# Talks

- Personalized Health: Challenges in Data Science at CWI Lectures in Machine Learning
- Data Science: Time for Professionalisation? at The Open Data Science Conference
- Living Together: Mind and Machine Intelligence at TEDx Exeter
- Where Next for AI? at CW AI Conference
- Introduction to Gaussian Processes at Gaussian Process Summer School, Sheffield
- Cloaking Functions: Differential Privacy with Gaussian Processes at CD-MAKE Workshop at ARES 2017
- What is Machine Learning? at Data Science Africa, Arusha, Tanzania
- Once Upon a Universal Standard Time: Embodiment and AI Narratives at CFI Conference
- Data Analytics Perspectives: Machine Learning at CSaP 2017 Conference, The Royal Society
- Machine Learning, Technology and the Future of Intelligence at Leverhulme CFI
- Probabilistic Dimensionality Reduction at Probabilistic Scientific Computing Workshop at ICERM, Brown
- Peppercorns and Machine Learning System Design at Sheffield ML Group Research Retreat
- The Data Science Process at AMLC Data Science Process Workshop
- The Data Science Process at DALI Data Science Process Workshop
- Machine Learning and the Data Science Process at OxWaSP Workshop, Amazon, Berlin
- The rise of the algorithm - artificial intelligence, ethics, trust and tech development at The Guardian Changing Media Summit
- Ethics, Computer Systems and the Professions at UCL Gustave Tuck Theatre, London WC1
- Challenges and Opportunities in Machine Learning and Artificial Intelligence at ARM, Cambridge
- Challenges for Delivering Machine Learning in Health at Deep Learning in Healthcare Summit, London
- Three Challenges in Data Science at Advanced Data Analytics Seminars, Data Science Institute, University of Manchester
- Latent Variable Models with Gaussian Processes at STOR-i Masterclass and University of Lancaster Seminar
- Introduction to Gaussian Processes at STOR-i Masterclass
- Covariance Functions and the Marginal Likelihood at STOR-i Masterclass
- Personalized Health: Challenges in Data Science at SMPGD 2017
- The Data Landscape at Defra Science Advisory Council: Data Sub Group
- Personalized Health: Challenges in Data Science at NIPS Workshop on Machine Learning for Health
- Computational Perspectives: Fairness and Awareness in the Analysis of Data at Fisher Trust, Royal Society and London Mathematical Society Joint Meeting
- Three Challenges for Open Data Science at Open Data Science Conference
- The Data Delusion: Challenges for Democratising Deep Learning at Deep Learning Summit, London, UK
- The Challenges of Data Science at European Network for Business and Industrial Statistics (ENBIS) 2016, Sheffield
- Fitting Covariance and Multioutput Gaussian Processes at GPSS, Sheffield
- Introduction to Gaussian Processes at GPSS, Sheffield
- Data Science: Where Computation and Statistics Meet? at Royal Statistical Society Conference, Manchester
- Communicating Machine Learning at Symposium on Communicating Machine Learning, Edinburgh
- Variational Compression and Deep Gaussian Processes at MLSS, Arequipa
- Probabilistic Dimensionality Reduction with Gaussian Processes at MLSS, Arequipa
- Introduction to Gaussian Processes at MLSS, Arequipa
- Introduction to Gaussian Processes II at MLSS, Arequipa
- Privacy and Learning at Workshop on Security, Workroom 2, Diamond Building, Sheffield
- Machine Learning and the Professions at Royal Society, London
- New Directions in Data Science at Data Science in Africa Workshop, UN Global Pulse, Kampala, Uganda
- Introduction to Data Science and Machine Learning at Data Science in Africa Summer School, Makerere University
- System Zero: What Kind of AI Have We Created? at Future of Humanity Institute, Oxford Martin School
- Machine Learning and the Future of Work at Cambridge Centre for Science and Policy
- What Kind of AI Have We Created? at A Pint of Science
- Data Efficiency and Machine Learning at Entropy Day, University of Sheffield
- Introduction to Gaussian Processes II at MLSS, Cadiz
- Introduction to Gaussian Processes at MLSS, Cadiz
- Beyond Backpropagation: Uncertainty Propagation at ICLR 2016, San Jaun, Puerto Rico
- Machine Learning with Gaussian Processes at Amazon Machine Learning Conference, Seattle
- Beyond Backpropagation: Uncertainty Propagation at Microsoft Research, New England, USA
- Variational Inference in Deep GPs at Microsoft Research, New England, USA
- Probabilistic Dimensionality Reduction at Facebook London, UK
- The Data Delusion: Challenges for Democratising Deep Learning at Deep Learning Summit, London, UK
- The Data Delusion at MARS Conference, Parker Palm Springs, Palm Springs, CA
- The Rise of the Algorithm: What is the AI that we have created? at Notre Dame High School, Sheffield
- The Rise of the Algorithm: What is the AI that we have created? at Birley Community College, Sheffield
- Future Debates: This House Believes an Artificial Intelligence will Benefit Society at British Science Association Future Debates, Coffee Revolution, University of Sheffield
- Machine Learning with Gaussian Processes at OxWaSP Symposium, University of Warwick
- What kind of AI have we created? at Sheffield Computer Science Welcome Event
- The Open Data Science Initiative at data@sheffield
- The Mechanistic Fallacy and Modelling How We Think at NIPS Workshop on Statistical Methods for Understanding Neural Systems
- Information Infrastructure for Health at ATI Scoping Workshop on the Data Analytics Pipeline, Edinburgh
- What Kind of Artificial Intelligence are we Creating? at Cognitive Science work in Progress Meeting, Sheffield
- Machine Learning Tutorial: Probabilistic Dimensionality Reduction II at Imperial College, U.K.
- What Kind of Artificial Intelligence have we Created? at The Data Hide, The Hide, Scotland Street, Sheffield
- Personalised Health and Gaussian Processes at Stratified Medical, 40 Churchway, London, NW1
- The Digital Oligarchy: Information, Knowledge and the Internet Era at Right First Time, ControlPoint Event
- Peer Review and The NIPS Experiment at MLPM Summer School, Museum of Science and Industry, Manchester, UK
- Deep Gaussian Processes at HIPS Group, SEAS, Harvard University
- Personalized Health with Gaussian Processes at Microsoft Research, New England
- Latent Force Models: Bridging the Divide between Mechanistic and Data Modelling Paradigms at MPI for Intelligent Systems, Stuttgart
- Gaussian Processes (Part III) at MLSS, Tübingen
- Gaussian Processes (Part II) at MLSS, Tübingen
- Gaussian Processes (Part I) at MLSS, Tübingen
- Panel Discussion at ICML Deep Learning Worshop, Lille, France
- Large Scale Learning in Gaussian Processes at Large-Scale Kernel Learning Workshop @ICML2015
- Deep Gaussian Processes at Deep Learning Workshop @ICML2015
- Personalized Health at Dedan Kimathi University, Nyeri, Kenya
- Regression at Data Science Africa School, Dedan Kimathi University, Nyeri, Kenya
- Introduction to Machine Learning and Data Science at Data Science Africa School, Dedan Kimathi University, Nyeri, Kenya
- Deep Gaussian Processes at 2nd Deep Learning Workshop, Edinburgh
- Deep Gaussian Processes at Computer Science Colloquium, NYU
- Deep Gaussian Processes at KTH Royal Institute of Technology, Sweden
- Deep Gaussian Processes at IDA Machine Learning Seminars, Linkoping, Sweden
- Deep Gaussian Processes at Mascot Num 2015, St Etienne, France
- Modelling in the Context of Massively Missing Data at Max Planck Institute, Tübingen
- The Data Farm at Westwoodside Primary School, Nether Gate, Westwoodside, Doncaster
- Data Science: A New Field or Just a Rebadging Exercise? at School of Mathematical Sciences, University of Nottingham
- Machine Learning Tutorial: Probabilistic Dimensionality Reduction at Imperial College, U.K.
- The Data Farm at Sheffield Festival of Engineering and Science, Jessop West Exhibition Space, Jessop West, University of Sheffield, 1 Upper Hanover Street, Sheffield S3 7RA
- Introduction to Gaussian Processes at MLSS, Sydney
- The NIPS Experiment at RADIANT Meeting, University of Zurich, Switzerland
- Deep Gaussian Processes at Instituto Italiano de Tecnologia, Genova, Italy
- Data Science: A New Field or Just a Rebadging Exercise? at Department of Statistics, University of Warwick
- Statistical Computing: Python at Royal Statistical Society, London, U.K.
- Approximate Inference in Deep GPs at Gatsby Computational Neuroscience Unit, University College London, U.K.
- Deep Gaussian Processes at UCL-Duke University Workshop on Sensing and Analysis of High-Dimensional Data
- Big Data and Open Data Science at UCLID Workshop, University of Lancaster, UK
- Flexible Parametric Representations of Non Parametric Models at Informatics Forum, University of Edinburgh, UK
- Visualizing Biological Data with Gaussian Processes at The Systems Biology Modelling Cycle, EBI, Hinxton, UK
- Gaussian Processes for Dynamic Modelling at The Systems Biology Modelling Cycle, EBI, Hinxton, UK
- What is Machine Learning? A Probabilistic Perspective (Part II) at MLSS, Reykjavik, Iceland
- What is Machine Learning? A Probabilistic Perspective (Part I) at MLSS, Reykjavik, Iceland
- Flexible Parametric Representations of Non Parametric Models at Statistical Machine Learning in Paris, France
- Applications of Gaussian Processes in Computational Biology at Institute Curie, Paris, France
- Modelling with Massively Missing Data at Facebook, Menlo Park, CA
- Flexible Parametric Representations of Non Parametric Models at Gatsby Computational Neuroscience Unit, University College London, U.K.
- Personalized Health with Gaussian Processes at Universidad Nacional de Colombia, Sede Manizales, Colombia
- Deep Gaussian Processes at Oxford University Statistics Department
- New Perspectives on Variational Approximations in Gaussian Processes: Modelling Data at University of Cambridge, Engineering Department
- Latent Variable Models with Gaussian Processes at Gaussian Process Winter School, Sheffield
- Fitting Covariance and Multi-output Gaussian Processes at Gaussian Process Winter School, Sheffield
- Introduction to Gaussian Processes at Gaussian Process Winter School, Sheffield
- Unravelling the Big Data Revolution at Maths Department, Teaching Away Day, University of Leeds
- Unravelling the Data Revolution with Machine Learning at Newcastle, Edinburgh, Cambridge and Sheffield academic Departments of Respiratory Medicine Meeting, Whitely Hall, Sheffield
- Personalized Health with Gaussian Processes at Disease Mapping Workshop, Leahurst
- Deep Health: Machine Learning for Personalized Medicine at E4L Away Day
- Probabilistic Approaches for Computational Biology and Medicine at Machine Learning for Personalized Medicine Summer School
- A Unifying Probabilistic Perspective on Spectral Approaches to Dimensionality Reduction at Microsoft Research, Cambridge
- Deep Gaussian Processes at Microsoft Research, Cambridge
- Deep Gaussian Processes at Natural Computing Applications Forum, University of Oxford
- Deep Health at Manchester and Sheffield Machine Learning Meetings
- Latent Force Models: Introduction at Latent Force Model Workshop, Sheffield
- Unsupervised Learning with Gaussian Processes at Gaussian Process Summer School, Sheffield
- Multioutput Gaussian Processes at Gaussian Process Summer School, Sheffield
- Introduction to Gaussian Processes at Gaussian Process Summer School, Sheffield
- Deep Gaussian Processes at University of Cambridge, Engineering Department
- How the Planets Affect Our Daily Lives: A Brief History of Uncertainty at King Edward's School, Sheffield
- Deep Learning: What is it and What are We doing About it? at University of Sheffield
- How the Planets Affect Our Daily Lives: A Brief History of Uncertainty at St Wilfrid's Primary School, Sheffield
- How the Planets Affect Our Daily Lives: A Brief History of Uncertainty at Birley Community College, Sheffield
- Variational Gaussian Processes at Max Planck Institute, Tübingen, Germany
- Deep Gaussian Processes at Max Planck Institute, Tübingen, Germany
- Deep Gaussian Processes at University College, London
- Deep Gaussian Processes at Aalto University, Finland
- Reproducible Research: Lessons from Machine Learning at RADIANT Kick-off Meeting, Manchester, UK
- Machine Learning and the Life Sciences: from Modelling to Medicine at Department of Infection and Immunity, University of Sheffield
- Life, the Universe and Machine Learning at St George's Church Lecture Theatre, University of Sheffield
- Model Based Target Identification from Expression Data at UCLA
- A Brief Introduction to Gaussian Processes at UCLA
- Bridging the Gap Between Computational Biology and Systems Biology at Pathologists Society Summer Meeting, Sheffield
- Kernels for Vector Valued Functions at ICML Workshop on ``Object, functional and structured data: towards next generation kernel-based methods''
- Everything You Want to Know About Gaussian Processes: Gaussian Process Regression at CVPR Tutorial, Providence, RI, USA
- Everything You Want to Know About Gaussian Processes: Multioutput Covariances and Mechanistic Models at CVPR Tutorial, Providence, RI, USA
- Gaussian Processes in Computational Biology Tutorial: Session 2 at BioPreDyn Workshop, CRG, Barcelona, Spain
- Gaussian Processes in Computational Biology Tutorial: Multioutput Gaussian Processes and Mechanistic Models at BioPreDyn Workshop, CRG, Barcelona, Spain
- Latent Force Models: Bridging the Divide between Mechanistic and Data Modelling Paradigms at Computer Science Department, University of Liverpool, U.K.
- What is Machine Learning? at La Palma, Canary Islands
- Nonlinear Probabilistic Dimensionality Reduction at La Palma, Canary Islands
- Spectral Approaches to Dimensionality Reduction at La Palma, Canary Islands
- Dimensionality Reduction: Motivation and Linear Models at La Palma, Canary Islands
- Latent Force Models: Combining the Mechanistic and Data Driven Modelling Paradigms at Rank Prize Workshop, Grasmere, Lake District
- Latent force models: Combining Probabilistic and Mechanistic Modelling at Robotics Research Group Seminar, Department of Engineering Science, University of Oxford
- A Unifying Review of Spectral Methods for Dimensionality Reduction at Robotics Research Group Tutorial, Department of Engineering Science, University of Oxford
- Model Based Target Identification from Expression Data at Cambridge Research Institute, Cancer Research UK
- at BioPreDyn Launch Workshop, CRG, Barcelona
- A Maximum Entropy Perspective on Spectral Dimensionality Reduction at Machine Learning @ CUED, University of Cambridge, U.K.
- Model Based Target Identification from Expression Data at Gene Expression Profiling Workshop, Liverpool, UK
- Between Systems and Data-driven Modeling for Computational Biology: Target Identification with Gaussian Processes at ABCD2011, Ravenna, Italy
- Latent Force Models at Bayes 250 Workshop, University of Edinburgh, U.K.
- Gaussian Processes and Probabilistic Models for Dimensionality Reduction at Schloss Dagstuhl, Germany
- Model Based Target Identification from Expression Data at Krebs Institute Symposium
- A unifying probabilistic perspective on spectral approaches to dimensionality reduction at Hausdorff Research Institute for Mathematics, Workshop on Manifold Learning, University of Bonn
- Advanced Use of Gaussian Processes at University of Siena, Italy
- Introduction to Gaussian Processes at Mathematics and Computer Science, University of Siena, Italy
- Latent Force Models at Mathematics and Computer Science, University of Exeter, U.K.
- Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Department of Computer Science, University of Loughgborough, U.K.
- A Unifying Probabilistic Perspective on Spectral Approaches to Dimensionality Reduction at ANC/DTC Seminar, School of Informatics, University of Edinburgh, U.K.
- Between Systems and Data-driven Modeling for Computational Biology: Target Identification with Gaussian Processes at SMPGD2010, Paris, France
- A Probabilistic Perspective on Spectral Dimensionality Reduction at Challenges of Data Visualization Workshop, NIPS 2010, Whistler
- A Probabilistic Perspective on Spectral Dimensionality Reduction at AAAI Fall Symposium on Manifold Methods, U.S.A.
- Latent Force Models at Dynamics Research Group Seminar, Mechanical Engineering, University of Sheffield, U.K.
- A Probabilistic Perspective on Spectral Dimensionality Reduction at Department of Statistics, Aalto University, Finland
- Bayesian approaches to Transcription Factor Target Identification at Course in Practical Systems Biology: Visualisation and Reverse engineering gene regulatory networks, EuroMediterranean University Centre of Ronzano, Bologna, Italy
- Making Implementations Available for the Research Community at Validation in Statistics and Machine Learning, Weierstrass Institute, Berlin, Germany
- Latent Force Models at Functional Phylogenies: Evolutionary Inference for Functional Data, University of Oxford, U.K.
- PRIB Tutorial: Gaussian Processes and Gene Regulation at PRIB2010, Radboud University, Nijmegen, Netherlands
- Between Systems and Data-driven Modeling for Computational Biology: Target Identification with Gaussian Processes at IBSB2010, Kyoto University, Japan
- Latent Force Models at Inference Group, Cavendish Laboratory, University of Cambridge
- Transfer Learning and Multiple Output Kernel Functions at NIPS 09 Workshop on Transfer Learning for Structured Data
- Latent Force Models at Disordered Systems Group, Department of Mathematics, King's College London
- Nonlinear Response in Gaussian Process Models of Transcription at Telethon Institute of Genetics and Medicine, Naples, Italy
- Model Based Target Identification from Gene Expression with Gaussian Processes at BioDN@work '09, Computational Biology \& Bioinformatics, University of Naples ``Federico II''
- Latent Force Models at Computer Science Colloquium, NYU, U.S.A.
- Latent Force Models at Google Research, New York, U.S.A.
- Model Based Target Identification from Gene Expression with Gaussian Processes at School of Public Health, Johns Hopkins University, U.S.A.
- Latent Force Modelling with Gaussian Processes at School of Mathematics and Statistics, University of Newcastle, U.K.
- Latent Force Models with Gaussian Processes at Inspire Workshop, Imperial College, U.K.
- Efficient Multiple Output Convolution Processes for Multiple Task Learning at Inf Workshop, University of Warwick, U.K.
- Dealing with High Dimensional Data with Dimensionality Reduction at Interspeech Tutorial, Brighton, U.K.
- Latent Force Models and Multiple Output Gaussian Processes at Statistics and Learning Interface Meeting, University of Manchester, U.K.
- Latent Force Models with Gaussian Processes at Pattern Recognition Applications Group, Department of Electrical and Electronic Engineering, University of Cagliari, Italy
- Non-linear Matrix Facorization with Gaussian Proceses at European Modern Massive Datasets Workshop, Denmark Techinical University, Copenhagen
- An Introduction to Systems Biology from a Machine Learning Perspective II at TISE Summer School, Tampere, Finland
- An Introduction to Systems Biology from a Machine Learning Perspective at TISE Summer School, Tampere, Finland
- Non-linear Matrix Factorization with Gaussian Processes at Learning Workshop, Clearwater, Florida
- Estimation of Multiple Transcription Factor Activities using ODEs and Gaussian Processes at Workshop on Learning and Inference in Computational and Systems Biology (LICSB)
- Python in Machine Learning at MLO Lunch, Kilburn 2.15
- GP-LVM for Data Consolidation at NIPS Workshop on Learning from Multiple Sources
- Latent Force Models with Gaussian Processes at Intelligent Systems Seminars, University of Bristol, U.K.
- Inference in Ordinary Differential Equations with Latent Functions through Gaussian Processes at RSS Manchester Local Group
- Dynamics with Gaussian Processes at Natural Computing Applications Forum, University of Sheffield, U.K.
- Ambiguity Modelling in Latent Spaces at Machine Learning for Multimodal Interaction, Utrecht, The Netherlands
- Latent Force Models with Gaussian Processes at Bayesian Research Kitchen, Grasmere, Lake District, U.K.
- Statistical inference in systems biology through Gaussian processes and ordinary differential equations at LICSB Workshop, University of Warwick, U.K.
- Statistical Inference in Systems Biology through Gaussian Processes and Ordinary Differential Equations at Max Planck Society, Ringberg Castle, Germany
- An Introduction to Systems Biology from a Machine Learning Perspective at Max Planck Society, Ringberg Castle, Germany
- Inferring Latent Functions with Gaussian Processes in Differential Equations at Department of Electronics and Computer Science, University of Southampton, U.K.
- Learning and Inference with Gaussian Processes: An Overview of Bayesian Inference and Gaussian Processes at Data Modelling Series, University of Sheffield, U.K.
- Human Motion Modelling with Gaussian Processes at Netwon Institute, Cambridge, U.K.
- Human Motion Modelling through Dimensional Reduction with Gaussian Processes at Hotel Golf, Bled, Slovenia
- TP1: Leveraging Complex Prior Knowledge in Learning at Hotel Golf, Bled, Slovenia
- Dimensionality Reduction at EPSRC Winter School, University of Sheffield, Sheffield, U.K.
- Exploiting Dimensional Dreduction In Modelling Of High Dimensional Distributions at
- Latent Variables, Differential Equations and Gaussian Processes at Microsoft Research, Cambridge, U.K.
- Modelling Transcriptional Regulation with Gaussian Processes at Parameter Estimation Workshop, Manchester Interdisciplinary Biocentre, University of Manchester, U.K.
- Towards Computational Systems Biology with a Statistical Analysis Pipeline for Microarray Data at Department of Molecular Biology and Biotechnology, University of Sheffield, U.K.
- Latent Variable Modelling with Gaussian Processes at Workshop on Probabilistic formulation of the inverse problem and application to image reconstruction, Neuroscience Research Institute, University of Manchester, U.K.
- Probabilistic Inference for Modelling of Transcription Factor Activity at Dept of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain
- Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Dept of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain
- Fast Sparse Gaussian Process Methods: The Informative Vector Machine at Dept of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain
- Hierarchical Gaussian Process Latent Variable Models at ICML, Corvallis, Oregon
- Probabilistic Inference for Modelling of Transcription Factor Activity at Gatsby Computational Neuroscience Unit, University College London, U.K.
- Gaussian Processes for Inference in Biological Interaction Networks at Exploring the Interface Between Mathematics and Bioscience, Manchester Interdisciplinary Biocentre, University of Manchester, U.K.
- Modelling Transcriptional Regulation with Gaussian Processes at Parameter Estimation in Systems Biology, School of Computer Science, University of Manchester, U.K.
- Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Neural Computing Research Group, Aston University, U.K.
- Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Google Research, New York, N.Y., U.S.A.
- Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, MA, U.S.A.
- Learning and Inference with Gaussian Processes: An Overview of Gaussian Processes and the GP-LVM at University of Manchester, Machine Learning Course Guest Lecture
- Learning and Inference with Gaussian Processes at Intel Research, Seattle, U.S.A.
- PUMA: Propagation of Uncertainty in Microarray Analysis at Max Planck Institute, Tübingen, Germany
- Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Erice Workshop on Mathematics and Medical Diagnosis, Sicily, Italy
- Local Distance Preservation in the GP-LVM through Back Constraints at International Conference on Machine Learning, Pittsburgh, U.S.A.
- Learning and Inference with Gaussian Processes at School of Computer Science, University of Manchester, U.K.
- A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription at Mathematical and Statistical Aspects of Molecular Biology
- Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model at Information Engineering, University of Cambridge, U.K.
- Computer Vision Reading Group: The Gaussian Process Latent Variable Model at Computer Vision Reading Group, Visual Geometry Group, Department of Engineering Science, University of Oxford, U.K.
- High Dimensional Probabilistic Modelling through Manifolds at University of Washington, Seattle, U.S.A.
- High Dimensional Probabilistic Modelling through Manifolds at Microsoft Research, Redmond, U.S.A.
- High Dimensional Probabilistic Modelling through Manifolds at Electronic Arts Speaker Series, University of British Columbia, Canada
- High Dimensional Probabilistic Modelling through Manifolds at Columbia University, New York, U.S.A.
- High Dimensional Probabilistic Modelling through Manifolds at IBM Thomas J Watson Research Center, New York, U.S.A.
- High Dimensional Probabilistic Modelling through Manifolds at Gatsby Computational Neuroscience Unit, University College London, U.K.
- High Dimensional Probabilistic Modelling through Manifolds at IDIAP Research Institute, Martigny, Switzerland
- High Dimensional Probabilistic Modelling through Manifolds at School of Computer and Communication Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at Max Planck Institute, Tübingen, Germany
- Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at Department of Electronics and Computer Science, University of Southampton, U.K.
- Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at Microsoft Research, Cambridge, U.K.
- Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at School of Computer Science, University of Manchester, U.K.
- Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at Institute for Adaptive and Neural Computation, University of Ediburgh, U.K.
- Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at Robotics Research Group, Department of Engineering Science, University of Oxford, U.K.
- Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at Sheffield Machine Learning Workshop, Sheffield, U.K.
- Probabilistic Non-linear Component Analysis through Gaussian Process Latent Variable Models at University of California, Berkeley, U.S.A.
- Bayesian Processing of cDNA Microarray Images through the Variational Importance Sampler at Microsoft Research, Redmond, U.S.A.
- Bayesian Processing of cDNA Microarray Images at The University of Sussex, Department of Cognitive Science, Bioinformatics and Vision Seminars
- Bayesian Processing of cDNA Microarray Images at The University of Manchester, Department of Computer Science, Bio-health sciences Seminars
- Particle Filters, Variational methods and Importance Sampling at Machine Learning and Perception Group, Microsoft Research, Cambridge, U.K.

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