Neil Lawrence
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Why Is Neil Lawrence Influential?
(Suggest an Edit or Addition)According to Wikipedia, Neil David Lawrence is the DeepMind Professor of Machine Learning at the University of Cambridge in the Department of Computer Science and Technology, senior AI fellow at the Alan Turing Institute and visiting professor at the University of Sheffield.
Neil Lawrence's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Dataset Shift in Machine Learning (2009) (1473)
- Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models (2005) (1055)
- Gaussian Processes for Big Data (2013) (991)
- Deep Gaussian Processes (2012) (895)
- Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data (2003) (819)
- Kernels for Vector-Valued Functions: a Review (2011) (662)
- Fast Sparse Gaussian Process Methods: The Informative Vector Machine (2002) (579)
- WiFi-SLAM Using Gaussian Process Latent Variable Models (2007) (568)
- Fast Forward Selection to Speed Up Sparse Gaussian Process Regression (2003) (481)
- Bayesian Gaussian Process Latent Variable Model (2010) (440)
- Learning to learn with the informative vector machine (2004) (368)
- Variational Information Distillation for Knowledge Transfer (2019) (349)
- Batch Bayesian Optimization via Local Penalization (2015) (289)
- Computationally Efficient Convolved Multiple Output Gaussian Processes (2011) (273)
- Local distance preservation in the GP-LVM through back constraints (2006) (261)
- Non-linear matrix factorization with Gaussian processes (2009) (246)
- Missing Data in Kernel PCA (2006) (237)
- Single-cell RNA-seq and computational analysis using temporal mixture modeling resolves TH1/TFH fate bifurcation in malaria (2017) (229)
- Estimating a Kernel Fisher Discriminant in the Presence of Label Noise (2001) (214)
- Sparse Convolved Gaussian Processes for Multi-output Regression (2008) (205)
- When Training and Test Sets Are Different: Characterizing Learning Transfer (2009) (203)
- Hierarchical Gaussian process latent variable models (2007) (200)
- Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes (2008) (196)
- Latent Force Models (2009) (179)
- Semi-supervised Learning via Gaussian Processes (2004) (171)
- Gaussian Process Latent Variable Models for Human Pose Estimation (2007) (167)
- Topologically-constrained latent variable models (2008) (143)
- Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies (2012) (138)
- Unravelling the enigma of selective vulnerability in neurodegeneration: motor neurons resistant to degeneration in ALS show distinct gene expression characteristics and decreased susceptibility to excitotoxicity (2012) (134)
- Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes (2016) (132)
- Efficient Multioutput Gaussian Processes through Variational Inducing Kernels (2010) (128)
- Learning for Larger Datasets with the Gaussian Process Latent Variable Model (2007) (122)
- Modelling transcriptional regulation using Gaussian Processes (2006) (122)
- Fast Variational Inference in the Conjugate Exponential Family (2012) (119)
- Model-based method for transcription factor target identification with limited data (2010) (118)
- Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities (2008) (117)
- Challenges in Deploying Machine Learning: A Survey of Case Studies (2020) (117)
- Variational Auto-encoded Deep Gaussian Processes (2015) (117)
- Elementary properties of CaV1.3 Ca2+ channels expressed in mouse cochlear inner hair cells (2009) (112)
- Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities (2006) (111)
- Manifold Relevance Determination (2012) (105)
- Advances in Neural Information Processing Systems 14 (2002) (100)
- Variational Gaussian Process Dynamical Systems (2011) (98)
- Approximating Posterior Distributions in Belief Networks Using Mixtures (1997) (98)
- A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression (2011) (96)
- Variational inference for visual tracking (2003) (96)
- GLASSES: Relieving The Myopia Of Bayesian Optimisation (2015) (95)
- Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis (2005) (95)
- A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips (2005) (91)
- Overlapping Mixtures of Gaussian Processes for the Data Association Problem (2011) (88)
- Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters (2013) (88)
- puma: a Bioconductor package for propagating uncertainty in microarray analysis (2009) (86)
- Empirical Bayes Transductive Meta-Learning with Synthetic Gradients (2020) (86)
- Probe-level measurement error improves accuracy in detecting differential gene expression (2006) (86)
- Efficient inference in matrix-variate Gaussian models with \iid observation noise (2011) (85)
- Learning and Inference in Computational Systems Biology (2010) (80)
- Linear Latent Force Models Using Gaussian Processes (2011) (77)
- Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays (2015) (75)
- Automatic Determination of the Number of Clusters Using Spectral Algorithms (2005) (75)
- Transcriptomic indices of fast and slow disease progression in two mouse models of amyotrophic lateral sclerosis. (2013) (75)
- Deep Gaussian Processes for Multi-fidelity Modeling (2019) (74)
- Recurrent Gaussian Processes (2015) (68)
- Genome-wide occupancy links Hoxa2 to Wnt–β-catenin signaling in mouse embryonic development (2012) (67)
- Ambiguity Modeling in Latent Spaces (2008) (66)
- Accounting for probe-level noise in principal component analysis of microarray data (2005) (66)
- Preferential Bayesian Optimization (2017) (65)
- Nested Variational Compression in Deep Gaussian Processes (2014) (65)
- Bottom-Up Data Trusts: Disturbing the ‘One Size Fits All’ Approach to Data Governance (2018) (64)
- Emulation of physical processes with Emukit (2021) (61)
- Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis (2006) (60)
- A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models (2010) (60)
- Bayesian Optimization for Synthetic Gene Design (2015) (58)
- Fast Nonparametric Clustering of Structured Time-Series (2014) (57)
- Extensions of the Informative Vector Machine (2004) (57)
- Transferring Knowledge across Learning Processes (2018) (53)
- Efficient Sampling for Gaussian Process Inference using Control Variables (2008) (51)
- Metrics for Probabilistic Geometries (2014) (49)
- Chained Gaussian Processes (2016) (48)
- International workshop on machine learning for multimodal interaction (2007) (48)
- Continual Learning in Practice (2019) (48)
- Reducing the variability in cDNA microarray image processing by Bayesian inference (2004) (47)
- Warped linear mixed models for the genetic analysis of transformed phenotypes (2014) (46)
- A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription (2006) (42)
- Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems (2017) (40)
- Solving Schrödinger Bridges via Maximum Likelihood (2021) (39)
- Switched Latent Force Models for Movement Segmentation (2010) (38)
- Modeling Human Locomotion with Topologically Constrained Latent Variable Models (2007) (37)
- The Bigraphical Lasso (2013) (37)
- Linear latent force models using Gaussian processes. (2013) (36)
- A probabilistic model for the extraction of expression levels from oligonucleotide arrays. (2003) (36)
- Structured Variationally Auto-encoded Optimization (2018) (34)
- Semi-described and semi-supervised learning with Gaussian processes (2015) (34)
- Fast Variational Inference for Gaussian Process Models Through KL-Correction (2006) (32)
- Gaussian Process Models with Parallelization and GPU acceleration (2014) (32)
- Meta-Surrogate Benchmarking for Hyperparameter Optimization (2019) (32)
- TFInfer: a tool for probabilistic inference of transcription factor activities (2010) (31)
- Spectral Dimensionality Reduction via Maximum Entropy (2011) (31)
- Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes (2017) (30)
- Propagating uncertainty in microarray data analysis (2006) (30)
- Intrinsic Gaussian processes on complex constrained domains (2018) (30)
- Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes (2020) (29)
- A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics (2001) (29)
- MOCAP Toolbox for MATLAB (2005) (29)
- Detecting periodicities with Gaussian processes (2016) (28)
- Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison (2012) (28)
- Differentially Private Regression with Gaussian Processes (2018) (28)
- Data Readiness Levels (2017) (27)
- An integrated probabilistic framework for robot perception, learning and memory (2016) (27)
- Mixture Representations for Inference and Learning in Boltzmann Machines (1998) (26)
- Bayesian Time Series Models: Markov chain Monte Carlo algorithms for Gaussian processes (2011) (26)
- Topslam: Waddington Landscape Recovery for Single Cell Experiments (2016) (25)
- Variational Bayesian Independent Component Analysis (1999) (25)
- Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data (2013) (25)
- Kernels for Vector-Valued Functions (2012) (24)
- Variational Inference for Uncertainty on the Inputs of Gaussian Process Models (2014) (24)
- Auto-Differentiating Linear Algebra (2017) (24)
- Memory and mental time travel in humans and social robots (2019) (23)
- A variational approach to robust Bayesian interpolation (2003) (20)
- Parallelizable sparse inverse formulation Gaussian processes (SpInGP) (2016) (19)
- A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant (2019) (18)
- Efficient Nonparametric Bayesian Modelling with Sparse Gaussian Process Approximations (2006) (18)
- Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition (2012) (18)
- Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery (2009) (18)
- Gaussian process models for periodicity detection (2013) (18)
- Latent Autoregressive Gaussian Processes Models for Robust System Identification (2016) (17)
- The Emergence of Organizing Structure in Conceptual Representation (2016) (17)
- Differentially Private Gaussian Processes (2016) (16)
- Deep recurrent Gaussian processes for outlier-robust system identification (2017) (16)
- Detecting regulatory gene-environment interactions with unmeasured environmental factors (2013) (16)
- Deep recurrent Gaussian processes for outlier-robust system identification (2017) (16)
- Tilted Variational Bayes (2014) (16)
- Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment (2021) (13)
- Residual Components Analysis (2012) (13)
- Conference on Biomimetic and Biohybrid Systems (2015) (13)
- Residual Component Analysis: Generalising PCA for more flexible inference in linear-Gaussian models (2012) (13)
- The Informative Vector Machine: A Practical Probabilistic Alternative to the Support Vector Machine (2004) (12)
- Variational Dropout and the Local Reparameterization Trick (2015) (12)
- Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients (2019) (12)
- Variational Inducing Kernels for Sparse Convolved Multiple Output G aussian Processes (2009) (11)
- A hybrid Maxent/HMM based ASR system (2005) (11)
- MXFusion: A Modular Deep Probabilistic Programming Library (2018) (11)
- Geometry of Covariate Shift with Applications to Active Learning (2009) (11)
- Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis (2016) (11)
- Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures (2005) (10)
- Transferring Nonlinear Representations using Gaussian Processes with a Shared Latent Space (2008) (10)
- Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions (2009) (10)
- Binary Classification under Sample Selection Bias (2009) (10)
- Variationally Auto-Encoded Deep G aussian Processes (2016) (9)
- Large Scale Learning with the Gaussian Process Latent Variable Model (2008) (9)
- tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor (2011) (9)
- Sparse Convolved Multiple Output G aussian Processes (2009) (9)
- A Top-Down Approach for a Synthetic Autobiographical Memory System (2015) (9)
- Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning (2004) (8)
- A reverse-engineering approach to dissect post-translational modulators of transcription factor’s activity from transcriptional data (2015) (8)
- Gaussian Process Latent Variable Models for Fault Detection (2007) (8)
- A Sparse B ayesian Compression Scheme — The Informative Vector Machine (2001) (8)
- Gaussian Processes for Natural Language Processing (2014) (8)
- Gaussian Process Regression for Binned Data (2018) (8)
- Gaussian Processes and the Null-Category Noise Model (2006) (7)
- When Training and Test Sets Are Different (2008) (7)
- Hybrid Discriminative-Generative Approach with Gaussian Processes (2014) (7)
- Temporal mixture modelling of single-cell RNA-seq data resolves a CD4+ T cell fate bifurcation (2016) (7)
- Acoustic space dimensionality selection and combination using the maximum entropy principle (2004) (7)
- Spike and Slab Gaussian Process Latent Variable Models (2015) (6)
- Gaussian Processes for Big Data through Stochastic Variational Inference (2012) (6)
- Bayesian processing of microarray images (2003) (6)
- A Variational B ayesian Committee of Neural Networks (1999) (6)
- Residual Component Analysis (2011) (6)
- Matching Kernels through K ullback- L eibler Divergence Minimisation (2004) (6)
- Introduction to Dataset Shift (2009) (6)
- Probabilistic modelling of replica divergence (2001) (6)
- Consistent mapping of government malaria records across a changing territory delimitation (2014) (5)
- Sparse B ayesian Learning: The Informative Vector Machine (2002) (5)
- Living Together: Mind and Machine Intelligence (2017) (5)
- A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift (2009) (5)
- Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design (2019) (5)
- Monitoring Short Term Changes of Infectious Diseases in Uganda with Gaussian Processes (2015) (5)
- Overlapping Mixtures of Gaussian Processes for the data association problem (2012) (4)
- Generalised GPLVM with Stochastic Variational Inference (2022) (4)
- Optimising Synchronisation Times for Mobile Devices (2001) (4)
- Note Relevance Determination (2001) (4)
- iCub Visual Memory Inspector: Visualising the iCub's Thoughts (2016) (4)
- Towards better data discovery and collection with flow-based programming (2021) (4)
- Deep Gaussian Processes for Large Datasets (2014) (4)
- Efficient inference for sparse latent variable models of transcriptional regulation (2017) (4)
- Genetic Analysis of Transformed Phenotypes (2014) (4)
- Increasing Power by Sharing Information from Genetic Background and Treatment in Clustering of Gene Expression Time Series (2018) (4)
- An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context (2022) (4)
- Dealing with high dimensional data with dimensionality reduction (2009) (4)
- Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count Data (2022) (3)
- Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model (2016) (3)
- Modeling the Machine Learning Multiverse (2022) (3)
- Generalised Component Analysis (2003) (3)
- Malaria surveillance with multiple data sources using Gaussian process models (2014) (3)
- GP-LVM for data consolidation (2008) (3)
- Empirical Bayes Meta-Learning with Synthetic Gradients (2019) (3)
- International Conference on Learning Representations Workshop track (2016) (3)
- Preface: Intelligent interactive data visualization (2013) (3)
- An Adversarial View of Covariate Shift and a Minimax Approach (2009) (3)
- Differentially Private Regression and Classification with Sparse Gaussian Processes (2019) (3)
- Markovian inference in belief networks (1998) (3)
- Behavioral Experiments for Understanding Catastrophic Forgetting (2021) (3)
- Model-driven detection of clean speech patches in noise (2007) (3)
- Advances in Intelligent Data Analysis XIV (2015) (3)
- Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters (2013) (2)
- Ranking of gene regulators through differential equations and Gaussian processes (2010) (2)
- Gaussian Process Inference for Differential Equation Models of Transcriptional Regulation (2011) (2)
- Bayesian learning via neural Schrödinger–Föllmer flows (2021) (2)
- Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology (2016) (2)
- Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs (2022) (2)
- Probabilistic Spectral Dimensionality Reduction (2010) (2)
- Introduction to Learning and Inference in Computational Systems Biology (2010) (2)
- A Gaussian Process Model for Inferring the Dynamic Transcription Factor Activity (2016) (2)
- Monitoring Short Term Changes of Malaria Incidence in Uganda with Gaussian Processes (2015) (2)
- Unravelling Nature's Networks: From Microarray and Proteomic Analysis to Systems Biology: University of Sheffield, 21–22 July 2003 (2003) (2)
- Towards a Data Science Collaboratory (2015) (2)
- Gaussian Processes for Missing Species in Biochemical Systems (2010) (1)
- Making Implementations Available for the Research Community (2010) (1)
- Soil Chemistry Analysis as an Effective Cultural Resource Management Tool: A Magical Mystery Tour (2007) (1)
- Deep G aussian Processes (2013) (1)
- The GP-LVM for Vocal Joystick Control (2006) (1)
- A probabilistic model to integrate chip and microarray data (2006) (1)
- Hierarchical G aussian Process Latent Variable Models (2007) (1)
- On Bayesian Transduction: Implications for the Covariate Shift Problem (2009) (1)
- Fast variational inference for nonparametric clustering of structured time-series (2014) (1)
- Identifying Submodules of Cellular Regulatory Networks (2006) (1)
- puma User Guide (2007) (1)
- Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference (2022) (1)
- G aussian Processes for Big Data with Stochastic Variational Inference (2012) (1)
- Higher dose corticosteroids in patients admitted to hospital with COVID-19 who are hypoxic but not requiring ventilatory support (RECOVERY): a randomised, controlled, open-label, platform trial (2023) (1)
- Network Performance Analysis (2007) (1)
- Variational Inference in G aussian Processes via Probabilistic Point Assimilation (2005) (1)
- Consistent mapping of government malaria records across a changing territory delimitation (2014) (1)
- The Effect of Task Ordering in Continual Learning (2022) (1)
- Modular Deep Probabilistic Programming (2018) (1)
- Variational Learning for Multi-Layer Networks of Linear Threshold Units (2001) (1)
- Theoretical Views on Dataset and Covariate Shift (2009) (1)
- Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects (2011) (1)
- MEDITERRANEAN CONFERENCE ON INTELLIGENT SYSTEMS AND AUTOMATION (2009) (1)
- Node Relevance Determination (2001) (1)
- Dataflow graphs as complete causal graphs (2023) (1)
- Blastocyst Development in Mice: Single Cell TaqMan Arrays (2016) (0)
- Markov chain M onte C arlo algorithms for G aussian processes (2011) (0)
- The Structure of Neural Network Posteriors (2001) (0)
- A Probabilistic Model of Transcription Dynamics applied to Estrogen Signalling in Breast Cancer Cells (2013) (0)
- G aussian Process Models for Visualisation of High Dimensional Data (2004) (0)
- tigre Quick Guide (2010) (0)
- Variational Optimisation by Marginal Matching (2007) (0)
- Dimensionality Reduction as Probabilistic Inference (2023) (0)
- Advances in Neural Information Processing Systems Workshop on Learning from Multiple Sources (2008) (0)
- Latent Force Models: Bridging the Divide between Mechanistic and Data Modelling Paradigms (2015) (0)
- Invited Talk Abstracts (2010) (0)
- Causal fault localisation in dataflow systems (2023) (0)
- Manifold Alignment Determination (2015) (0)
- TP1: Leveraging Complex Prior Knowledge in Learning (2008) (0)
- Algorithms for Covariate Shift (2009) (0)
- Prior Knowledge and Sparse Methods for Convolved Multiple Outputs Gaussian Processes (2009) (0)
- On the Training/Test Distributions Gap: A Data Representation Learning Framework (2009) (0)
- Gaussian Processes in Practice (2007) (0)
- Variational B ayesian Independent Component Analysis (2000) (0)
- Human Motion Modelling through Dimensional Reduction with Gaussian Processes (2008) (0)
- Approximate Inference in Deep GPs (2014) (0)
- An Introduction to Systems Biology from a Machine Learning Perspective II (2009) (0)
- Explorer Modelling transcriptional regulation using Gaussian processes (2006) (0)
- Gene expression Probe-level measurement error improves accuracy in detecting differential gene expression (2006) (0)
- Projection and Projectability (2009) (0)
- The G aussian Process Latent Variable Model (2006) (0)
- UvA-DARE (Digital Academic Repository) Variational Dropout and the Local Reparameterization Trick Variational Dropout and the Local Reparameterization Trick (2015) (0)
- Detecting periodicities with Gaussian 1 processes 2 (2017) (0)
- Human Motion Modelling with Gaussian Processes (2008) (0)
- Ice Core Dating using Probabilistic Programming (2022) (0)
- Clustering Gene Expression Time Series with Coregionalization: Speed propagation of ALS (2018) (0)
- On Learning Decision Heuristics (2017) (0)
- The 3Ds of Machine Learning Systems Design (2018) (0)
- An Efficient Convolutional Framework for Multitask Learning (2009) (0)
- Modelling Technical and Biological Effects in single-cell RNA-seq data with Scalable Gaussian Process Latent Variable Models (GPLVMs) (2022) (0)
- Latent Force Models: Introduction (2013) (0)
- A reverse-engineering approach to dissect post-translational modulators of transcription factor’s activity from transcriptional data (2015) (0)
- Notation and Symbols (2009) (0)
- Sparse Multi-output Gaussian Processes (2008) (0)
- Discriminative Learning under Covariate Shift with a Single Optimization Problem (2009) (0)
- A Brief Introduction to Bayesian Inference (2010) (0)
- Introduction to Gaussian Processes (2013) (0)
- AI for Science: An Emerging Agenda (2023) (0)
- Gaussian Process Latent Variable Flows for Massively Missing Data (2020) (0)
- Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382) (2022) (0)
- Recurrent G aussian Processes (2016) (0)
- E MPIRICAL B AYES T RANSDUCTIVE M ETA -L EARNING WITH S YNTHETIC G RADIENTS (2020) (0)
- Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective (2023) (0)
- Mining regulatory network connections by ranking transcription factor target genes using time series expression data. (2013) (0)
- VIA STRUCTURE CONSOLIDATION LATENT VARIABLE MODEL (2016) (0)
- Chained G aussian Processes (2016) (0)
- Manifold Alignment Determination: finding correspondences across different data views (2017) (0)
- Analysis of therapy monitoring in the International Congenital Adrenal Hyperplasia Registry (2021) (0)
- Multi-omic modeling of translational efficiency for synthetic gene design (2016) (0)
- A Brief Introduction to B ayesian Inference (2010) (0)
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Neil Lawrence is affiliated with the following schools:
