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ORCIDLingjiong Zhu
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2020 – today
- 2024
[i19]Xuefeng Gao
, Lingjiong Zhu:
Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances. CoRR abs/2401.17958 (2024)- [i18]Shaeke Salman, Md Montasir Bin Shams, Xiuwen Liu, Lingjiong Zhu:
Intriguing Differences Between Zero-Shot and Systematic Evaluations of Vision-Language Transformer Models. CoRR abs/2402.08473 (2024) - [i17]Umut Simsekli, Mert Gürbüzbalaban, Sinan Yildirim, Lingjiong Zhu:
Differential Privacy of Noisy (S)GD under Heavy-Tailed Perturbations. CoRR abs/2403.02051 (2024) - 2023
- [j16]Dan Pirjol, Lingjiong Zhu:
Asymptotics for the Laplace transform of the time integral of the geometric Brownian motion. Oper. Res. Lett. 51(3): 346-352 (2023) - [j15]Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Lingjiong Zhu:
Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD than Constant Stepsize. Trans. Mach. Learn. Res. 2023 (2023) - [c10]Anant Raj, Melih Barsbey, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares. ALT 2023: 1292-1342 - [c9]Anant Raj, Lingjiong Zhu, Mert Gürbüzbalaban, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions. ICML 2023: 28578-28597 - [c8]Lingjiong Zhu, Mert Gürbüzbalaban, Anant Raj, Umut Simsekli:
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent. NeurIPS 2023 - [i16]Anant Raj, Lingjiong Zhu, Mert Gürbüzbalaban, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions. CoRR abs/2301.11885 (2023) - [i15]Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Lingjiong Zhu:
Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD. CoRR abs/2302.05516 (2023) - [i14]Lingjiong Zhu, Mert Gürbüzbalaban, Anant Raj, Umut Simsekli:
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent. CoRR abs/2305.12056 (2023) - [i13]Xuefeng Gao, Hoang M. Nguyen, Lingjiong Zhu:
Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models. CoRR abs/2311.11003 (2023) - 2022
- [j14]Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu:
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Nonconvex Stochastic Optimization: Nonasymptotic Performance Bounds and Momentum-Based Acceleration. Oper. Res. 70(5): 2931-2947 (2022) - [j13]Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar, Umut Simsekli, Lingjiong Zhu:
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks. J. Mach. Learn. Res. 23: 220:1-220:96 (2022) - [i12]Mert Gürbüzbalaban, Yuanhan Hu, Umut Simsekli, Kun Yuan, Lingjiong Zhu:
Heavy-Tail Phenomenon in Decentralized SGD. CoRR abs/2205.06689 (2022) - [i11]Anant Raj, Melih Barsbey, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli:
Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares. CoRR abs/2206.01274 (2022) - [i10]Mert Gürbüzbalaban, Yuanhan Hu, Lingjiong Zhu:
Penalized Langevin and Hamiltonian Monte Carlo Algorithms for Constrained Sampling. CoRR abs/2212.00570 (2022) - 2021
- [j12]Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu, Lingjiong Zhu:
Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo. J. Mach. Learn. Res. 22: 239:1-239:69 (2021) - [j11]Zhenyu Cui, Chihoon Lee, Lingjiong Zhu, Yunfan Zhu:
On the optimal design of the randomized unbiased Monte Carlo estimators. Oper. Res. Lett. 49(4): 477-484 (2021) - [c7]Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris C. Holmes, Mert Gürbüzbalaban, Umut Simsekli:
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. ICML 2021: 1249-1260 - [c6]Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu:
The Heavy-Tail Phenomenon in SGD. ICML 2021: 3964-3975 - [c5]Alexander Camuto, George Deligiannidis, Murat A. Erdogdu, Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu:
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms. NeurIPS 2021: 18774-18788 - [c4]Hongjian Wang, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli, Murat A. Erdogdu:
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance. NeurIPS 2021: 18866-18877 - [i9]Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris C. Holmes, Mert Gürbüzbalaban, Umut Simsekli:
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. CoRR abs/2102.07006 (2021) - [i8]Alexander Camuto, George Deligiannidis, Murat A. Erdogdu, Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu:
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms. CoRR abs/2106.04881 (2021) - 2020
- [j10]Zhenyu Cui, Michael C. Fu, Yijie Peng, Lingjiong Zhu:
Optimal unbiased estimation for expected cumulative discounted cost. Eur. J. Oper. Res. 286(2): 604-618 (2020) - [j9]Zhenyu Cui, Michael C. Fu, Jian-Qiang Hu, Yanchu Liu, Yijie Peng, Lingjiong Zhu:
On the Variance of Single-Run Unbiased Stochastic Derivative Estimators. INFORMS J. Comput. 32(2): 390-407 (2020) - [j8]Yuqian Xu, Lingjiong Zhu, Michael L. Pinedo:
Operational Risk Management: A Stochastic Control Framework with Preventive and Corrective Controls. Oper. Res. 68(6): 1804-1825 (2020) - [c3]Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gürbüzbalaban:
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. ICML 2020: 8970-8980 - [c2]Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu:
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization. NeurIPS 2020 - [i7]Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gürbüzbalaban:
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. CoRR abs/2002.05685 (2020) - [i6]Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu:
The Heavy-Tail Phenomenon in SGD. CoRR abs/2006.04740 (2020) - [i5]Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu, Lingjiong Zhu:
Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo. CoRR abs/2007.00590 (2020)
2010 – 2019
- 2019
- [j7]Zhijian He, Lingjiong Zhu:
Asymptotic normality of extensible grid sampling. Stat. Comput. 29(1): 53-65 (2019) - [c1]Bugra Can, Mert Gürbüzbalaban, Lingjiong Zhu:
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances. ICML 2019: 891-901 - [i4]Bugra Can, Mert Gürbüzbalaban, Lingjiong Zhu:
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances. CoRR abs/1901.07445 (2019) - [i3]Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar, Umut Simsekli, Lingjiong Zhu:
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks. CoRR abs/1910.08701 (2019) - 2018
- [j6]Dan Pirjol, Lingjiong Zhu:
Explosion in the quasi-Gaussian HJM model. Finance Stochastics 22(3): 643-666 (2018) - [j5]Xuefeng Gao, Lingjiong Zhu:
Functional central limit theorems for stationary Hawkes processes and application to infinite-server queues. Queueing Syst. Theory Appl. 90(1-2): 161-206 (2018) - [i2]Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu:
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration. CoRR abs/1809.04618 (2018) - [i1]Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu:
Breaking Reversibility Accelerates Langevin Dynamics for Global Non-Convex Optimization. CoRR abs/1812.07725 (2018) - 2017
- [j4]Dan Pirjol, Lingjiong Zhu:
Small-noise limit of the quasi-Gaussian log-normal HJM model. Oper. Res. Lett. 45(1): 6-11 (2017) - 2016
- [j3]Dan Pirjol, Lingjiong Zhu:
Short Maturity Asian Options in Local Volatility Models. SIAM J. Financial Math. 7(1): 947-992 (2016) - 2014
- [j2]Lingjiong Zhu:
Limit Theorems for a Cox-Ingersoll-Ross Process with Hawkes Jumps. J. Appl. Probab. 51(3): 699-712 (2014) - 2013
- [j1]Lingjiong Zhu:
Central Limit Theorem for Nonlinear Hawkes Processes. J. Appl. Probab. 50(3): 760-771 (2013)
Coauthor Index
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