Követés
Holden Lee
Holden Lee
Assistant Professor of Applied Mathematics and Statistics, Johns Hopkins University
E-mail megerősítve itt: jhu.edu - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
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
1722023
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
1572022
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
1432023
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
1142018
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
1052023
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
1042017
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
1002019
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
292020
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
292018
Towards provable control for unknown linear dynamical systems
S Arora, E Hazan, H Lee, K Singh, C Zhang, Y Zhang
272018
Sampling approximately low-rank Ising models: MCMC meets variational methods
F Koehler, H Lee, A Risteski
Conference on Learning Theory, 4945-4988, 2022
252022
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
242020
Learning mixtures of gaussians using diffusion models
K Gatmiry, J Kelner, H Lee
arXiv preprint arXiv:2404.18869, 2024
222024
Improved rates for prediction and identification of partially observed linear dynamical systems
H Lee
International Conference on Algorithmic Learning Theory, 668-698, 2022
192022
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
192021
Fisher information lower bounds for sampling
S Chewi, P Gerber, H Lee, C Lu
International Conference on Algorithmic Learning Theory, 375-410, 2023
162023
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
142023
Pixie: a social chatbot
O Adewale, A Beatson, D Buniatyan, J Ge, M Khodak, H Lee, N Prasad, ...
Alexa prize proceedings, 2017
132017
Robust guarantees for learning an autoregressive filter
H Lee, C Zhang
Algorithmic Learning Theory, 490-517, 2020
122020
A rendszer jelenleg nem tudja elvégezni a műveletet. Próbálkozzon újra később.
Cikkek 1–20