Lingjiong Zhu

According to our database, Lingjiong Zhu authored at least 54 papers between 2013 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Stochastic Transition-Map Distillation for Fast Probabilistic Inference.

George Rapakoulias, Peter Garud, Lingjiong Zhu, Panagiotis Tsiotras

CoRR, May, 2026

Decentralized Proximal Stochastic Gradient Langevin Dynamics.

Mohammad Rafiqul Islam, Lingjiong Zhu

CoRR, May, 2026

Duality and transform analysis for non-decreasing functionals of stochastic processes and their applications.

Zhenyu Cui, Chihoon Lee, Yanchu Liu, Lingjiong Zhu

J. Appl. Probab., 2026

2025

Rényi Differential Privacy for Heavy-Tailed SDEs via Fractional Poincaré Inequalities.

Benjamin Dupuis, Mert Gürbüzbalaban, Umut Simsekli, Jian Wang, Sinan Yildirim, Lingjiong Zhu

CoRR, November, 2025

DIGing-SGLD: Decentralized and Scalable Langevin Sampling over Time-Varying Networks.

Waheed U. Bajwa, Mert Gürbüzbalaban, Mustafa Ali Kutbay, Lingjiong Zhu, Muhammad Zulqarnain

CoRR, November, 2025

Anchored Langevin Algorithms.

Mert Gürbüzbalaban, Hoang M. Nguyen, Xicheng Zhang, Lingjiong Zhu

CoRR, September, 2025

Regime-Switching Langevin Monte Carlo Algorithms.

Xiaoyu Wang, Yingli Wang, Lingjiong Zhu

CoRR, September, 2025

High-Order Langevin Monte Carlo Algorithms.

Thanh Dang, Mert Gürbüzbalaban, Mohammad Rafiqul Islam, Nian Yao, Lingjiong Zhu

CoRR, August, 2025

Accelerating Constrained Sampling: A Large Deviations Approach.

Yingli Wang, Changwei Tu, Xiaoyu Wang, Lingjiong Zhu

CoRR, June, 2025

BRIDLE: Generalized Self-supervised Learning with Quantization.

Hoang M. Nguyen, Satya Narayan Shukla, Qiang Zhang, Hanchao Yu, Sreya Dutta Roy, Tai-Peng Tian, Lingjiong Zhu, Yuchen Liu

CoRR, February, 2025

Algorithmic Stability of Stochastic Gradient Descent with Momentum under Heavy-Tailed Noise.

Thanh Dang, Melih Barsbey, A K. M. Rokonuzzaman Sonet, Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu

CoRR, February, 2025

Non-Reversible Langevin Algorithms for Constrained Sampling.

Hengrong Du, Qi Feng, Changwei Tu, Xiaoyu Wang, Lingjiong Zhu

CoRR, January, 2025

Asian Options for Local-Stochastic Volatility Models in the Short-Maturity Regime.

Dan Pirjol, Lingjiong Zhu

SIAM J. Financial Math., 2025

VIX options in the SABR model.

Dan Pirjol, Lingjiong Zhu

Oper. Res. Lett., 2025

Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models.

Xuefeng Gao, Hoang M. Nguyen, Lingjiong Zhu

J. Mach. Learn. Res., 2025

Go With the Flow: Fast Diffusion for Gaussian Mixture Models.

George Rapakoulias, Ali Reza Pedram, Fengjiao Liu, Lingjiong Zhu, Panagiotis Tsiotras

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Intriguing Differences Between Zero-Shot and Systematic Evaluations of Vision-Language Transformer Models.

Shaeke Salman, Montasir Shams, Xiuwen Liu, Lingjiong Zhu

Proceedings of the International Joint Conference on Neural Networks, 2025

Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances.

Xuefeng Gao, Lingjiong Zhu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling.

Mert Gürbüzbalaban, Yuanhan Hu, Lingjiong Zhu

J. Mach. Learn. Res., 2024

Generalized EXTRA stochastic gradient Langevin dynamics.

Mert Gürbüzbalaban, Mohammad Rafiqul Islam, Xiaoyu Wang, Lingjiong Zhu

CoRR, 2024

Differential Privacy of Noisy (S)GD under Heavy-Tailed Perturbations.

Umut Simsekli, Mert Gürbüzbalaban, Sinan Yildirim, Lingjiong Zhu

CoRR, 2024

Intriguing Differences Between Zero-Shot and Systematic Evaluations of Vision-Language Transformer Models.

Shaeke Salman, Md Montasir Bin Shams, Xiuwen Liu, Lingjiong Zhu

CoRR, 2024

A New Approach to Sensitivity Analysis Based on Dirac Delta Family Methods.

Zhenyu Cui, Kailin Ding, Yanchu Liu, Lingjiong Zhu

Proceedings of the Winter Simulation Conference, 2024

2023

Asymptotics for the Laplace transform of the time integral of the geometric Brownian motion.

Dan Pirjol, Lingjiong Zhu

Oper. Res. Lett., May, 2023

Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD than Constant Stepsize.

Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Lingjiong Zhu

Trans. Mach. Learn. Res., 2023

Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD.

Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Lingjiong Zhu

CoRR, 2023

Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent.

Lingjiong Zhu, Mert Gürbüzbalaban, Anant Raj, Umut Simsekli

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

Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions.

Anant Raj, Lingjiong Zhu, Mert Gürbüzbalaban, Umut Simsekli

Proceedings of the International Conference on Machine Learning, 2023

Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares.

Anant Raj, Melih Barsbey, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022

Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks.

Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar, Umut Simsekli, Lingjiong Zhu

J. Mach. Learn. Res., 2022

Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Nonconvex Stochastic Optimization: Nonasymptotic Performance Bounds and Momentum-Based Acceleration.

Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu

Oper. Res., 2022

Penalized Langevin and Hamiltonian Monte Carlo Algorithms for Constrained Sampling.

Mert Gürbüzbalaban, Yuanhan Hu, Lingjiong Zhu

CoRR, 2022

Heavy-Tail Phenomenon in Decentralized SGD.

Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Kun Yuan, Lingjiong Zhu

CoRR, 2022

2021

On the optimal design of the randomized unbiased Monte Carlo estimators.

Zhenyu Cui, Chihoon Lee, Lingjiong Zhu, Yunfan Zhu

Oper. Res. Lett., 2021

Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo.

Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu, Lingjiong Zhu

J. Mach. Learn. Res., 2021

Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance.

Hongjian Wang, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli, Murat A. Erdogdu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms.

Alexander Camuto, George Deligiannidis, Murat A. Erdogdu, Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Heavy-Tail Phenomenon in SGD.

Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu

Proceedings of the 38th International Conference on Machine Learning, 2021

Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections.

Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris C. Holmes, Mert Gürbüzbalaban, Umut Simsekli

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Operational Risk Management: A Stochastic Control Framework with Preventive and Corrective Controls.

Yuqian Xu, Lingjiong Zhu, Michael L. Pinedo

Oper. Res., 2020

On the Variance of Single-Run Unbiased Stochastic Derivative Estimators.

Zhenyu Cui, Michael C. Fu, Jian-Qiang Hu, Yanchu Liu, Yijie Peng, Lingjiong Zhu

INFORMS J. Comput., 2020

Optimal unbiased estimation for expected cumulative discounted cost.

Zhenyu Cui, Michael C. Fu, Yijie Peng, Lingjiong Zhu

Eur. J. Oper. Res., 2020

Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization.

Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu

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

Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise.

Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gürbüzbalaban

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

2019

Asymptotic normality of extensible grid sampling.

Zhijian He, Lingjiong Zhu

Stat. Comput., 2019

Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances.

Bugra Can, Mert Gürbüzbalaban, Lingjiong Zhu

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Functional central limit theorems for stationary Hawkes processes and application to infinite-server queues.

Xuefeng Gao, Lingjiong Zhu

Queueing Syst. Theory Appl., 2018

Explosion in the quasi-Gaussian HJM model.

Dan Pirjol, Lingjiong Zhu

Finance Stochastics, 2018

Breaking Reversibility Accelerates Langevin Dynamics for Global Non-Convex Optimization.

Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu

CoRR, 2018

Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration.

Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu

CoRR, 2018

2017

Small-noise limit of the quasi-Gaussian log-normal HJM model.

Dan Pirjol, Lingjiong Zhu

Oper. Res. Lett., 2017

2016

Short Maturity Asian Options in Local Volatility Models.

Dan Pirjol, Lingjiong Zhu

SIAM J. Financial Math., 2016

2014

Limit Theorems for a Cox-Ingersoll-Ross Process with Hawkes Jumps.

Lingjiong Zhu

J. Appl. Probab., 2014

2013

Central Limit Theorem for Nonlinear Hawkes Processes.

Lingjiong Zhu

J. Appl. Probab., 2013