine-Tuning of Language Models without Backpropagation. ICML 2024 - [i69]Ilyas Fatkhullin, Niao He, Yifan Hu:
Stochastic Optimization under Hidden Convexity. CoRR abs/2401.00108 (2024) - [i68]Jiawei Huang, Niao He, Andreas Krause:
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL. CoRR abs/2402.05724 (2024) - [i67]Batuhan Yardim, Artur Goldman, Niao He:
When is Mean-Field Reinforcement Learning Tractable and Relevant? CoRR abs/2402.05757 (2024) - [i66]Michael J. Curry, Vinzenz Thoma, Darshan Chakrabarti, Stephen McAleer, Christian Kroer, Tuomas Sandholm, Niao He, Sven Seuken:
Automated Design of Affine Maximizer Mechanisms in Dynamic Settings. CoRR abs/2402.08129 (2024) - [i65]Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He:
Truly No-Regret Learning in Constrained MDPs. CoRR abs/2402.15776 (2024) - [i64]Ilyas Fatkhullin, Niao He:
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence. CoRR abs/2402.17722 (2024) - [i63]Philip Jordan, Anas Barakat, Niao He:
Independent Learning in Constrained Markov Potential Games. CoRR abs/2402.17885 (2024) - [i62]Liang Zhang, Niao He, Michael Muehlebach:
Primal Methods for Variational Inequality Problems with Functional Constraints. CoRR abs/2403.12859 (2024) - [i61]Xiang Li, Zebang Shen, Liang Zhang, Niao He:
A Hessian-Aware Stochastic Differential Equation for Modelling SGD. CoRR abs/2405.18373 (2024) - [i60]Yan Huang, Xiang Li, Yipeng Shen, Niao He, Jinming Xu:
Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes. CoRR abs/2406.02939 (2024) - [i59]Jiawei Huang, Vinzenz Thoma, Zebang Shen, Heinrich H. Nax, Niao He:
Learning to Steer Markovian Agents under Model Uncertainty. CoRR abs/2407.10207 (2024) - [i58]Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran:
Complexity of Minimizing Projected-Gradient-Dominated Functions with Stochastic First-order Oracles. CoRR abs/2408.01839 (2024) - [i57]Pragnya Alatur, Anas Barakat, Niao He:
Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players. CoRR abs/2408.08075 (2024) - [i56]Yifan Hu, Jie Wang, Xin Chen, Niao He:
Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles. CoRR abs/2408.11084 (2024) - [i55]Batuhan Yardim, Niao He:
Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning. CoRR abs/2408.15173 (2024) - 2023
- [j7]Donghwan Lee, Jianghai Hu, Niao He:
A Discrete-Time Switching System Analysis of Q-Learning. SIAM J. Control. Optim. 61(3): 1861-1880 (2023) - [j6]Semih Cayci, Siddhartha Satpathi, Niao He, R. Srikant:
Sample Complexity and Overparameterization Bounds for Temporal-Difference Learning With Neural Network Approximation. IEEE Trans. Autom. Control. 68(5): 2891-2905 (2023) - [j5]Jun Song, Niao He, Lijun Ding, Chaoyue Zhao:
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods. Trans. Mach. Learn. Res. 2023 (2023) - [c47]Kei Ishikawa, Niao He:
Kernel Conditional Moment Constraints for Confounding Robust Inference. AISTATS 2023: 650-674 - [c46]Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai:
Learning to Optimize with Stochastic Dominance Constraints. AISTATS 2023: 8991-9009 - [c45]Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He:
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. CDC 2023: 2602-2609 - [c44]Xiang Li, Junchi Yang, Niao He:
TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization. ICLR 2023 - [c43]Anas Barakat, Ilyas Fatkhullin, Niao He:
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. ICML 2023: 1753-1800 - [c42]Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He:
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. ICML 2023: 9827-9869 - [c41]Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He:
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. ICML 2023: 39722-39754 - [c40]Jiawei Huang, Niao He:
Robust Knowledge Transfer in Tiered Reinforcement Learning. NeurIPS 2023 - [c39]Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Laurière, Matthieu Geist:
On Imitation in Mean-field Games. NeurIPS 2023 - [c38]Junchi Yang, Xiang Li, Ilyas Fatkhullin, Niao He:
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods. NeurIPS 2023 - [c37]Liang Zhang, Junchi Yang, Amin Karbasi, Niao He:
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization. NeurIPS 2023 - [i54]Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He:
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. CoRR abs/2302.01734 (2023) - [i53]Jiawei Huang, Niao He:
Robust Knowledge Transfer in Tiered Reinforcement Learning. CoRR abs/2302.05534 (2023) - [i52]Kei Ishikawa, Niao He:
Kernel Conditional Moment Constraints for Confounding Robust Inference. CoRR abs/2302.13348 (2023) - [i51]Jiawei Huang, Batuhan Yardim, Niao He:
On the Statistical Efficiency of Mean Field Reinforcement Learning with General Function Approximation. CoRR abs/2305.11283 (2023) - [i50]Junchi Yang, Xiang Li, Ilyas Fatkhullin, Niao He:
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods. CoRR abs/2305.12475 (2023) - [i49]Anas Barakat, Ilyas Fatkhullin, Niao He:
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. CoRR abs/2306.01854 (2023) - [i48]Adrian Müller, Pragnya Alatur, Giorgia Ramponi, Niao He:
Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes. CoRR abs/2306.07001 (2023) - [i47]Pragnya Alatur, Giorgia Ramponi, Niao He, Andreas Krause:
Provably Learning Nash Policies in Constrained Markov Potential Games. CoRR abs/2306.07749 (2023) - [i46]Jun Song, Niao He, Lijun Ding, Chaoyue Zhao:
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods. CoRR abs/2306.14133 (2023) - [i45]Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Laurière, Matthieu Geist:
On Imitation in Mean-field Games. CoRR abs/2306.14799 (2023) - [i44]Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He:
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. CoRR abs/2309.04272 (2023) - [i43]Kei Ishikawa, Niao He, Takafumi Kanamori:
A Convex Framework for Confounding Robust Inference. CoRR abs/2309.12450 (2023) - [i42]Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He:
DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization. CoRR abs/2310.09639 (2023) - [i41]Liang Zhang, Junchi Yang, Amin Karbasi, Niao He:
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization. CoRR abs/2310.17759 (2023) - [i40]Florian Hübler, Junchi Yang, Xiang Li, Niao He:
Parameter-Agnostic Optimization under Relaxed Smoothness. CoRR abs/2311.03252 (2023) - [i39]Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser:
Efficiently Escaping Saddle Points for Non-Convex Policy Optimization. CoRR abs/2311.08914 (2023) - [i38]Vinzenz Thoma, Michael J. Curry, Niao He, Sven Seuken:
Learning Best Response Policies in Dynamic Auctions via Deep Reinforcement Learning. CoRR abs/2312.13232 (2023) - 2022
- [c36]Kiran Koshy Thekumparampil, Niao He, Sewoong Oh:
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization. AISTATS 2022: 4281-4308 - [c35]Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He:
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. AISTATS 2022: 5485-5517 - [c34]Ahmet Alacaoglu, Luca Viano, Niao He, Volkan Cevher:
A Natural Actor-Critic Framework for Zero-Sum Markov Games. ICML 2022: 307-366 - [c33]Ilyas Fatkhullin, Jalal Etesami, Niao He, Negar Kiyavash:
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality. NeurIPS 2022 - [c32]Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran:
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions. NeurIPS 2022 - [c31]Junchi Yang, Xiang Li, Niao He:
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization. NeurIPS 2022 - [c30]Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He:
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization. NeurIPS 2022 - [i37]Kiran Koshy Thekumparampil, Niao He, Sewoong Oh:
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization. CoRR abs/2201.07427 (2022) - [i36]Semih Cayci, Niao He, R. Srikant:
Learning to Control Partially Observed Systems with Finite Memory. CoRR abs/2202.09753 (2022) - [i35]Saber Salehkaleybar, Sadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran:
Adaptive Momentum-Based Policy Gradient with Second-Order Information. CoRR abs/2205.08253 (2022) - [i34]Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran:
Stochastic Second-Order Methods Provably Beat SGD For Gradient-Dominated Functions. CoRR abs/2205.12856 (2022) - [i33]Siqi Zhang, Yifan Hu, Liang Zhang, Niao He:
Uniform Convergence and Generalization for Nonconvex Stochastic Minimax Problems. CoRR abs/2205.14278 (2022) - [i32]Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He:
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization. CoRR abs/2206.00363 (2022) - [i31]Junchi Yang, Xiang Li, Niao He:
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization. CoRR abs/2206.00743 (2022) - [i30]Semih Cayci, Niao He, R. Srikant:
Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm. CoRR abs/2206.00833 (2022) - [i29]Xiang Li, Junchi Yang, Niao He:
TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization. CoRR abs/2210.17478 (2022) - [i28]Hanjun Dai, Yuan Xue, Niao He, Bethany Wang, Na Li, Dale Schuurmans, Bo Dai:
Learning to Optimize with Stochastic Dominance Constraints. CoRR abs/2211.07767 (2022) - [i27]Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He:
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. CoRR abs/2212.14449 (2022) - 2021
- [c29]Yifan Hu, Xin Chen, Niao He:
On the Bias-Variance-Cost Tradeoff of Stochastic Optimization. NeurIPS 2021: 22119-22131 - [c28]Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He:
The complexity of nonconvex-strongly-concave minimax optimization. UAI 2021: 482-492 - [i26]Semih Cayci, Siddhartha Satpathi, Niao He, R. Srikant:
Sample Complexity and Overparameterization Bounds for Projection-Free Neural TD Learning. CoRR abs/2103.01391 (2021) - [i25]Donghwan Lee, Niao He, Seungjae Lee, Panagiota Karava, Jianghai Hu:
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction. CoRR abs/2103.07919 (2021) - [i24]Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He:
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization. CoRR abs/2103.15888 (2021) - [i23]Semih Cayci, Niao He, R. Srikant:
Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation. CoRR abs/2106.04096 (2021) - [i22]Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He:
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. CoRR abs/2112.05604 (2021) - 2020
- [j4]Pan Li, Niao He, Olgica Milenkovic:
Quadratic Decomposable Submodular Function Minimization: Theory and Practice. J. Mach. Learn. Res. 21: 106:1-106:49 (2020) - [j3]Yifan Hu, Xin Chen, Niao He:
Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization. SIAM J. Optim. 30(3): 2103-2133 (2020) - [j2]Donghwan Lee, Niao He, Parameswaran Kamalaruban, Volkan Cevher:
Optimization for Reinforcement Learning: From a single agent to cooperative agents. IEEE Signal Process. Mag. 37(3): 123-135 (2020) - [c27]Donghwan Lee, Niao He:
Periodic Q-Learning. L4DC 2020: 582-598 - [c26]Donghwan Lee, Niao He:
A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms. NeurIPS 2020 - [c25]Yifan Hu, Siqi Zhang, Xin Chen, Niao He:
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning. NeurIPS 2020 - [c24]Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant:
The Mean-Squared Error of Double Q-Learning. NeurIPS 2020 - [c23]Junchi Yang, Negar Kiyavash, Niao He:
Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems. NeurIPS 2020 - [c22]Yingxiang Yang, Negar Kiyavash, Le Song, Niao He:
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models. NeurIPS 2020 - [c21]Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He:
A Catalyst Framework for Minimax Optimization. NeurIPS 2020 - [i21]Junchi Yang, Negar Kiyavash, Niao He:
Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems. CoRR abs/2002.09621 (2020) - [i20]Donghwan Lee, Niao He:
Periodic Q-Learning. CoRR abs/2002.09795 (2020) - [i19]Yifan Hu, Siqi Zhang, Xin Chen, Niao He:
Biased Stochastic Gradient Descent for Conditional Stochastic Optimization. CoRR abs/2002.10790 (2020) - [i18]Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant:
Provably-Efficient Double Q-Learning. CoRR abs/2007.05034 (2020)
2010 – 2019
- 2019
- [c20]Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He:
Kernel Exponential Family Estimation via Doubly Dual Embedding. AISTATS 2019: 2321-2330 - [c19]Donghwan Lee, Niao He, Jianghai Hu:
Dynamic Programming for POMDP with Jointly Discrete and Continuous State-Spaces. ACC 2019: 1250-1255 - [c18]Donghwan Lee, Niao He:
Stochastic Primal-Dual Q-Learning Algorithm For Discounted MDPs. ACC 2019: 4897-4902 - [c17]Donghwan Lee, Niao He:
Target-Based Temporal-Difference Learning. ICML 2019: 3713-3722 - [c16]Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans:
Exponential Family Estimation via Adversarial Dynamics Embedding. NeurIPS 2019: 10977-10988 - [c15]Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, Niao He:
Learning Positive Functions with Pseudo Mirror Descent. NeurIPS 2019: 14144-14154 - [c14]Harsh Gupta, Niao He, R. Srikant:
Optimization and Learning Algorithms for Stochastic and Adversarial Power Control. WiOpt 2019: 1-8 - [i17]Pan Li, Niao He, Olgica Milenkovic:
Quadratic Decomposable Submodular Function Minimization: Theory and Practice. CoRR abs/1902.10132 (2019) - [i16]Donghwan Lee, Niao He:
Target-Based Temporal Difference Learning. CoRR abs/1904.10945 (2019) - [i15]Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans:
Exponential Family Estimation via Adversarial Dynamics Embedding. CoRR abs/1904.12083 (2019) - [i14]Donghwan Lee, Niao He, Parameswaran Kamalaruban, Volkan Cevher:
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents. CoRR abs/1912.00498 (2019) - [i13]Donghwan Lee, Niao He:
A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms. CoRR abs/1912.02270 (2019) - 2018
- [c13]Bo Dai, Albert E. Shaw, Niao He, Lihong Li, Le Song:
Boosting the Actor with Dual Critic. ICLR (Poster) 2018 - [c12]Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song:
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation. ICML 2018: 1133-1142 - [c11]Pan Li, Niao He, Olgica Milenkovic:
Quadratic Decomposable Submodular Function Minimization. NeurIPS 2018: 1062-1072 - [c10]Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song:
Coupled Variational Bayes via Optimization Embedding. NeurIPS 2018: 9713-9723 - [c9]Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He:
Predictive Approximate Bayesian Computation via Saddle Points. NeurIPS 2018: 10282-10291 - [i12]Pan Li, Niao He, Olgica Milenkovic:
Quadratic Decomposable Submodular Function Minimization. CoRR abs/1806.09842 (2018) - [i11]Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He:
Kernel Exponential Family Estimation via Doubly Dual Embedding. CoRR abs/1811.02228 (2018) - 2017
- [c8]Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song:
Learning from Conditional Distributions via Dual Embeddings. AISTATS 2017: 1458-1467 - [c7]Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song:
Stochastic Generative Hashing. ICML 2017: 913-922 - [c6]Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash:
Online Learning for Multivariate Hawkes Processes. NIPS 2017: 4937-4946 - [i10]Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song:
Stochastic Generative Hashing. CoRR abs/1701.02815 (2017) - [i9]Bo Dai, Albert E. Shaw, Niao He, Lihong Li, Le Song:
Boosting the Actor with Dual Critic. CoRR abs/1712.10282 (2017) - [i8]Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Jianshu Chen, Le Song:
Smoothed Dual Embedding Control. CoRR abs/1712.10285 (2017) - 2016
- [c5]Bo Dai, Niao He, Hanjun Dai, Le Song:
Provable Bayesian Inference via Particle Mirror Descent. AISTATS 2016: 985-994 - [i7]Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song:
Learning from Conditional Distributions via Dual Kernel Embeddings. CoRR abs/1607.04579 (2016) - [i6]Niao He, Zaïd Harchaoui, Yichen Wang, Le Song:
Fast and Simple Optimization for Poisson Likelihood Models. CoRR abs/1608.01264 (2016) - 2015
- [j1]Niao He, Anatoli B. Juditsky, Arkadi Nemirovski:
Mirror Prox algorithm for multi-term composite minimization and semi-separable problems. Comput. Optim. Appl. 61(2): 275-319 (2015) - [c4]Niao He, Zaïd Harchaoui:
Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization. NIPS 2015: 3411-3419 - [c3]Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song:
Time-Sensitive Recommendation From Recurrent User Activities. NIPS 2015: 3492-3500 - [i5]Bo Dai, Niao He, Hanjun Dai, Le Song:
Scalable Bayesian Inference via Particle Mirror Descent. CoRR abs/1506.03101 (2015) - [i4]Niao He, Zaïd Harchaoui:
Semi-proximal Mirror-Prox for Nonsmooth Composite Minimization. CoRR abs/1507.01476 (2015) - 2014
- [c2]Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina Balcan, Le Song:
Scalable Kernel Methods via Doubly Stochastic Gradients. NIPS 2014: 3041-3049 - [i3]Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina Balcan, Le Song:
Scalable Kernel Methods via Doubly Stochastic Gradients. CoRR abs/1407.5599 (2014) - 2013
- [c1]Hua Ouyang, Niao He, Long Q. Tran, Alexander G. Gray:
Stochastic Alternating Direction Method of Multipliers. ICML (1) 2013: 80-88 - [i2]Niao He, Anatoli B. Juditsky, Arkadi Nemirovski:
Mirror Prox Algorithm for Multi-Term Composite Minimization and Alternating Directions. CoRR abs/1311.1098 (2013) - 2012
- [i1]Hua Ouyang, Niao He, Alexander G. Gray:
Stochastic ADMM for Nonsmooth Optimization. CoRR abs/1211.0632 (2012)
Coauthor Index
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last updated on 2024-10-25 20:15 CEST by the dblp team
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