Publications - Zebang Shen

Scalable neural incentive design with parameterized mean-field approximation

Nathan Corecco, Batuhan Yardim, Vinzenz Thoma, Zebang Shen, Niao He

Advances in Neural Information Processing Systems, vol. 38, pp. 143143–143201. 2026

[Paper] [arXiv] [Scholar]

Scalable neural incentive design with parameterized mean-field approximation

Nathan Corecco, Batuhan Yardim, Vinzenz Thoma, Zebang Shen, Niao He

Advances in Neural Information Processing Systems, vol. 38, pp. 143143–143201. 2026

[Paper] [arXiv] [Scholar]

Flow density control: Generative optimization beyond entropy-regularized fine-tuning

Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause

Advances in neural information processing systems, vol. 38, pp. 11056–11088. 2026

[Paper] [arXiv] [Scholar]

Flow density control: Generative optimization beyond entropy-regularized fine-tuning

Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause

Advances in neural information processing systems, vol. 38, pp. 11056–11088. 2026

[Paper] [arXiv] [Scholar]

Landing with the score: Riemannian optimization through denoising

Andrey Kharitenko, Zebang Shen, Riccardo De Santi, Niao He, Florian Dorfler

International Conference on Learning Representations, vol. 2026, pp. 129064–129104. 2026

[Paper] [arXiv] [Scholar]

Landing with the score: Riemannian optimization through denoising

Andrey Kharitenko, Zebang Shen, Riccardo De Santi, Niao He, Florian Dorfler

International Conference on Learning Representations, vol. 2026, pp. 129064–129104. 2026

[Paper] [arXiv] [Scholar]

When scores learn geometry: Rate separations under the manifold hypothesis

Xiang Li, Zebang Shen, Ya-Ping Hsieh, Niao He

International Conference on Learning Representations, vol. 2026, pp. 40627–40660. 2026

[Paper] [arXiv] [Scholar]

When scores learn geometry: Rate separations under the manifold hypothesis

Xiang Li, Zebang Shen, Ya-Ping Hsieh, Niao He

International Conference on Learning Representations, vol. 2026, pp. 40627–40660. 2026

[Paper] [arXiv] [Scholar]

A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control

Louis Claeys, Artur Goldman, Zebang Shen, Niao He

International Conference on Learning Representations, vol. 2026. 2026

[Paper] [arXiv] [Scholar]

A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control

Louis Claeys, Artur Goldman, Zebang Shen, Niao He

International Conference on Learning Representations, vol. 2026. 2026

[Paper] [arXiv] [Scholar]

A Hessian-aware stochastic differential equation for modelling SGD

Xiang Li, Zebang Shen, Liang Zhang, Niao He

Mathematical Programming, pp. 1–80, Springer Berlin Heidelberg. 2026

[Paper] [arXiv] [Scholar]

A Hessian-aware stochastic differential equation for modelling SGD

Xiang Li, Zebang Shen, Liang Zhang, Niao He

Mathematical Programming, pp. 1–80, Springer Berlin Heidelberg. 2026

[Paper] [arXiv] [Scholar]

Provable maximum entropy manifold exploration via diffusion models

Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause

International Conference on Machine Learning, vol. 2025. 2025

[Paper] [arXiv] [Scholar]

Provable maximum entropy manifold exploration via diffusion models

Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause

International Conference on Machine Learning, vol. 2025. 2025

[Paper] [arXiv] [Scholar]

Learning to steer markovian agents under model uncertainty

Jiawei Huang, Vinzenz Thoma, Zebang Shen, Heinrich Nax, Niao He

International Conference on Learning Representations, vol. 2025, pp. 84692–84726. 2025

[Paper] [arXiv] [Scholar]

Learning to steer markovian agents under model uncertainty

Jiawei Huang, Vinzenz Thoma, Zebang Shen, Heinrich Nax, Niao He

International Conference on Learning Representations, vol. 2025, pp. 84692–84726. 2025

[Paper] [arXiv] [Scholar]

Efficient projection-free online convex optimization using stochastic gradients

Jiahao Xie, Chao Zhang, Zebang Shen, Hui Qian

Machine Learning, vol. 114, no. 4, pp. 93, Springer US. 2025

[Paper] [Scholar]

Efficient projection-free online convex optimization using stochastic gradients

Jiahao Xie, Chao Zhang, Zebang Shen, Hui Qian

Machine Learning, vol. 114, no. 4, pp. 93, Springer US. 2025

[Paper] [Scholar]

Solving zero-sum Markov games with continuous state via spectral dynamic embedding

Chenhao Zhou, Zebang Shen, Chao Zhang, Hanbin Zhao, Hui Qian

Advances in Neural Information Processing Systems, vol. 37, pp. 68622–68657. 2024

[Paper] [Scholar]

Solving zero-sum Markov games with continuous state via spectral dynamic embedding

Chenhao Zhou, Zebang Shen, Chao Zhang, Hanbin Zhao, Hui Qian

Advances in Neural Information Processing Systems, vol. 37, pp. 68622–68657. 2024

[Paper] [Scholar]

Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning

Zebang Shen, Jiayuan Ye, Anmin Kang, Hamed Hassani, Reza Shokri

The Eleventh International Conference on Learning Representations 2023

[Paper] [arXiv] [Scholar]

Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning

Zebang Shen, Jiayuan Ye, Anmin Kang, Hamed Hassani, Reza Shokri

The Eleventh International Conference on Learning Representations 2023

[Paper] [arXiv] [Scholar]

CDMA: a practical cross-device federated learning algorithm for general minimax problems

Jiahao Xie, Chao Zhang, Zebang Shen, Weijie Liu, Hui Qian

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 9, pp. 10481–10489. 2023

[Paper] [arXiv] [Scholar]

CDMA: a practical cross-device federated learning algorithm for general minimax problems

Jiahao Xie, Chao Zhang, Zebang Shen, Weijie Liu, Hui Qian

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 9, pp. 10481–10489. 2023

[Paper] [arXiv] [Scholar]

Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs

Zebang Shen, Zhenfu Wang

Thirty-seventh Conference on Neural Information Processing Systems 2023

[Paper] [arXiv] [Scholar]

Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs

Zebang Shen, Zhenfu Wang

Thirty-seventh Conference on Neural Information Processing Systems 2023

[Paper] [arXiv] [Scholar]

From One to All: Learning to Match Heterogeneous and Partially Overlapped Graphs

Weijie Liu, Hui Qian, Chao Zhang, Jiahao Xie, Zebang Shen, Nenggan Zheng

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 4, pp. 4109–4119. 2022

[Paper] [arXiv] [Scholar]

From One to All: Learning to Match Heterogeneous and Partially Overlapped Graphs

Weijie Liu, Hui Qian, Chao Zhang, Jiahao Xie, Zebang Shen, Nenggan Zheng

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 4, pp. 4109–4119. 2022

[Paper] [arXiv] [Scholar]

Straggler-Resilient Personalized Federated Learning

Isidoros Tziotis, Zebang Shen, Ramtin Pedarsani, Hamed Hassani, Aryan Mokhtari

Transactions on Machine Learning Research 2022

[Paper] [arXiv] [Scholar]

Straggler-Resilient Personalized Federated Learning

Isidoros Tziotis, Zebang Shen, Ramtin Pedarsani, Hamed Hassani, Aryan Mokhtari

Transactions on Machine Learning Research 2022

[Paper] [arXiv] [Scholar]

Self-Consistency of the Fokker Planck Equation

Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani

Proceedings of Thirty Fifth Conference on Learning Theory, vol. 178, pp. 817–841, PMLR. 2022

[Paper] [arXiv] [Scholar]

Self-Consistency of the Fokker Planck Equation

Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani

Proceedings of Thirty Fifth Conference on Learning Theory, vol. 178, pp. 817–841, PMLR. 2022

[Paper] [arXiv] [Scholar]

Federated functional gradient boosting

Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi

International Conference on Artificial Intelligence and Statistics, pp. 7814–7840, PMLR. 2022

[Paper] [arXiv] [Scholar]

Federated functional gradient boosting

Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi

International Conference on Artificial Intelligence and Statistics, pp. 7814–7840, PMLR. 2022

[Paper] [arXiv] [Scholar]

A hybrid stochastic gradient hamiltonian monte carlo method

Chao Zhang, Zhijian Li, Zebang Shen, Jiahao Xie, Hui Qian

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 12, pp. 10842–10850. 2021

[Paper] [Scholar]

A hybrid stochastic gradient hamiltonian monte carlo method

Chao Zhang, Zhijian Li, Zebang Shen, Jiahao Xie, Hui Qian

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 12, pp. 10842–10850. 2021

[Paper] [Scholar]

An agnostic approach to federated learning with class imbalance

Zebang Shen, Juan Cervino, Hamed Hassani, Alejandro Ribeiro

International Conference on Learning Representations 2021

[Paper] [Scholar]

An agnostic approach to federated learning with class imbalance

Zebang Shen, Juan Cervino, Hamed Hassani, Alejandro Ribeiro

International Conference on Learning Representations 2021

[Paper] [Scholar]

Sinkhorn barycenter via functional gradient descent

Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani

Thirty-fifth Conference on Neural Information Processing Systems 2020

[Paper] [arXiv] [Scholar]

Sinkhorn barycenter via functional gradient descent

Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani

Thirty-fifth Conference on Neural Information Processing Systems 2020

[Paper] [arXiv] [Scholar]

One sample stochastic frank-wolfe

Mingrui Zhang, Zebang Shen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi

International Conference on Artificial Intelligence and Statistics, pp. 4012–4023, PMLR. 2020

[Paper] [arXiv] [Scholar]

One sample stochastic frank-wolfe

Mingrui Zhang, Zebang Shen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi

International Conference on Artificial Intelligence and Statistics, pp. 4012–4023, PMLR. 2020

[Paper] [arXiv] [Scholar]

Aggregated Gradient Langevin Dynamics

Chao Zhang, Jiahao Xie, Zebang Shen, Peilin Zhao, Tengfei Zhou, Hui Qian

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 6746–6753. 2020

[Paper] [arXiv] [Scholar]

Aggregated Gradient Langevin Dynamics

Chao Zhang, Jiahao Xie, Zebang Shen, Peilin Zhao, Tengfei Zhou, Hui Qian

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 6746–6753. 2020

[Paper] [arXiv] [Scholar]

Efficient projection-free online methods with stochastic recursive gradient

Jiahao Xie, Zebang Shen, Chao Zhang, Boyu Wang, Hui Qian

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 6446–6453. 2020

[Paper] [arXiv] [Scholar]

Efficient projection-free online methods with stochastic recursive gradient

Jiahao Xie, Zebang Shen, Chao Zhang, Boyu Wang, Hui Qian

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 6446–6453. 2020

[Paper] [arXiv] [Scholar]

Stochastic conditional gradient++:(non) convex minimization and continuous submodular maximization

Hamed Hassani, Amin Karbasi, Aryan Mokhtari, Zebang Shen

SIAM Journal on Optimization, vol. 30, no. 4, pp. 3315–3344, Society for Industrial and Applied Mathematics. 2020

[Paper] [arXiv] [Scholar]

Stochastic conditional gradient++:(non) convex minimization and continuous submodular maximization

Hamed Hassani, Amin Karbasi, Aryan Mokhtari, Zebang Shen

SIAM Journal on Optimization, vol. 30, no. 4, pp. 3315–3344, Society for Industrial and Applied Mathematics. 2020

[Paper] [arXiv] [Scholar]

Sinkhorn natural gradient for generative models

Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani

(Spotlight) Advances in Neural Information Processing Systems, vol. 33, pp. 1646–1656. 2020

[Paper] [arXiv] [Scholar]

Sinkhorn natural gradient for generative models

Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani

(Spotlight) Advances in Neural Information Processing Systems, vol. 33, pp. 1646–1656. 2020

[Paper] [arXiv] [Scholar]

Accelerating Stratified Sampling SGD by Reconstructing Strata.

Weijie Liu, Hui Qian, Chao Zhang, Zebang Shen, Jiahao Xie, Nenggan Zheng

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 2725–2731. 2020

[Paper] [Scholar]

Accelerating Stratified Sampling SGD by Reconstructing Strata.

Weijie Liu, Hui Qian, Chao Zhang, Zebang Shen, Jiahao Xie, Nenggan Zheng

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 2725–2731. 2020

[Paper] [Scholar]

Stochastic continuous greedy++: When upper and lower bounds match

Amin Karbasi, Hamed Hassani, Aryan Mokhtari, Zebang Shen

Advances in Neural Information Processing Systems 32 (NeurIPS 2019) 2019

[Paper] [arXiv] [Scholar]

Stochastic continuous greedy++: When upper and lower bounds match

Amin Karbasi, Hamed Hassani, Aryan Mokhtari, Zebang Shen

Advances in Neural Information Processing Systems 32 (NeurIPS 2019) 2019

[Paper] [arXiv] [Scholar]

Hessian aided policy gradient

Zebang Shen, Hamed Hassani, Chao Mi, Hui Qian, Alejandro Ribeiro

International conference on machine learning, pp. 5729–5738, PMLR. 2019

[Paper] [Scholar]

Hessian aided policy gradient

Zebang Shen, Hamed Hassani, Chao Mi, Hui Qian, Alejandro Ribeiro

International conference on machine learning, pp. 5729–5738, PMLR. 2019

[Paper] [Scholar]

Multitask metric learning: Theory and algorithm

Boyu Wang, Hejia Zhang, Peng Liu, Zebang Shen, Joelle Pineau

The 22nd International Conference on Artificial Intelligence and Statistics, pp. 3362–3371, PMLR. 2019

[Paper] [Scholar]

Multitask metric learning: Theory and algorithm

Boyu Wang, Hejia Zhang, Peng Liu, Zebang Shen, Joelle Pineau

The 22nd International Conference on Artificial Intelligence and Statistics, pp. 3362–3371, PMLR. 2019

[Paper] [Scholar]

Complexities in projection-free stochastic non-convex minimization

Zebang Shen, Cong Fang, Peilin Zhao, Junzhou Huang, Hui Qian

The 22nd International Conference on Artificial Intelligence and Statistics, pp. 2868–2876, PMLR. 2019

[Paper] [Scholar]

Complexities in projection-free stochastic non-convex minimization

Zebang Shen, Cong Fang, Peilin Zhao, Junzhou Huang, Hui Qian

The 22nd International Conference on Artificial Intelligence and Statistics, pp. 2868–2876, PMLR. 2019

[Paper] [Scholar]

Decentralized gradient tracking for continuous dr-submodular maximization

Jiahao Xie, Chao Zhang, Zebang Shen, Chao Mi, Hui Qian

The 22nd International Conference on Artificial Intelligence and Statistics, pp. 2897–2906, PMLR. 2019

[Paper] [Scholar]

Decentralized gradient tracking for continuous dr-submodular maximization

Jiahao Xie, Chao Zhang, Zebang Shen, Chao Mi, Hui Qian

The 22nd International Conference on Artificial Intelligence and Statistics, pp. 2897–2906, PMLR. 2019

[Paper] [Scholar]

JUMP: a joint predictor for user click and dwell time

Tengfei Zhou, Hui Qian, Zebang Shen, Chao Zhang, Chengwei Wang, Shichen Liu, Wenwu Ou

Proceedings of the 27th International Joint Conference on Artificial Intelligence. AAAI Press, pp. 3704–3710. 2018

[Paper] [Scholar]

JUMP: a joint predictor for user click and dwell time

Tengfei Zhou, Hui Qian, Zebang Shen, Chao Zhang, Chengwei Wang, Shichen Liu, Wenwu Ou

Proceedings of the 27th International Joint Conference on Artificial Intelligence. AAAI Press, pp. 3704–3710. 2018

[Paper] [Scholar]

Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication

Zebang Shen, Aryan Mokhtari, Tengfei Zhou, Peilin Zhao, Hui Qian

Proceedings of the 35th International Conference on Machine Learning, vol. 80, pp. 4624–4633. 2018

[Paper] [arXiv] [Scholar]

Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication

Zebang Shen, Aryan Mokhtari, Tengfei Zhou, Peilin Zhao, Hui Qian

Proceedings of the 35th International Conference on Machine Learning, vol. 80, pp. 4624–4633. 2018

[Paper] [arXiv] [Scholar]

Towards memory-friendly deterministic incremental gradient method

Jiahao Xie, Hui Qian, Zebang Shen, Chao Zhang

International Conference on Artificial Intelligence and Statistics, pp. 1147–1156, PMLR. 2018

[Paper] [Scholar]

Towards memory-friendly deterministic incremental gradient method

Jiahao Xie, Hui Qian, Zebang Shen, Chao Zhang

International Conference on Artificial Intelligence and Statistics, pp. 1147–1156, PMLR. 2018

[Paper] [Scholar]

Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization.

Zebang Shen, Hui Qian, Tongzhou Mu, Chao Zhang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 2715–2721. 2017

[Paper] [arXiv] [Scholar]

Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization.

Zebang Shen, Hui Qian, Tongzhou Mu, Chao Zhang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 2715–2721. 2017

[Paper] [arXiv] [Scholar]

Tensor completion with side information: A riemannian manifold approach.

Tengfei Zhou, Hui Qian, Zebang Shen, Chao Zhang, Congfu Xu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 3539–3545. 2017

[Paper] [arXiv] [Scholar]

Tensor completion with side information: A riemannian manifold approach.

Tengfei Zhou, Hui Qian, Zebang Shen, Chao Zhang, Congfu Xu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 3539–3545. 2017

[Paper] [arXiv] [Scholar]

Adaptive Variance Reducing for Stochastic Gradient Descent.

Zebang Shen, Hui Qian, Tengfei Zhou, Tongzhou Mu

Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 1990–1996. 2016

[Paper] [Scholar]

Adaptive Variance Reducing for Stochastic Gradient Descent.

Zebang Shen, Hui Qian, Tengfei Zhou, Tongzhou Mu

Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 1990–1996. 2016

[Paper] [Scholar]

Fast hybrid algorithm for big matrix recovery

Tengfei Zhou, Hui Qian, Zebang Shen, Congfu Xu

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, no. 1. 2016

[Paper] [Scholar]

Fast hybrid algorithm for big matrix recovery

Tengfei Zhou, Hui Qian, Zebang Shen, Congfu Xu

Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, no. 1. 2016

[Paper] [Scholar]

Simple atom selection strategy for greedy matrix completion

Zebang Shen, Hui Qian, Tengfei Zhou, Song Wang

Twenty-Fourth International Joint Conference on Artificial Intelligence 2015

[Paper] [Scholar]

Simple atom selection strategy for greedy matrix completion

Zebang Shen, Hui Qian, Tengfei Zhou, Song Wang

Twenty-Fourth International Joint Conference on Artificial Intelligence 2015

[Paper] [Scholar]

Support Before Frequency in Discrete Diffusion

Adrian Müller, Antoine Gonon, Zebang Shen, Ya-Ping Hsieh, Niao He

arXiv preprint arXiv:2605.13999 2026

[arXiv] [Scholar]

Support Before Frequency in Discrete Diffusion

Adrian Müller, Antoine Gonon, Zebang Shen, Ya-Ping Hsieh, Niao He

arXiv preprint arXiv:2605.13999 2026

[arXiv] [Scholar]

Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets

Saeed Masiha, Zebang Shen, Negar Kiyavash, Niao He

arXiv preprint arXiv:2605.09209 2026

[arXiv] [Scholar]

Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets

Saeed Masiha, Zebang Shen, Negar Kiyavash, Niao He

arXiv preprint arXiv:2605.09209 2026

[arXiv] [Scholar]

On the Connectedness of Sublevel Sets in Invex Optimization

Vinzenz Thoma, Zebang Shen, Niao He

arXiv preprint arXiv:2604.12045 2026

[arXiv] [Scholar]

On the Connectedness of Sublevel Sets in Invex Optimization

Vinzenz Thoma, Zebang Shen, Niao He

arXiv preprint arXiv:2604.12045 2026

[arXiv] [Scholar]

Manifold Generalization Provably Proceeds Memorization in Diffusion Models

Zebang Shen, Ya-Ping Hsieh, Niao He

arXiv preprint arXiv:2603.23792 2026

[arXiv] [Scholar]

Manifold Generalization Provably Proceeds Memorization in Diffusion Models

Zebang Shen, Ya-Ping Hsieh, Niao He

arXiv preprint arXiv:2603.23792 2026

[arXiv] [Scholar]

Minima Selection in Stochastic Optimization: A Long-Time, Small-Stepsize Perspective

Xiang Li, Zebang Shen, Ya-Ping Hsieh, Niao He

Preprint 2026

[Scholar]

Minima Selection in Stochastic Optimization: A Long-Time, Small-Stepsize Perspective

Xiang Li, Zebang Shen, Ya-Ping Hsieh, Niao He

Preprint 2026

[Scholar]

Adjoint-based aerodynamic shape optimization with a manifold constraint learned by diffusion models

Long Chen, Emre Özkaya, Jan Rottmayer, Nicolas R Gauger, Zebang Shen, Yinyu Ye

arXiv preprint arXiv:2507.23443 2025

[arXiv] [Scholar]

Adjoint-based aerodynamic shape optimization with a manifold constraint learned by diffusion models

Long Chen, Emre Özkaya, Jan Rottmayer, Nicolas R Gauger, Zebang Shen, Yinyu Ye

arXiv preprint arXiv:2507.23443 2025

[arXiv] [Scholar]

Superquantile-Gibbs Relaxation for Minima-selection in Bilevel Optimization

Saeed Masiha, Zebang Shen, Negar Kiyavash, Niao He

arXiv preprint arXiv:2505.05991 2025

[arXiv] [Scholar]

Superquantile-Gibbs Relaxation for Minima-selection in Bilevel Optimization

Saeed Masiha, Zebang Shen, Negar Kiyavash, Niao He

arXiv preprint arXiv:2505.05991 2025

[arXiv] [Scholar]

Poincaré Inequality for Local Log-Polyak-Łojasiewicz Measures: Non-Asymptotic Analysis in the Low-Temperature Regime

Yun Gong, Niao He, Zebang Shen

arXiv preprint arXiv:2501.00429 2024

[arXiv] [Scholar]

Poincaré Inequality for Local Log-Polyak-Łojasiewicz Measures: Non-Asymptotic Analysis in the Low-Temperature Regime

Yun Gong, Niao He, Zebang Shen

arXiv preprint arXiv:2501.00429 2024

[arXiv] [Scholar]

A federated learning framework for nonconvex-pl minimax problems

Jiahao Xie, Chao Zhang, Yunsong Zhang, Zebang Shen, Hui Qian

arXiv preprint arXiv:2105.14216 2021

[arXiv] [Scholar]

A federated learning framework for nonconvex-pl minimax problems

Jiahao Xie, Chao Zhang, Yunsong Zhang, Zebang Shen, Hui Qian

arXiv preprint arXiv:2105.14216 2021

[arXiv] [Scholar]

Partial gromov-wasserstein learning for partial graph matching

Weijie Liu, Chao Zhang, Jiahao Xie, Zebang Shen, Hui Qian, Nenggan Zheng

arXiv preprint arXiv:2012.01252 2020

[arXiv] [Scholar]

Partial gromov-wasserstein learning for partial graph matching

Weijie Liu, Chao Zhang, Jiahao Xie, Zebang Shen, Hui Qian, Nenggan Zheng

arXiv preprint arXiv:2012.01252 2020

[arXiv] [Scholar]

Safe learning under uncertain objectives and constraints

Mohammad Fereydounian, Zebang Shen, Aryan Mokhtari, Amin Karbasi, Hamed Hassani

arXiv preprint arXiv:2006.13326 2020

[arXiv] [Scholar]

Safe learning under uncertain objectives and constraints

Mohammad Fereydounian, Zebang Shen, Aryan Mokhtari, Amin Karbasi, Hamed Hassani

arXiv preprint arXiv:2006.13326 2020

[arXiv] [Scholar]

A decentralized proximal point-type method for saddle point problems

Weijie Liu, Aryan Mokhtari, Asuman Ozdaglar, Sarath Pattathil, Zebang Shen, Nenggan Zheng

arXiv preprint arXiv:1910.14380 2019

[arXiv] [Scholar]

A decentralized proximal point-type method for saddle point problems

Weijie Liu, Aryan Mokhtari, Asuman Ozdaglar, Sarath Pattathil, Zebang Shen, Nenggan Zheng

arXiv preprint arXiv:1910.14380 2019

[arXiv] [Scholar]

A stochastic trust region method for non-convex minimization

Zebang Shen, Pan Zhou, Cong Fang, Alejandro Ribeiro

arXiv preprint arXiv:1903.01540 2019

[arXiv] [Scholar]

A stochastic trust region method for non-convex minimization

Zebang Shen, Pan Zhou, Cong Fang, Alejandro Ribeiro

arXiv preprint arXiv:1903.01540 2019

[arXiv] [Scholar]

Accelerated stochastic ADMM with variance reduction

Chao Zhang, Zebang Shen, Hui Qian, Tengfei Zhou, Jianya Zhou, Jianying Zhou

arXiv preprint arXiv:1611.04074 2016

[arXiv] [Scholar]

Accelerated stochastic ADMM with variance reduction

Chao Zhang, Zebang Shen, Hui Qian, Tengfei Zhou, Jianya Zhou, Jianying Zhou

arXiv preprint arXiv:1611.04074 2016

[arXiv] [Scholar]

Kinetic energy plus penalty functions for sparse estimation

Zhihua Zhang, Shibo Zhao, Zebang Shen, Shuchang Zhou

arXiv preprint arXiv:1307.5601 2013

[arXiv] [Scholar]

Kinetic energy plus penalty functions for sparse estimation

Zhihua Zhang, Shibo Zhao, Zebang Shen, Shuchang Zhou

arXiv preprint arXiv:1307.5601 2013

[arXiv] [Scholar]