Central Limit Theorem for Nonlinear Hawkes Processes. - Lingjiong Zhu (J. Appl. Probab., 2013)

Limit Theorems for a Cox-Ingersoll-Ross Process with Hawkes Jumps. - Lingjiong Zhu (J. Appl. Probab., 2014)

Short Maturity Asian Options in Local Volatility Models. - Dan Pirjol; Lingjiong Zhu (SIAM J. Financial Math., 2016)

Small-noise limit of the quasi-Gaussian log-normal HJM model. - Dan Pirjol; Lingjiong Zhu (Oper. Res. Lett., 2017)

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)

Breaking Reversibility Accelerates Langevin Dynamics for Global Non-Convex Optimization. - Xuefeng Gao; Mert Gürbüzbalaban; Lingjiong Zhu (CoRR, 2018)

Explosion in the quasi-Gaussian HJM model. - Dan Pirjol; Lingjiong Zhu (Finance Stochastics, 2018)

Functional central limit theorems for stationary Hawkes processes and application to infinite-server queues. - Xuefeng Gao; Lingjiong Zhu (Queueing Syst. Theory Appl., 2018)

Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances. - Bugra Can; Mert Gürbüzbalaban; Lingjiong Zhu (ICML, 2019)

Asymptotic normality of extensible grid sampling. - Zhijian He; Lingjiong Zhu (Stat. Comput., 2019)

Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. - Umut Simsekli; Lingjiong Zhu; Yee Whye Teh; Mert Gürbüzbalaban (ICML, 2020)

Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization. - Xuefeng Gao; Mert Gürbüzbalaban; Lingjiong Zhu (NeurIPS, 2020)

Optimal unbiased estimation for expected cumulative discounted cost. - Zhenyu Cui; Michael C. Fu 001; Yijie Peng; Lingjiong Zhu (Eur. J. Oper. Res., 2020)

On the Variance of Single-Run Unbiased Stochastic Derivative Estimators. - Zhenyu Cui; Michael C. Fu 001; Jian-Qiang Hu; Yanchu Liu; Yijie Peng; Lingjiong Zhu (INFORMS J. Comput., 2020)

Operational Risk Management: A Stochastic Control Framework with Preventive and Corrective Controls. - Yuqian Xu; Lingjiong Zhu; Michael L. Pinedo (Oper. Res., 2020)

Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. - Alexander Camuto; Xiaoyu Wang; Lingjiong Zhu; Chris C. Holmes; Mert Gürbüzbalaban; Umut Simsekli (ICML, 2021)

The Heavy-Tail Phenomenon in SGD. - Mert Gürbüzbalaban; Umut Simsekli; Lingjiong Zhu (ICML, 2021)

Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms. - Alexander Camuto; George Deligiannidis; Murat A. Erdogdu; Mert Gürbüzbalaban; Umut Simsekli; Lingjiong Zhu (NeurIPS, 2021)

Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance. - Hongjian Wang; Mert Gürbüzbalaban; Lingjiong Zhu; Umut Simsekli; Murat A. Erdogdu (NeurIPS, 2021)

Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo. - Mert Gürbüzbalaban; Xuefeng Gao; Yuanhan Hu; Lingjiong Zhu (J. Mach. Learn. Res., 2021)

On the optimal design of the randomized unbiased Monte Carlo estimators. - Zhenyu Cui; Chihoon Lee; Lingjiong Zhu; Yunfan Zhu (Oper. Res. Lett., 2021)

Heavy-Tail Phenomenon in Decentralized SGD. - Mert Gürbüzbalaban; Yuanhan Hu; Umut Simsekli; Kun Yuan 001; Lingjiong Zhu (CoRR, 2022)

Penalized Langevin and Hamiltonian Monte Carlo Algorithms for Constrained Sampling. - Mert Gürbüzbalaban; Yuanhan Hu; Lingjiong Zhu (CoRR, 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)

Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks. - Alireza Fallah 001; Mert Gürbüzbalaban; Asuman E. Ozdaglar; Umut Simsekli; Lingjiong Zhu (J. Mach. Learn. Res., 2022)

Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares. - Anant Raj; Melih Barsbey; Mert Gürbüzbalaban; Lingjiong Zhu; Umut Simsekli (ALT, 2023)

Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions. - Anant Raj; Lingjiong Zhu; Mert Gürbüzbalaban; Umut Simsekli (ICML, 2023)

Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent. - Lingjiong Zhu; Mert Gürbüzbalaban; Anant Raj; Umut Simsekli (NeurIPS, 2023)

Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD. - Mert Gürbüzbalaban; Yuanhan Hu; Umut Simsekli; Lingjiong Zhu (CoRR, 2023)

Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models. - Xuefeng Gao; Hoang M. Nguyen; Lingjiong Zhu (CoRR, 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)

Asymptotics for the Laplace transform of the time integral of the geometric Brownian motion. - Dan Pirjol; Lingjiong Zhu (Oper. Res. Lett., 2023)

A New Approach to Sensitivity Analysis Based on Dirac Delta Family Methods. - Zhenyu Cui; Kailin Ding; Yanchu Liu; Lingjiong Zhu (WSC, 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)

Differential Privacy of Noisy (S)GD under Heavy-Tailed Perturbations. - Umut Simsekli; Mert Gürbüzbalaban; Sinan Yildirim; Lingjiong Zhu (CoRR, 2024)

Generalized EXTRA stochastic gradient Langevin dynamics. - Mert Gürbüzbalaban; Mohammad Rafiqul Islam; Xiaoyu Wang; Lingjiong Zhu (CoRR, 2024)

Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances. - Xuefeng Gao; Lingjiong Zhu (AISTATS, 2025)

VIX options in the SABR model. - Dan Pirjol; Lingjiong Zhu (Oper. Res. Lett., 2025)

Asian Options for Local-Stochastic Volatility Models in the Short-Maturity Regime. - Dan Pirjol; Lingjiong Zhu (SIAM J. Financial Math., 2025)

Non-Reversible Langevin Algorithms for Constrained Sampling. - Hengrong Du; Qi Feng; Changwei Tu; Xiaoyu Wang; Lingjiong Zhu (CoRR, 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, 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, 2025)

Accelerating Constrained Sampling: A Large Deviations Approach. - Yingli Wang; Changwei Tu; Xiaoyu Wang; Lingjiong Zhu (CoRR, 2025)

High-Order Langevin Monte Carlo Algorithms. - Thanh Dang; Mert Gürbüzbalaban; Mohammad Rafiqul Islam; Nian Yao; Lingjiong Zhu (CoRR, 2025)

Regime-Switching Langevin Monte Carlo Algorithms. - Xiaoyu Wang; Yingli Wang; Lingjiong Zhu (CoRR, 2025)

Anchored Langevin Algorithms. - Mert Gürbüzbalaban; Hoang M. Nguyen; Xicheng Zhang; Lingjiong Zhu (CoRR, 2025)