Luo Luo

CoRR, 2024

Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates.

[BibT_eX]

[DOI]

Zhuanghua Liu

Bryan Kian Hsiang Low

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Faster Stochastic Algorithms for Minimax Optimization under Polyak-Łojasiewicz Conditions.

[BibT_eX]

[DOI]

Boyuan Yao

CoRR, 2023

Accelerating Inexact HyperGradient Descent for Bilevel Optimization.

[BibT_eX]

[DOI]

CoRR, 2023

Block Broyden's Methods for Solving Nonlinear Equations.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Communication Efficient Distributed Newton Method with Fast Convergence Rates.

[BibT_eX]

[DOI]

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization.

[BibT_eX]

[DOI]

Jing Xu

Proceedings of the International Conference on Machine Learning, 2023

2022

A Simple and Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization.

[BibT_eX]

[DOI]

CoRR, 2022

An Optimal Stochastic Algorithm for Decentralized Nonconvex Finite-sum Optimization.

[BibT_eX]

[DOI]

CoRR, 2022

Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization.

[BibT_eX]

[DOI]

CoRR, 2022

Decentralized Stochastic Variance Reduced Extragradient Method.

[BibT_eX]

[DOI]

CoRR, 2022

Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization.

[BibT_eX]

[DOI]

Yujun Li

Cheng Chen

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Quasi-Newton Methods for Saddle Point Problems.

[BibT_eX]

[DOI]

Chengchang Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Faster Stochastic Algorithms for Minimax Optimization under Polyak-{\L}ojasiewicz Condition.

[BibT_eX]

[DOI]

Boyuan Yao

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Partial-Quasi-Newton Methods: Efficient Algorithms for Minimax Optimization Problems with Unbalanced Dimensionality.

[BibT_eX]

[DOI]

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

Accelerated Proximal Subsampled Newton Method.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2021

Approximate Newton Methods.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

Quasi-Newton Methods for Saddle Point Problems and Beyond.

[BibT_eX]

[DOI]

Chengchang Liu

CoRR, 2021

Finding Second-Order Stationary Point for Nonconvex-Strongly-Concave Minimax Problem.

[BibT_eX]

[DOI]

Cheng Chen

CoRR, 2021

Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization.

[BibT_eX]

[DOI]

CoRR, 2021

Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Nesterov's Acceleration for Approximate Newton.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2020

Multi-consensus Decentralized Accelerated Gradient Descent.

[BibT_eX]

[DOI]

CoRR, 2020

Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems.

[BibT_eX]

[DOI]

Tong Zhang

CoRR, 2020

Decentralized Accelerated Proximal Gradient Descent.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient Projection-free Algorithms for Saddle Point Problems.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient and Robust High-Dimensional Linear Contextual Bandits.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Fast stochastic second-order method logarithmic in condition number.

[BibT_eX]

[DOI]

Pattern Recognit., 2019

Robust Frequent Directions with Application in Online Learning.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2019

A Stochastic Proximal Point Algorithm for Saddle-Point Problems.

[BibT_eX]

[DOI]

CoRR, 2019

A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization.

[BibT_eX]

[DOI]

Guangzeng Xie

CoRR, 2019

2018

Sketched Follow-The-Regularized-Leader for Online Factorization Machine.

[BibT_eX]

[DOI]

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2017

A Unifying Framework for Convergence Analysis of Approximate Newton Methods.

[BibT_eX]

[DOI]

CoRR, 2017

Online Learning Via Regularized Frequent Directions.

[BibT_eX]

[DOI]

CoRR, 2017

Approximate Newton Methods and Their Local Convergence.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features.

[BibT_eX]

[DOI]

Zihao Chen

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions.

[BibT_eX]

[DOI]

Shusen Wang

J. Mach. Learn. Res., 2016

Revisiting Sub-sampled Newton Methods.

[BibT_eX]

[DOI]

CoRR, 2016

Variance-Reduced Second-Order Methods.

[BibT_eX]

[DOI]

CoRR, 2016

Quasi-Newton Hamiltonian Monte Carlo.

[BibT_eX]

[DOI]

Tianfan Fu

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA.

[BibT_eX]

[DOI]

Qiaomin Ye

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

2015

Support Matrix Machines.

[BibT_eX]

[DOI]

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

The Modified Nystrom Method: Theories, Algorithms, and Extension.

[BibT_eX]

[DOI]

Shusen Wang

CoRR, 2014

2013

Robust crowdsourced learning.

[BibT_eX]

[DOI]

Zhiquan Liu

Wu-Jun Li

Proceedings of the 2013 IEEE International Conference on Big Data (IEEE BigData 2013), 2013

2008

Will they stay or will they go?

[BibT_eX]

[DOI]

Joanne McGrath Cohoon

Zhen Wu