| An Empirical Comparison of GANs and Normalizing Flows for Density Estimation T Liu, J Regier NeurIPS 2021 Workshop on Bayesian Deep Learning, 2021 | 7* | 2021 |
| Estimating three-and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder T Liu, C Wang, G Xu Frontiers in Psychology 13, 935419, 2022 | 6 | 2022 |
| SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification T Liu, H Wang, Y Wang, X Wang, L Su, J Gao Proceedings of the AAAI Conference on Artificial Intelligence 37 (12), 14338 …, 2023 | 3 | 2023 |
| Physically informed machine-learning algorithms for the identification of two-dimensional atomic crystals L Zichi, T Liu, E Drueke, L Zhao, G Xu Scientific Reports 13 (1), 6143, 2023 | 2 | 2023 |
| HadSkip: Homotopic and Adaptive Layer Skipping of Pre-trained Language Models for Efficient Inference H Wang, Y Wang, T Liu, T Zhao, J Gao The 2023 Conference on Empirical Methods in Natural Language Processing, 2023 | 1 | 2023 |
| Optimization for Amortized Inverse Problems T Liu, T Yang, Q Zhang, Q Lei Proceedings of the 40th International Conference on Machine Learning 202 …, 2023 | 1 | 2023 |
| Towards Poisoning Fair Representations T Liu, H Wang, F Wu, H Zhang, P Li, L Su, J Gao The twelfth International Conference on Learning Representations, 2023 | | 2023 |
| Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks D Zhang, T Liu, J Kang Stat 12 (1), e604, 2023 | | 2023 |
| PANOM: Automatic Hyper-parameter Tuning for Inverse Problems T Liu, Q Zhang, Q Lei NeurIPS 2021 Workshop on Deep Learning and Inverse Problems, 2021 | | 2021 |
| One Training Fits All: Addressing Model-Heterogeneity Federated Learning via Architecture Probing F Wu, Y Wang, T Liu, X Wang, L Su, J Gao | | |
