| From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers K Choromanski, H Lin, H Chen, T Zhang, A Sehanobish, V Likhosherstov, ... International Conference on Machine Learning, 3962-3983, 2022 | 35* | 2022 |
| Learning prediction intervals for regression: Generalization and calibration H Chen, Z Huang, H Lam, H Qian, H Zhang International Conference on Artificial Intelligence and Statistics, 820-828, 2021 | 25 | 2021 |
| Hybrid random features K Choromanski, H Chen, H Lin, Y Ma, A Sehanobish, D Jain, MS Ryoo, ... The Tenth International Conference on Learning Representations, 2021 | 24 | 2021 |
| Demystifying orthogonal monte carlo and beyond H Lin, H Chen, KM Choromanski, T Zhang, C Laroche Advances in Neural Information Processing Systems 33, 8030-8041, 2020 | 8 | 2020 |
| Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning H Zhao, H Chen, J Zhang, DD Yao, W Tang arXiv preprint arXiv:2409.08400, 2024 | 3 | 2024 |
| MallowsPO: Fine-Tune Your LLM with Preference Dispersions H Chen, H Zhao, H Lam, D Yao, W Tang The Thirteenth International Conference on Learning Representations, 2024 | 3 | 2024 |
| Pseudo-bayesian optimization H Chen, H Lam arXiv preprint arXiv:2310.09766, 2023 | 3 | 2023 |
| Constrained Reinforcement Learning via Policy Splitting H Chen, H Lam, F Li, A Meisami Asian Conference on Machine Learning, 209-224, 2020 | 2 | 2020 |
| Calibrating over-parametrized simulation models: A framework via eligibility set Y Bai, T Balch, H Chen, D Dervovic, H Lam, S Vyetrenko arXiv preprint arXiv:2105.12893, 2021 | 1 | 2021 |
| Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate F Li, H Chen, J Lin, A Gupta, X Tan, G Xu, Y Nevmyvaka, A Capponi, ... arXiv preprint arXiv:2412.11257, 2024 | | 2024 |
Henry LamColumbia UniversityPreverjeni e-poštni naslov na columbia.edu
