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 | 112 | 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 | 102 | 2022 |
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 | 101 | 2017 |
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 | 99 | 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 | 96 | 2018 |
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 | 87 | 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 | 55* | 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, 2024 | 53 | 2024 |
Towards provable control for unknown linear dynamical systems S Arora, E Hazan, H Lee, K Singh, C Zhang, Y Zhang | 26 | 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 | 23 | 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 | 22 | 2018 |
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 | 19 | 2020 |
Sampling approximately low-rank Ising models: MCMC meets variational methods F Koehler, H Lee, A Risteski Conference on Learning Theory, 4945-4988, 2022 | 18 | 2022 |
Improved rates for prediction and identification of partially observed linear dynamical systems H Lee International Conference on Algorithmic Learning Theory, 668-698, 2022 | 15 | 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 | 15 | 2021 |
Pixie: a social chatbot O Adewale, A Beatson, D Buniatyan, J Ge, M Khodak, H Lee, N Prasad, ... Alexa prize proceedings, 2017 | 14 | 2017 |
Fisher information lower bounds for sampling S Chewi, P Gerber, H Lee, C Lu International Conference on Algorithmic Learning Theory, 375-410, 2023 | 11 | 2023 |
Robust guarantees for learning an autoregressive filter H Lee, C Zhang Algorithmic Learning Theory, 490-517, 2020 | 10 | 2020 |
-Adic properties of partition functions E Belmont, H Lee, A Musat, S Trebat-Leder Monatshefte für Mathematik 173 (1), 1-34, 2014 | 8 | 2014 |
Provable benefits of score matching C Pabbaraju, D Rohatgi, AP Sevekari, H Lee, A Moitra, A Risteski Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |