| Depaudionet: An efficient deep model for audio based depression classification X Ma, H Yang, Q Chen, D Huang, Y Wang Proceedings of the 6th international workshop on audio/visual emotion …, 2016 | 246 | 2016 |
| Meta-cal: Well-controlled post-hoc calibration by ranking X Ma, MB Blaschko International Conference on Machine Learning, 7235-7245, 2021 | 22 | 2021 |
| A Bayesian optimization framework for neural network compression X Ma, AR Triki, M Berman, C Sagonas, J Cali, MB Blaschko Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 22 | 2019 |
| Cost-sensitive two-stage depression prediction using dynamic visual clues X Ma, D Huang, Y Wang, Y Wang Computer Vision–ACCV 2016: 13th Asian Conference on Computer Vision, Taipei …, 2017 | 15 | 2017 |
| Additive tree-structured conditional parameter spaces in Bayesian optimization: A novel covariance function and a fast implementation X Ma, MB Blaschko IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (9), 3024-3036, 2020 | 7 | 2020 |
| Additive tree-structured covariance function for conditional parameter spaces in Bayesian optimization X Ma, M Blaschko International Conference on Artificial Intelligence and Statistics, 1015-1025, 2020 | 7 | 2020 |
| Confidence-aware personalized federated learning via variational expectation maximization J Zhu, X Ma, MB Blaschko Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 3 | 2023 |
| Tackling personalized federated learning with label concept drift via hierarchical Bayesian modeling X Ma, J Zhu, M Blaschko Online FL-NeurIPS 2022, 2022 | 1 | 2022 |
| A corrected expected improvement acquisition function under noisy observations H Zhou, X Ma, MB Blaschko arXiv preprint arXiv:2310.05166, 2023 | | 2023 |
| Uncertainty Estimation in Machine Learning: Applications in Neural Network Compression and Calibration X Ma, M Blaschko | | 2023 |
Matthew BlaschkoKU LeuvenVerified email at esat.kuleuven.be
