Understanding Research Field Evolving and Trend with Dynamic Bayesian Networks. - Jinlong Wang; Congfu Xu; Gang Li 009; Zhenwen Dai; Guojing Luo (PAKDD, 2007)

Polygonal Light Source Estimation. - Dirk Schnieders; Kwan-Yee Kenneth Wong; Zhenwen Dai (ACCV (3), 2009)

Specular Surface Recovery from Reflections of a Planar Pattern Undergoing an Unknown Pure Translation. - Miaomiao Liu 001; Kwan-Yee Kenneth Wong; Zhenwen Dai; Zhihu Chen (ACCV (2), 2010)

Pose estimation from reflections for specular surface recovery. - Miaomiao Liu 001; Kwan-Yee Kenneth Wong; Zhenwen Dai; Zhihu Chen (ICCV, 2011)

Unsupervised learning of translation invariant occlusive components. - Zhenwen Dai; Jörg Lücke (CVPR, 2012)

Autonomous cleaning of corrupted scanned documents - A generative modeling approach. - Zhenwen Dai; Jörg Lücke (CVPR, 2012)

What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach. - Zhenwen Dai; Georgios Exarchakis; Jörg Lücke (NIPS, 2013)

Unsupervised learning of invariant object representations: a probabilistic generative modeling approach. - Zhenwen Dai (2013)

Gaussian Process Models with Parallelization and GPU acceleration. - Zhenwen Dai; Andreas C. Damianou; James Hensman; Neil D. Lawrence (CoRR, 2014)

Autonomous Document Cleaning - A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts. - Zhenwen Dai; Jörg Lücke (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Spike and Slab Gaussian Process Latent Variable Models. - Zhenwen Dai; James Hensman; Neil D. Lawrence (CoRR, 2015)

Batch Bayesian Optimization via Local Penalization. - Javier González 002; Zhenwen Dai; Philipp Hennig; Neil D. Lawrence (AISTATS, 2016)

Variational Auto-encoded Deep Gaussian Processes. - Zhenwen Dai; Andreas C. Damianou; Javier González 002; Neil D. Lawrence (ICLR (Poster), 2016)

Recurrent Gaussian Processes. - César Lincoln C. Mattos; Zhenwen Dai; Andreas C. Damianou; Jeremy Forth; Guilherme A. Barreto; Neil D. Lawrence (ICLR (Poster), 2016)

Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model. - Fariba Yousefi; Zhenwen Dai; Carl Henrik Ek; Neil D. Lawrence (CoRR, 2016)

Data-Driven Detection of Prominent Objects. - José A. Rodríguez-Serrano; Diane Larlus; Zhenwen Dai (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

Preferential Bayesian Optimization. - Javier González 002; Zhenwen Dai; Andreas C. Damianou; Neil D. Lawrence (ICML, 2017)

Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. - Zhenwen Dai; Mauricio A. Álvarez; Neil D. Lawrence (NIPS, 2017)

Efficient inference for sparse latent variable models of transcriptional regulation. - Zhenwen Dai; Mudassar Iqbal; Neil D. Lawrence; Magnus Rattray (Bioinform., 2017)

Auto-Differentiating Linear Algebra. - Matthias W. Seeger; Asmus Hetzel; Zhenwen Dai; Neil D. Lawrence (CoRR, 2017)

GP-Select: Accelerating EM Using Adaptive Subspace Preselection. - Jacquelyn A. Shelton; Jan Gasthaus; Zhenwen Dai; Jörg Lücke; Arthur Gretton (Neural Comput., 2017)

Truncated Variational Sampling for 'Black Box' Optimization of Generative Models. - Jörg Lücke; Zhenwen Dai; Georgios Exarchakis (LVA/ICA, 2018)

Structured Variationally Auto-encoded Optimization. - Xiaoyu Lu; Javier González 002; Zhenwen Dai; Neil D. Lawrence (ICML, 2018)

Intrinsic Gaussian processes on complex constrained domains. - Mu Niu; Pokman Cheung; Lizhen Lin; Zhenwen Dai; Neil D. Lawrence; David B. Dunson (CoRR, 2018)

Variational Information Distillation for Knowledge Transfer. - Sungsoo Ahn; Shell Xu Hu; Andreas C. Damianou; Neil D. Lawrence; Zhenwen Dai (CVPR, 2019)

Meta-Surrogate Benchmarking for Hyperparameter Optimization. - Aaron Klein; Zhenwen Dai; Frank Hutter; Neil D. Lawrence; Javier González 002 (NeurIPS, 2019)

A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant. - Bei Wang; Zhichao Li; Zhenwen Dai; Neil D. Lawrence; Xuefeng Yan (Appl. Soft Comput., 2019)

ProSper - A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions. - Georgios Exarchakis; Jörg Bornschein; Abdul-Saboor Sheikh; Zhenwen Dai; Marc Henniges; Jakob Drefs; Jörg Lücke (CoRR, 2019)

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

Modulating Surrogates for Bayesian Optimization. - Erik Bodin; Markus Kaiser 001; Ieva Kazlauskaite; Zhenwen Dai; Neill W. Campbell; Carl Henrik Ek (ICML, 2020)

Model Selection for Production System via Automated Online Experiments. - Zhenwen Dai; Praveen Chandar; Ghazal Fazelnia; Benjamin A. Carterette; Mounia Lalmas (NeurIPS, 2020)

Stochastic Variational Inference for Dynamic Correlated Topic Models. - Federico Tomasi; Praveen Chandar; Gal Levy-Fix; Mounia Lalmas-Roelleke; Zhenwen Dai (UAI, 2020)

Black-box density function estimation using recursive partitioning. - Erik Bodin; Zhenwen Dai; Neill D. F. Campbell; Carl Henrik Ek (CoRR, 2020)

The Evidence Lower Bound of Variational Autoencoders Converges to a Sum of Three Entropies. - Jörg Lücke; Dennis Forster; Zhenwen Dai (CoRR, 2020)

Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes. - Bei Wang; Zhichao Li; Zhenwen Dai; Neil D. Lawrence; Xuefeng Yan (IEEE Trans. Ind. Informatics, 2020)

Black-box density function estimation using recursive partitioning. - Erik Bodin; Zhenwen Dai; Neill W. Campbell; Carl Henrik Ek (ICML, 2021)

Making Differentiable Architecture Search less local. - Erik Bodin; Federico Tomasi; Zhenwen Dai (CoRR, 2021)

Efficient inference for dynamic topic modeling with large vocabularies. - Federico Tomasi; Mounia Lalmas; Zhenwen Dai (UAI, 2022)

The ELBO of Variational Autoencoders Converges to a Sum of Entropies. - Simon Damm; Dennis Forster; Dmytro Velychko; Zhenwen Dai; Asja Fischer; Jörg Lücke (AISTATS, 2023)

Exploiting Sequential Music Preferences via Optimisation-Based Sequencing. - Dmitrii Moor; Yi Yuan; Rishabh Mehrotra; Zhenwen Dai; Mounia Lalmas (CIKM, 2023)

Automatic Music Playlist Generation via Simulation-based Reinforcement Learning. - Federico Tomasi; Joseph Cauteruccio; Surya Kanoria; Kamil Ciosek; Matteo Rinaldi; Zhenwen Dai (KDD, 2023)

A Strong Baseline for Batch Imitation Learning. - Matthew Smith; Lucas Maystre; Zhenwen Dai; Kamil Ciosek (CoRR, 2023)

Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics. - Mu Niu; Zhenwen Dai; Pokman Cheung; Yizhu Wang (J. Mach. Learn. Res., 2023)

In-context Exploration-Exploitation for Reinforcement Learning. - Zhenwen Dai; Federico Tomasi; Sina Ghiassian (ICLR, 2024)

Diffusion Model for Slate Recommendation. - Federico Tomasi; Francesco Fabbri; Mounia Lalmas; Zhenwen Dai (CoRR, 2024)