Holden Lee

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	Toate	Din 2020
Referințe bibliografice	1260	1178
h-index	16	16
i10-index	21	20

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Jianfeng Lu 鲁剑锋Department of Mathematics, Department of Chemistry and Department of Physics, Duke UniversityAdresă de e-mail confirmată pe math.duke.edu
Andrej RisteskiCarnegie Mellon UniversityAdresă de e-mail confirmată pe andrew.cmu.edu
Rong GeDuke UniversityAdresă de e-mail confirmată pe cs.duke.edu
Cyril ZhangMicrosoft Research NYCAdresă de e-mail confirmată pe microsoft.com
Yi ZhangSenior Researcher at Microsoft Research RedmondAdresă de e-mail confirmată pe microsoft.com
Sanjeev AroraProfessor of Computer Science, Princeton UniversityAdresă de e-mail confirmată pe princeton.edu
Karan SinghCarnegie Mellon UniversityAdresă de e-mail confirmată pe cmu.edu
Elad HazanProfessor at Princeton University and Director Google AI PrincetonAdresă de e-mail confirmată pe princeton.edu
Chirag PabbarajuStanford UniversityAdresă de e-mail confirmată pe stanford.edu
Tengyu MAStanford UniversityAdresă de e-mail confirmată pe stanford.edu
Zhiyuan LiAssistant Professor, Toyota Technological Institute at ChicagoAdresă de e-mail confirmată pe ttic.edu
Wei HuAssistant Professor of Computer Science and Engineering, University of MichiganAdresă de e-mail confirmată pe umich.edu
Xiang WangMeta, Duke UniversityAdresă de e-mail confirmată pe meta.com
Rohith KuditipudiStanford UniversityAdresă de e-mail confirmată pe stanford.edu
Udaya GhaiAmazon, NYCAdresă de e-mail confirmată pe amazon.com
Oren MangoubiAssistant Professor, Mathematical Sciences and Data Science, Worcester Polytechnic Institute (WPI)Adresă de e-mail confirmată pe wpi.edu
Tyler HelmuthDurham UniversityAdresă de e-mail confirmată pe durham.ac.uk
Will PerkinsGeorgia Institute of TechnologyAdresă de e-mail confirmată pe willperkins.org

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Holden Lee

Assistant Professor of Applied Mathematics and Statistics, Johns Hopkins University

Adresă de e-mail confirmată pe jhu.edu - Pagina de pornire

machine learning probability statistics theoretical computer science


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions H Chen, H Lee, J Lu International Conference on Machine Learning, 4735-4763, 2023	171	2023
Convergence for score-based generative modeling with polynomial complexity H Lee, J Lu, Y Tan Advances in Neural Information Processing Systems 35, 22870--22882, 2022	157	2022
Convergence of score-based generative modeling for general data distributions H Lee, J Lu, Y Tan International Conference on Algorithmic Learning Theory, 946-985, 2023	143	2023
Spectral filtering for general linear dynamical systems E Hazan, H Lee, K Singh, C Zhang, Y Zhang Advances in Neural Information Processing Systems 31, 2018	114	2018
The probability flow ode is provably fast S Chen, S Chewi, H Lee, Y Li, J Lu, A Salim Advances in Neural Information Processing Systems 36, 68552-68575, 2023	105	2023
On the ability of neural nets to express distributions H Lee, R Ge, T Ma, A Risteski, S Arora Conference on Learning Theory 2017., 2017	104	2017
Explaining landscape connectivity of low-cost solutions for multilayer nets R Kuditipudi, X Wang, H Lee, Y Zhang, Z Li, W Hu, R Ge, S Arora Advances in neural information processing systems 32, 2019	99	2019
Beyond log-concavity: Provable guarantees for sampling multi-modal distributions using simulated tempering langevin monte carlo H Lee, A Risteski, R Ge Advances in neural information processing systems 31, 2018	67*	2018
Estimating normalizing constants for log-concave distributions: Algorithms and lower bounds R Ge, H Lee, J Lu Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing …, 2020	29	2020
Simulated tempering langevin monte carlo ii: An improved proof using soft markov chain decomposition R Ge, H Lee, A Risteski arXiv preprint arXiv:1812.00793, 2018	28	2018
Towards provable control for unknown linear dynamical systems S Arora, E Hazan, H Lee, K Singh, C Zhang, Y Zhang	27	2018
Sampling approximately low-rank Ising models: MCMC meets variational methods F Koehler, H Lee, A Risteski Conference on Learning Theory, 4945-4988, 2022	24	2022
No-regret prediction in marginally stable systems U Ghai, H Lee, K Singh, C Zhang, Y Zhang COLT 2020 - The 33rd Annual Conference on Learning Theory, July 9-12, 2020., 2020	24	2020
Learning mixtures of gaussians using diffusion models K Gatmiry, J Kelner, H Lee arXiv preprint arXiv:2404.18869, 2024	21	2024
Improved rates for prediction and identification of partially observed linear dynamical systems H Lee International Conference on Algorithmic Learning Theory, 668-698, 2022	19	2022
Universal approximation for log-concave distributions using well-conditioned normalizing flows H Lee, C Pabbaraju, A Sevekari, A Risteski Advances in Neural Information Processing Systems 34, 12700--12711, 2021	19	2021
Fisher information lower bounds for sampling S Chewi, P Gerber, H Lee, C Lu International Conference on Algorithmic Learning Theory, 375-410, 2023	16	2023
Provable benefits of score matching C Pabbaraju, D Rohatgi, AP Sevekari, H Lee, A Moitra, A Risteski Advances in Neural Information Processing Systems 36, 61306-61326, 2023	13	2023
Pixie: a social chatbot O Adewale, A Beatson, D Buniatyan, J Ge, M Khodak, H Lee, N Prasad, ... Alexa prize proceedings, 2017	13	2017
Robust guarantees for learning an autoregressive filter H Lee, C Zhang Algorithmic Learning Theory, 490-517, 2020	12	2020

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