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The Devil is in the Detail: a Framework for Macroscopic Prediction via Microscopic Models

Yingxiang Yang, Negar Kiyavash, Le Song, and Niao He

NeurIPS, 2020. (Spotlight)

A Catalyst Framework for Minimax Optimization

Junchi Yang, Siqi Zhang, Negar Kiyavash, and Niao He

NeurIPS, 2020.

A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms

Donghwan Lee and Niao He

NeurIPS, 2020.

Provably-Efficient Double Q-Learning

Wentao Weng, Harsh Gupta, Niao He, Lei Ying, and R Srikant

NeurIPS, 2020.

Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems

Junchi Yang, Negar Kiyavash, and Niao He

NeurIPS, 2020.

Biased Stochastic Gradient Descent for Conditional Stochastic Optimization

Yifan Hu, Siqi Zhang, Xin Chen, and Niao He

NeurIPS, 2020.

Periodic Q-Learning

Donghwan Lee and Niao He

Learning for Dynamics and Control (L4DC), 2020.

Quadratic Decomposable Submodular Function Minimization: Theory and Practice

Pan Li, Niao He, Olgica Milenkovic

Journal of Machine Learning Research, 2020

Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization

Yifan Hu, Xin Chen, and Niao He

SIAM Journal on Optimization, 2020.

Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents

Donghwan Lee, Niao He, Parameswaran Kamalaruban, Volkan Cevher

IEEE Signal Processing Magazine, Volume: 37, Issue: 3, May 2020.

Bregman Augmented Lagrangian and Its Acceleration

Shen Yan and Niao He

arXiv preprint arXiv:2002.06315, 2020.

Point Process Estimation with Mirror Prox Algorithms

Niao He, Zaid Harchaoui, Yichen Wang, and Le Song

Applied Mathematics and Optimization, 2019.

Learning Positive Functions with Pseudo Mirror Descent

Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, and Niao He
Neural Information Processing Systems (NeurIPS), 2019. (Spotlight)

Exponential Family Estimation via Adversarial Dynamics Embedding

Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, and Dale Schuurmans
Neural Information Processing Systems (NeurIPS), 2019.

Target-Based Temporal Difference Learning

Donghwan Lee, Niao He
International Conference on Machine Learning (ICML), 2019.

Optimization and Learning Algorithms for Stochastic and Adversarial Power Control

Harsh Gupta, Niao He, and R. Srikant
The 17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2019.

Kernel Exponential Family Estimation via Doubly Dual Embedding

Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He
Artificial Intelligence and Statistics (AISTATS), 2019.

Stochastic Primal-Dual Q-Learning Algorithms for Discounted MDPs

Donghwan Lee, Niao He
American Control Conference (ACC), 2019.

Dynamic Programming for Stochastic Control Systems with Jointly Discrete and Continuous State-Spaces

Donghwan Lee, Niao He, Jianghai Hu
American Control Conference (ACC), 2019.

On the Convergence Rate of Stochastic Mirror Descent for Nonsmooth Nonconvex Optimization

Siqi Zhang, Niao He
Under revision, Journal of Optimization Theory and Applications, 2019.

Coupled Variational Bayes via Optimization Embedding

Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
Neural Information Processing Systems (NIPS), 2018.

Quadratic Decomposable Submodular Function Minimization

Pan Li, Niao He, Olgica Milenkovic
Neural Information Processing Systems (NIPS), 2018.

Predictive Approximate Bayesian Computation via Saddle Points

Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
Neural Information Processing Systems (NIPS), 2018.

SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation

Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song
International Conference on Machine Learning (ICML), 2018.

Boosting The Actor With Dual Critic

Bo Dai, Albert Shaw, Niao He, Lihong Li, and Le Song
International Conference on Learning Representations (ICLR), 2018.

Online Learning for Multivariate Hawkes Processes

Yingxiang Yang, Jalal Etsami, Niao He, and Negar Kiyavash
Neural Information Processing Systems (NIPS), 2017.

Smoothed Dual Embedding Control

Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Jianshu Chen, Le Song
NIPS Deep Reinforcement Learning Symposium, 2017.

Stochastic Generative Hashing

Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song
International Conference on Machine Learning (ICML), 2017.

Learning from Conditional Distributions via Dual Kernel Embeddings

Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song
Artificial Intelligence and Statistics (AISTATS), 2017.

Provable Bayesian Inference via Particle Mirror Descent

Bo Dai, Niao He, Hanjun Dai, and Le Song
Artificial Intelligence and Statistics (AISTATS), 2016.

Saddle Point Techniques in Convex Composite and Error-in-Measurement Optimization

Niao He
Georgia Institute of Technology, November 2015.

Mirror Prox Algorithm for Multi-Term Composite Minimization and Semi-Separable Problems

Niao He, Anatoli Juditsky, and Arkadi Nemirovski
Journal of Computational Optimization and Applications, 61(2), 275-319, 2015.

Semi-proximal Mirror-Prox for Nonsmooth Composite Minimization

Niao He and Zaid Harchaoui
Neural Information Processing Systems (NIPS), 2015.

Time-sensitive Recommendation From Recurrent User Activities

Nan Du, Yichen Wang, Niao He, and Le Song
Neural Information Processing Systems (NIPS), 2015.

Stochastic Semi-Proximal Mirror Prox

Niao He and Zaid Harchaoui
NIPS 8th International Workshop on Optimization for Machine Learning, 2015.

Scalable Kernel Methods via Doubly Stochastic Gradients

Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina Balcan, and Le Song
Neural Information Processing Systems (NIPS), 2014.

Stochastic Alternating Direction Method of Multipliers

Hua Ouyang, Niao He, Long Tran, and Alexander Gray
International Conference on Machine Learning (ICML), 2013.