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Neil D. Lawrence
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Publications
- 2020
- [j38]Bei Wang, Zhichao Li, Zhenwen Dai, Neil D. Lawrence, Xuefeng Yan:
Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes. IEEE Trans. Ind. Informatics 16(6): 3651-3661 (2020) - 2019
- [j37]Bei Wang, Zhichao Li, Zhenwen Dai, Neil D. Lawrence, Xuefeng Yan:
A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant. Appl. Soft Comput. 82 (2019) - [c77]Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai:
Variational Information Distillation for Knowledge Transfer. CVPR 2019: 9163-9171 - [c75]Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González:
Meta-Surrogate Benchmarking for Hyperparameter Optimization. NeurIPS 2019: 6267-6277 - [i36]Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai:
Variational Information Distillation for Knowledge Transfer. CoRR abs/1904.05835 (2019) - [i35]Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González:
Meta-Surrogate Benchmarking for Hyperparameter Optimization. CoRR abs/1905.12982 (2019) - 2018
- [c73]Xiaoyu Lu, Javier González, Zhenwen Dai, Neil D. Lawrence:
Structured Variationally Auto-encoded Optimization. ICML 2018: 3273-3281 - [i33]Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil D. Lawrence, David B. Dunson:
Intrinsic Gaussian processes on complex constrained domains. CoRR abs/1801.01061 (2018) - 2017
- [j35]Zhenwen Dai, Mudassar Iqbal, Neil D. Lawrence, Magnus Rattray:
Efficient inference for sparse latent variable models of transcriptional regulation. Bioinform. 33(23): 3776-3783 (2017) - [c72]Javier González, Zhenwen Dai, Andreas C. Damianou, Neil D. Lawrence:
Preferential Bayesian Optimization. ICML 2017: 1282-1291 - [c70]Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence:
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS 2017: 5131-5139 - [i27]Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence:
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. CoRR abs/1705.09862 (2017) - [i25]Matthias W. Seeger, Asmus Hetzel, Zhenwen Dai, Neil D. Lawrence:
Auto-Differentiating Linear Algebra. CoRR abs/1710.08717 (2017) - 2016
- [c69]Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence:
Batch Bayesian Optimization via Local Penalization. AISTATS 2016: 648-657 - [c63]Zhenwen Dai, Andreas C. Damianou, Javier González, Neil D. Lawrence:
Variational Auto-encoded Deep Gaussian Processes. ICLR (Poster) 2016 - [c62]César Lincoln C. Mattos, Zhenwen Dai, Andreas C. Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence:
Recurrent Gaussian Processes. ICLR (Poster) 2016 - [i21]Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek, Neil D. Lawrence:
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model. CoRR abs/1607.00067 (2016) - 2015
- [i18]Zhenwen Dai, James Hensman, Neil D. Lawrence:
Spike and Slab Gaussian Process Latent Variable Models. CoRR abs/1505.02434 (2015) - 2014
- [i14]Zhenwen Dai, Andreas C. Damianou, James Hensman, Neil D. Lawrence:
Gaussian Process Models with Parallelization and GPU acceleration. CoRR abs/1410.4984 (2014)
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