A full list of my publications can be found on my Google Scholar page.

  • Erik Bodin, Zhenwen Dai, Neill D.F. Campbell, Carl Henrik Ek (2021)
    Black-box density function estimation using recursive partitioning
    International Conference on Machine Learning (ICML)
    [PDF]

  • Erik Bodin, Federico Tomasi, Zhenwen Dai (2021)
    Making Differentiable Architecture Search Less Local
    ICLR Workshop on Neural Architecture Search
    [PDF]

  • Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Benjamin Carterette, Mounia Lalmas (2020)
    Model Selection for Production System via Automated Online Experiments
    Advances in Neural Information Processing Systems (NeurIPS)
    [PDF]

  • Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill Campbell, Carl Henrik Ek (2020)
    Modulating Surrogates for Bayesian Optimization
    International Conference on Machine Learning (ICML)
    [PDF]

  • Federico Tomasi, Praveen Ravichandran, Gal Levy-Fix, Mounia Lalmas-Roelleke, Zhenwen Dai (2020)
    Stochastic Variational Inference for Dynamic Correlated Topic Models
    Conference on Uncertainty in Artificial Intelligence (UAI)
    [PDF]

  • Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier Gonzalez (2019)
    Meta-Surrogate Benchmarking for Hyperparameter Optimization
    Conference on Neural Information Processing Systems (NeurIPS)
    [PDF]

  • Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil Lawrence, David Dunson (2019)
    Intrinsic Gaussian processes on complex constrained domains
    Journal of the Royal Statistical Society: Series B (Statistical Methodology)
    [PDF]

  • Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai (2019)
    Variational Information Distillation for Knowledge Transfer
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    [PDF]

  • Abdul-Saboor Sheikh, Nicol S. Harper, Jakob Drefs, Yosef Singer, Zhenwen Dai, Richard E. Turner, Jörg Lücke (2019)
    STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds
    PLoS computational biology
    [PDF]

  • Zhenwen Dai, Eric Meissner, Neil D. Lawrence (2018)
    MXFusion: A Modular Deep Probabilistic Programming Library
    Neurips 2018 Workshop MLOSS Submission
    [PDF]

  • Xiaoyu Lu, Javier González, Zhenwen Dai, Neil D. Lawrence (2018)
    Structured Variationally Auto-encoded Optimization
    International Conference on Machine Learning (ICML)
    [PDF]

  • Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence (2017)
    Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
    Conference on Neural Information Processing Systems (NIPS)
    [PDF]

  • Zhenwen Dai, Mudassar Iqbal, Neil D. Lawrence, Magnus Rattray (2017)
    Efficient inference for sparse latent variable models of transcriptional regulation
    Bioinformatics
    [PDF] [CODE]

  • César Lincoln C. Mattos, Zhenwen Dai, Andreas Damianou, Guilherme A. Barreto, Neil D. Lawrence (2017)
    Deep recurrent Gaussian processes for outlier-robust system identification
    Journal of Process Control
    [PDF]

  • Jacquelyn A Shelton, Jan Gasthaus, Zhenwen Dai, Jörg Lücke, Arthur Gretton (2017)
    GP-select: Accelerating EM using adaptive subspace preselection
    Neural Computation
    [PDF]

  • Javier González, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence (2017)
    Preferential Bayesian Optimization
    International Conference on Machine Learning (ICML)
    [PDF] [video]

  • Zhenwen Dai, Andreas Damianou, Javier González, Neil D. Lawrence (2016)
    Variational Auto-encoded Deep Gaussian Processes
    International Conference on Learning Representations (ICLR)
    [PDF] [video]

  • César Lincoln C. Mattos, Zhenwen Dai, Andreas Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence (2016)
    Recurrent Gaussian Processes
    International Conference on Learning Representations (ICLR)
    [PDF] [video1] [video2] [video3]

  • Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence (2016)
    Batch Bayesian Optimization via Local Penalization
    International Conference on Artificial Intelligence and Statistics (AISTATS)
    [PDF]

  • Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek, Neil Lawrence (2016)
    Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
    International Conference on Learning Representations (ICLR) workshop track
    [PDF]

  • Fariba Yousefi, Zhenwen Dai, Qing Zhong, Peter Wild, Carl Henrik Ek, Neil D. Lawrence (2016)
    An unsupervised computational approach to detect PTEN loss in prostate cancer using an in situ hybridization (ISH) assay
    German Society of Pathology (DGP), oral presentation

  • Jose A. Rodriguez-Serrano, Diane Larlus, Zhenwen Dai (2015)
    Data-Driven Detection of Prominent Objects and of their Parts towards Improved Fine-Grained Visual Recognition
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
    [PDF]

  • Jacquelyn Shelton, Jan Gasthaus, Zhenwen Dai, Jörg Lücke and Arthur Gretton (2015)
    GP-select: Accelerating EM using adaptive subspace preselection
    arXiv:1412.3411

  • Zhenwen Dai, James Hensman and Neil D. Lawrence (2015)
    Spike and Slab Gaussian Process Latent Variable Models
    arXiv:1505.02434

  • Abdul-Saboor Sheikh, Zhenwen Dai, Nicol Harper, Richard Turner and Jörg Lücke (2015)
    Maximal causes for a masking based model of STRFs in primary auditory cortex
    Computational and Systems Neuroscience (Cosyne)

  • Zhenwen Dai and Neil D. Lawrence (2015)
    Variational Hierarchical Community of Experts
    ICML Deep Learning workshop

  • Fariba Yousefi, Zhenwen Dai, Qing Zhong, Peter Wild, Carl Henrik Ek, Neil D. Lawrence (2015)
    An Unsupervised Approach for Prostate Cancer Diagnosis from Tissue Microarray Images
    NIPS workshop on Women in Machine Learning

  • Zhenwen Dai and Jörg Lücke (2014)
    Autonomous Document Cleaning—A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 1950–1962
    [PDF]

  • Zhenwen Dai, Andreas Damianou, James Hensman and Neil D. Lawrence (2014)
    Gaussian Process Models with Parallelization and GPU acceleration
    NIPS workshop Software Engineering for Machine Learning
    [PDF]

  • Zhenwen Dai, Georgios Exarchakis and Jörg Lücke (2013)
    What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach
    Advances in Neural Information Processing Systems (NIPS), 243–251
    [PDF]

  • Zhenwen Dai and Jörg Lücke (2012)
    Unsupervised learning of translation invariant occlusive components
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2400–2407

  • Zhenwen Dai and Jörg Lücke (2012)
    Autonomous cleaning of corrupted scanned documents—A generative modeling approach
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), oral presentation, 3338–3345

  • Miaomiao Liu, K-YK Wong, Zhenwen Dai and Zhihu Chen (2011)
    Pose estimation from reflections for specular surface recovery
    IEEE International Conference on Computer Vision (ICCV), 579–586

  • Zhenwen Dai, Jacquelyn A Shelton, Jörg Bornschein, Abdul Saboor Sheikh and Jörg Lücke (2011)
    Combining approximate inference methods for efficient learning on large computer clusters
    NIPS workshop Big Learning: Algorithms, Systems, and Tools for Learning, 16–17

  • Miaomiao Liu, Kwan-Yee K Wong, Zhenwen Dai and Zhihu Chen (2011)
    Specular surface recovery from reflections of a planar pattern undergoing an unknown pure translation
    Asian Conference on Computer Vision (ACCV) 2010, 137–147