Lingjiong Zhu

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	すべて	2019 年以来
引用	1913	1463
h 指標	24	23
i10 指標	47	38

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共著者

Mert GürbüzbalabanRutgers University確認したメールアドレス: business.rutgers.edu
Xuefeng GaoThe Chinese University of Hong Kong確認したメールアドレス: se.cuhk.edu.hk
Umut SimsekliINRIA - École Normale Supérieure確認したメールアドレス: inria.fr
Zhenyu CuiAssociate Professor, School of Business, Stevens Institute of Technology確認したメールアドレス: stevens.edu
Yuanhan HuRutgers University確認したメールアドレス: rutgers.edu
Yuqian XuAssistant Professor of Operations Management, Kenan-Flagler Business School, UNC-Chapel Hill確認したメールアドレス: kenan-flagler.unc.edu
behzad mehrdadBritish Petroleum確認したメールアドレス: cims.nyu.edu
Murat A. ErdogduUniversity of Toronto確認したメールアドレス: stanford.edu
Chihoon LeeStevens Institute of Technology確認したメールアドレス: stevens.edu
Angelo MeleJohns Hopkins University - Carey Business School確認したメールアドレス: jhu.edu
Alexander CamutoDPhil, University of Oxford, Alan Turing Institute確認したメールアドレス: st-hughs.ox.ac.uk
Arash FahimFlorida State University確認したメールアドレス: math.fsu.edu
Yijie PengPeking University確認したメールアドレス: pku.edu.cn
Michael C. FuUniversity of Maryland確認したメールアドレス: umd.edu
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMind確認したメールアドレス: stats.ox.ac.uk
Yanchu LiuLingnan (University) College, Sun Yat-sen University確認したメールアドレス: mail.sysu.edu.cn
BUGRA CANRutgers University確認したメールアドレス: scarletmail.rutgers.edu
Hongjian WangCarnegie Mellon University確認したメールアドレス: cmu.edu
Steve TaylorAsso. Tch. Professor of Finance, Stevens Institute of Technology確認したメールアドレス: stevens.edu
Michael PinedoJulius Schlesinger Professor of Operations Management, Stern School of Business, New York University確認したメールアドレス: stern.nyu.edu

フォロー

Lingjiong Zhu

Florida State University

確認したメールアドレス: fsu.edu


タイトル引用回数順公開年順タイトル順	引用先引用先	年
The heavy-tail phenomenon in SGD M Gurbuzbalaban, U Simsekli, L Zhu International Conference on Machine Learning, 3964-3975, 2021	134	2021
Central limit theorem for nonlinear Hawkes processes L Zhu Journal of Applied Probability 50 (3), 760-771, 2013	113	2013
Limit theorems for a Cox-Ingersoll-Ross process with Hawkes jumps L Zhu Journal of Applied Probability 51 (3), 699-712, 2014	87	2014
Large deviations for Markovian nonlinear Hawkes processes L Zhu	85	2015
Functional central limit theorems for stationary Hawkes processes and application to infinite-server queues X Gao, L Zhu Queueing Systems 90, 161-206, 2018	84	2018
Global convergence of stochastic gradient hamiltonian monte carlo for nonconvex stochastic optimization: Nonasymptotic performance bounds and momentum-based acceleration X Gao, M Gürbüzbalaban, L Zhu Operations Research 70 (5), 2931-2947, 2022	82	2022
Ruin probabilities for risk processes with non-stationary arrivals and subexponential claims L Zhu Insurance: Mathematics and Economics 53 (3), 544-550, 2013	81	2013
Nonlinear Hawkes processes L Zhu New York University, 2013	67	2013
Limit theorems for marked Hawkes processes with application to a risk model D Karabash, L Zhu Stochastic Models 31 (3), 433-451, 2015	60	2015
Fractional underdamped langevin dynamics: Retargeting sgd with momentum under heavy-tailed gradient noise U Simsekli, L Zhu, YW Teh, M Gurbuzbalaban International conference on machine learning, 8970-8980, 2020	54	2020
Process-level large deviations for nonlinear Hawkes point processes L Zhu Annales de l'IHP Probabilités et statistiques 50 (3), 845-871, 2014	52	2014
Accelerated linear convergence of stochastic momentum methods in wasserstein distances B Can, M Gurbuzbalaban, L Zhu International Conference on Machine Learning, 891-901, 2019	49	2019
Approximate variational estimation for a model of network formation A Mele, L Zhu Review of Economics and Statistics 105 (1), 113-124, 2023	45*	2023
Moderate deviations for Hawkes processes L Zhu Statistics & Probability Letters 83 (3), 885-890, 2013	45	2013
Convergence rates of stochastic gradient descent under infinite noise variance H Wang, M Gurbuzbalaban, L Zhu, U Simsekli, MA Erdogdu Advances in Neural Information Processing Systems 34, 18866-18877, 2021	44	2021
Large deviations and applications for Markovian Hawkes processes with a large initial intensity X Gao, L Zhu	40	2018
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization X Gao, M Gurbuzbalaban, L Zhu Advances in Neural Information Processing Systems 33, 2020	36*	2020
Limit theorems for Markovian Hawkes processes with a large initial intensity X Gao, L Zhu Stochastic Processes and their Applications 128 (11), 3807-3839, 2018	35	2018
Transform analysis for Hawkes processes with applications in dark pool trading X Gao, X Zhou, L Zhu Quantitative Finance 18 (2), 265-282, 2018	35	2018
Short maturity Asian options in local volatility models D Pirjol, L Zhu SIAM Journal on Financial Mathematics 7 (1), 947-992, 2016	34	2016

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