arg58's list


2006-03-07 12:30: Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model (Dr Neil Lawrence, Computer Science, University of Sheffield)

2006-10-05 16:00: Mixture Models and the EM Algorithm (Professor Chris Bishop, Microsoft Research Cambridge)

2006-10-26 16:00: Gaussian Processes for Machine Learning (Ed Snelson, Gatsby Unit, UCL)

2006-11-09 16:00: Causality (Dr Ricardo Silva, Gatsby Unit, UCL)

2006-11-16 16:00: Expectation Propagation (Dr Tom Minka, Microsoft Research Cambridge)

2006-11-23 16:00: Advanced MCMC Methods (Iain Murray, Gatsby Unit, UCL)

2007-02-01 16:00: Robot Localisation and Mapping (Dr Paul M Newman, Oxford University)

2007-02-22 16:00: An Introduction to Non-parametric Bayesian Methods (Professor Zoubin Ghahramani, University of Cambridge)

2007-03-01 16:00: Dirichlet Processes and Hierarchical Dirichlet Processes (Dr Yee Whye Teh, Gatsby Unit, UCL)

2007-03-15 13:00: Logistic Regression with a Laplacian prior on the Eigenvalues: Convex duality and application to EEG classification (Ryota Tomioka (University of Tokyo / Fraunhofer FIRST))

2007-03-15 16:00: Bayesian Ranking (Ralf Herbrich, Microsoft Research Cambridge)

2007-09-13 13:00: Covariate Shift Adaptation: Supervised Learning When Training and Test Inputs Have Different Distributions (Masashi Sugiyama (Tokyo Institute of Technlogy))

2007-09-14 14:00: Geometric Algorithms for Linear Independent Component Analysis (Hao Shen, National ICT Australia and Australian National University, Canberra)

2007-09-19 14:00: Graph Kernels for Data Mining (Karsten Borgwardt, Machine Learning Group @ CUED)

2007-10-03 14:00: Optimal Spreading Sequences for Chaos-Based Communication Systems; Using CSK as a Case Study (Theodore Papamarkou, University of Warwick)

2007-10-17 14:00: Hidden Common Cause Relations in Relational Learning (Ricardo Silva (Statistical Laboratory))

2007-10-18 16:00: Group Theory and Machine Learning (Imre Risi Kondor, Gatsby Unit, UCL)

2007-10-25 16:00: Spectral Clustering (Arik Azran,  Machine Learning Group)

2007-11-01 16:00: Prequential Statistics (Philip Dawid, Statistical Laboratory, Cambridge)

2007-11-07 11:30: Optimal Control and Reinforcement Learning with Gaussian Process Models (Marc Deisenroth (University of Cambridge))

2007-11-07 14:00: An overview of covariance operators in Hilbert space, and their applications (Arthur Gretton)

2007-11-15 16:00: Machine Learning Applications / Challenges in Natural Language Parsing (Ted Briscoe, Computer Laboratory)

2007-11-21 14:00: Clinical data based optimal STI strategies for HIV: a reinforcement learning approach (Dr Guy-Bart Stan (University of Cambridge))

2007-11-22 16:00: Error Correcting Codes (David J.C. MacKay, Department of Physics)

2007-11-26 15:00: Gene Regulatory Network Inference: A Kernel-Based Learning Approach (Sandy Klemm, University of Cambridge)

2008-01-17 16:00: Graphical Models (Christopher M. Bishop, Microsoft Research, Cambridge)

2008-01-22 11:00: Sparse Gaussian Process in Disease Mapping (Jarno Vanhatalo, Helsinki University of Technology)

2008-01-23 14:00: Modeling Science: Topic models of Scientific Journals and Other Large Document Collections (David Blei, Computer Science, Princeton University)

2008-01-29 16:00: Reinforcement Learning (Peter Dayan, Gatsby Unit, UCL)

2008-02-06 14:00: Stable distribution and data sketching (Ioana Cosma, Department of Statistics, University of Oxford)

2008-02-14 16:00: Information Retrieval (Stephen Robertson, Microsoft Research Cambridge)

2008-02-18 11:30: Biomedical Image Search (Alex Ksikes (University of Cambridge))

2008-02-20 14:00: Expectation Propagation, Experimental Design for the Sparse Linear Model (Matthias Seeger (Max Planck Institute for Biological Cybernetics))

2008-02-21 14:00: Model selection and model order adaptation for clustering (Peter Orbanz (ETH Zurich))

2008-02-21 16:00: Statistical Machine Translation (Bill Byrne, Machine Intelligence Laboratory)

2008-02-28 16:00: Inductive Logic Programming (Stephen Muggleton (Imperial College London))

2008-03-06 11:00: A Bayesian approach to network modularity: inferring the structure and scale of modular networks (Jake Hofman (Columbia University))

2008-03-11 11:30:  Discriminative Methods with Structure (Simon Lacoste-Julien (Univ of California at Berkeley))

2008-03-12 13:00: Convergence analysis of the EM algorithm and joint minimization of free energy (Dr Shin-ichi Maeda (NARA Institute of Science and Technology))

2008-03-17 14:00: Learning quantum physics (Dr Gabor Csanyi (Dept of Engineering))

2008-04-02 14:00: Beam Sampling for Infinite Hidden Markov Models (Jurgen Van Gael)

2008-05-08 16:00: An Introduction to Statistical Learning Theory (Prof. John Shawe-Taylor (UCL))

2008-05-13 11:30: Talking with Robots: A Case Study in Architectures for Cognitive Robotics (Dr Jeremy Wyatt (University of Birmingham))

2008-05-14 14:00: Modeling Behaviour in Economic Games using Game-Theoretic POMDPs (Debajyoti Ray (Gatsby Unit, UCL))

2008-05-27 14:00: Sustainable Energy - without the hot air (David MacKay (Cavendish Laboratory))

2008-05-28 14:00: H-Infinity Clustering (Prof. Sam Roweis (Toronto))

2008-05-30 11:00: Identification of dynamical subpopulations, optimal experiment design and more (Alberto Giovanni Busetto, ETH Zurich)

2008-06-12 14:00: Assessing high-dimensional latent variable models (Dr Iain Murray (Toronto))

2008-06-16 14:00: Variational inference for partially observed diffusion processes (Dr. Cedric Archambeau (University College London))

2008-06-25 15:00: Bayesian analysis of complex biological systems (Dr Edo Airoldi (Princeton))

2008-07-15 11:30: Message-passing inference on graphical models (Simon Byrne (Cambridge))

2008-07-15 14:00: Nonparametric Bayesian Learning of Switching Dynamical Systems (Emily Fox (MIT))

2008-07-16 14:00: Double Feature: Optimal Precoding for MIMO and Divergence Estimation for Continuous Distributions (Dr Fernando Perez-Cruz (Princeton))

2008-09-09 14:00: Matrix Factorization and Relational Learning (Ajit Paul Singh (CMU))

2008-09-10 14:00: Non-negative matrix factorization with Gaussian process priors (Dr Mikkel N. Schmidt (Technical University of Denmark / Cambridge))

2008-09-15 14:00: Bayesian approaches to autonomous Bayesian real-time learning (Jo-Anne Ting (University of Southern California))

2008-09-17 14:00: Nonparametric Bayesian Natural Language Model Domain Adaptation: A Hierarchical, Hierarchical Pitman-Yor Process Language Model (Dr Frank Wood (UCL))

2008-09-23 14:00: Learning Bigrams from Unigrams (Andrew B. Goldberg (University of Wisconsin, Madison))

2008-09-26 15:00: A Bayesian approach to language learning (Dr Sharon Goldwater (Edinburgh))

2008-09-29 11:00: Spoken Dialogue Management (Sebastien Bratieres)

2008-10-08 13:30: Shared Segmentation of Natural Scenes using Dependent Pitman-Yor Processes (Dr Erik Sudderth (UC Berkeley))

2008-10-09 16:00: Foundations of Nonparametric Bayesian Methods (Part I) (Peter Orbanz (University of Cambridge))

2008-10-16 16:00: Foundations of Nonparametric Bayesian Methods (Part II) (Peter Orbanz (University of Cambridge))

2008-10-21 15:00: Context in human robot interaction (Thomas Kollar (MIT))

2008-10-22 13:00: Efficient Sequential Monte Carlo Inference for Kingman's Coalescent (Dr Dilan Gorur (Gatsby Unit, UCL))

2008-10-28 11:00: Foundations of Nonparametric Bayesian Methods (Part III) (Peter Orbanz (University of Cambridge))

2008-11-12 13:00: Consensus finding, exponential models and infinite rankings (Dr Marina Meila (University of Washington))

2008-11-25 14:00: Hardness Ratios (Yue Wu)

2008-11-27 16:00: Deep Networks for Vision (Prof Brendan Frey (University of Toronto))

2008-12-05 14:00: Mondrian Processes (Yee Whye Teh (Gatsby Unit, UCL))

2009-01-13 14:00: HMMs for Protein Sequencing from Mass Spectrometry Data (Dr Bernd Fischer (EBI))

2009-01-19 14:00: Probabilistic Graph Models for Debugging Software (Laura Dietz (Max Planck Institute for Computer Science, Saarbrücken))

2009-01-21 13:00: The Block Diagonal Infinite Hidden Markov Model (Tom Stepleton (CMU))

2009-01-30 13:00: Extending the Affinity Propagation Model (Inmar Givoni (University of Toronto))

2009-02-17 14:00: Stochastic control as an inference problem (Prof. Bert Kappen (University of Nijmegen))

2009-02-20 11:00: Generalization in Learning (Yevgeny Seldin (Hebrew University))

2009-02-26 16:00: An Introduction to Transcriptomics (Prof Brendan Frey (University of Toronto))

2009-03-06 15:00: Quasi-linear Sensor Management (Marco Huber (University of Karlsruhe))

2009-03-10 15:00: Shrinkage regression for multivariate inference with missing   data, with an application to portfolio balancing (Robert B. Gramacy (University of Cambridge))

2009-03-25 11:00: Learning from Measurements in Exponential Families (Percy Liang (University of California, Berkeley))

2009-05-26 13:00: Using gradient descent for optimization and learning (Nicolas Le Roux (Microsoft Research))

2009-07-28 14:00: Computable Probability Theory (Daniel Roy (MIT))

2009-08-27 11:00: Convex Variational Bayesian Inference for Large Scale Generalized Linear Models (Hannes Nickisch)

2009-09-18 10:00: CANCELLED (Prof. Eric Xing (CMU))

2009-09-29 14:00: Coconut: Optimizing computations for machine learning (Andrew Fitzgibbon (Microsoft Research Cambridge))

2009-10-09 13:00: Motor Skills Learning for Robotics (Jan Peters, Max Planck Institute for Biological Cybernetics in Tübingen)

2009-10-28 11:00: Information theoretic model selection in clustering (Joachim M Buhmann, Department of Computer Science, ETH Zurich)

2009-11-10 11:00: KL control theory and decision making under uncertainty (Bert Kappen ( Radboud University Nijmegen, The Netherlands))

2009-11-19 14:00: Gaussian Processes for Active Data Selection, Faults, Changepoints and Sensor Selection (Prof. Stephen Roberts (Oxford))

2009-11-20 14:00: Indian Buffet Processes with Power-law Behaviour (Yee Whye Teh (Gatsby Unit, UCL))

2009-12-02 11:00: Joint imputation and estimation of haplotype transition probabilities (Wolfgang Lehrach, Microsoft Research Cambridge)

2009-12-04 11:00: Optimal Tag Sets for Automatic Image Annotation (Sean Moran (Edinburgh))

2010-01-14 10:00: Machine Learning Course (4F13) (Zoubin Ghahramani and Carl Rasmussen (Cambridge))

2010-01-18 10:00: Visuospatial Reasoning (Stephanie Chan (MIT))

2010-01-19 11:00: CANCELLED: Learning Components for Human Sensing (Dr Fernando de la Torre (CMU))

2010-01-20 10:00: Machine Learning Course (4F13) (Zoubin Ghahramani and Carl Rasmussen (Cambridge))

2010-02-02 11:00: Mind Reading by Machine Learning: Optimal Experimental Design (Neil Houlsby (CUED))

2010-03-08 16:00: Stochastic Outlier Selection (Jeroen Janssens (Tilburg University  / University of Cambridge))

2010-03-09 11:00: Bayesian Inference in Networks of Queues (Dr Charles Sutton (Edinburgh))

2010-03-10 11:00: Title to be confirmed (Alex Davies (ANU))

2010-03-10 11:30: Subspace Codes for Adversarial Error-Correction in Network Coding (Azadeh Khaleghi (University of Toronto))

2010-03-10 12:30: Recursive CRFs for Scalable Vision (David Duvenaud (UBC))

2010-03-11 11:00: Making Sense of Data - A Research Agenda (Prof Bob Williamson (ANU and Scientific Director of NICTA))

2010-03-11 14:00: Dynamic Network Tomography: Model, Algorithm, Theory, and Application (Prof. Eric Xing (CMU))

2010-03-16 11:00: Slice sampling with latent Gaussian models (Dr Iain Murray (Toronto / Edinburgh))

2010-06-09 14:00: Parametric Bandits, Query Learning, and the Haystack Dimension (Prof. Michael Kearns (University of Pennsylvania))

2010-06-10 11:30: Structured Prediction Cascades (Dr. Ben Taskar (University of Pennsylvania))

2010-06-11 11:00: Message Passing In Centralized Database (Konstantina Palla (Edinburgh))

2010-06-11 14:00: Using topic models to help cure cancer (Prof Quaid Morris (Toronto))

2010-06-15 11:00: Sparse Factor Analysis Applied to Three Biological Problems (Barbara Engelhardt (University of Chicago))

2010-06-16 11:00: Natural Conjugate Gradient Learning for Fixed-Form Variational Bayes (Dr Antti Honkela (Aalto University School of Science and Technology, Finland))

2010-07-07 11:00: Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design (Andreas Krause (Caltech))

2010-07-12 11:30: Using transformed domains to sparsify Gaussian Processes (Dr Miguel Lázaro-Gredilla (Universidad Carlos III de Madrid))

2010-07-20 14:00: Scalable Parallel Computing with CUDA (James Balfour (NVIDIA))

2010-07-21 11:00: Learning Common Grammar from Multilingual Corpus / Online Multiscale Dynamic Topic Models (Dr. Tomoharu Iwata (NTT))

2010-07-26 15:00: Efficient Bayesian analysis of multiple changepoint models (Prof Paul Fearnhead (Lancaster))

2010-07-28 14:00: Creating structured and flexible models: some  open problems (Prof Andrew Gelman (Columbia University))

2010-07-29 16:30: Culture wars, voting and polarization: divisions and unities in modern American politics (Prof Andrew Gelman (Columbia University))

2010-08-25 14:00: On the Equivalence of Graph Cuts and Max-product Belief Propagation (Danny Tarlow (University of Toronto))

2010-09-20 11:00: Bayesian Inference with Kernels (Dr Arthur Gretton (UCL))

2010-09-30 11:00: Searching for Knowledge Instead of Web Pages (Gjergji Kasneci (Microsoft Research, Cambridge))

2010-11-05 13:30: Short talks: Mixed Cumulative Distribution Networks; Nonparametric Bayesian community discovery in social networks; Expectation Propagation for Dirichlet Process Mixture Models (Charles Blundell, Lloyd Elliot and Vinayak Rao, Gatsby Unit, UCL)

2010-11-11 11:00: CANCELLED (Dr Paolo Emilio Barbano (DAMTP))

2010-11-18 11:00: Crowdsourcing data modelling (Anthony Goldbloom (Kaggle))

2010-11-26 11:00: Non-Smooth-Norm Image Reconstruction from Noisy Data (Dr Paolo Emilio Barbano (DAMTP, University of Cambridge))

2010-11-30 11:00: Learning item trees for collaborative filtering with implicit feedback (Dr Andriy Mnih (Gatsby Unit, UCL))

2011-01-13 11:00: Novi Quadrianto (Novi Quadrianto, Australian National University)

2011-01-17 11:00: Machine learning in cancer research (a.k.a CRI meets CUED) (Dr Florian Markowetz (CRUK)  - lab visit)

2011-01-18 11:00: Universal Bayesian Agents: Theory and Applications (Prof. Marcus Hutter (ANU))

2011-01-25 11:00: Mining viral datasets (Dr Simon Frost (University of Cambridge))

2011-02-01 11:00: Differential Geometric MCMC Methods (Prof. Mark Girolami (UCL))

2011-02-25 11:00: Title to be confirmed (Yee Whye Teh (Gatsby Unit, UCL))

2011-03-04 10:30: Probabilistic matrix factorization for reconstruction of missing data (Dr Alexander Ilin (Aalto University))

2011-03-04 11:30: Expectation Propagation in Sparse Linear Models with Spike and Slab Priors (Dr José Miguel Hernández Lobato (Univ. Aut. Madrid))

2011-03-10 12:00: Challenges in implementing the Bayesian paradigm (Prof. Steve MacEachern (Ohio State))

2011-03-15 11:00: Exponential Conditional Volatility Models (Prof Andrew Harvey (Economics, Cambridge))

2011-03-21 11:00: Nonlinear Dynamics of Learning (Prof. Max Welling (UC Irvine))

2011-04-11 11:00: Characterization of the Ewens-Pitman family of random partitions by a deletion property and a de Finetti-type theorem for exchangeable hierarchies (Chris Haulk (UC Berkeley))

2011-05-24 11:00: An FX trading system using adaptive reinforcement learning (Professor Michael Dempster (Statistical Laboratory))

2011-06-03 11:00: Censored Exploration in Dark Pools (Prof. Michael Kearns (University of Pennsylvania))

2011-06-07 11:00: An FX trading system using adaptive reinforcement learning (Professor Michael Dempster (Statistical Laboratory))

2011-06-21 14:30: Bayesian regression and classification with multivariate sparsifying priors (Prof. Tom Heskes (Radboud University Nijmegen))

2011-07-04 11:00: Graphical Models for Bandit Problems (Kareem Amin (University of Pennsylvania))

2011-07-11 11:00: Beyond Keyword Search: Discovering Relevant Scientific Literature (Khalid El-Arini (Carnegie Mellon University))

2011-07-13 11:00: Some Practical Reflections on Graphical Models (Dr Charles Sutton (University of Edinburgh))

2011-07-28 11:30: Approximate Bayesian Inference for Large Scale Inverse Problems: A Computational Viewpoint (Prof. Matthias Seeger (EPFL))

2011-09-16 11:00: Exclusive Pólya Urns and their applications (Christian Steinruecken (University of Cambridge))

2011-09-19 15:00: Optimal Reinforcement Learning for Gaussian Systems (Philipp Hennig, Max Planck Institute for Intelligent Systems, Department of Empirical Inference, Tübingen, Germany)

2011-09-27 11:00: Factored Shapes and Appearances for Parts-based Object Understanding AND Transformation Equivariant Boltzmann Machines (Chris Williams, School of Informatics, University of Edinburgh)

2011-10-07 11:00: Machine Learning Markets (Dr Amos Storkey (Edinburgh))

2011-10-11 11:30: Bayesian Nonparametrics: Latent Feature and Prediction Models, and Efficient Inference (Piyush Rai (University of Utah))

2011-10-12 11:00: Not so naive Bayesian classification (Prof. Geoff Webb (Monash Univ))

2011-10-26 11:00: Variational Inference for Non-Conjugate Models (Dr Guillaume Bouchard (Xerox))

2011-11-16 15:00: A Maximum Entropy Perspective on Spectral Dimensionality Reduction (Prof Neil Lawrence (Sheffield))

2011-11-23 11:00: Efficient MCMC for Continuous Time Discrete State Systems (Vinayak Rao (UCL))

2011-11-28 11:00: Bayesian Quadrature for Prediction and Optimisation (Michael Osborne ( Oxford University))

2011-12-07 11:00: Scaling Machine Learning for the Internet (Prof. Alexander Smola (Yahoo!))

2012-02-23 12:30: Probabilistic computing: computation as universal stochastic inference, not deterministic calculation (Vikash K. Mansinghka (MIT))

2012-03-15 11:00: Infinite Structured Explicit Duration Hidden Markov Models (Jonathan Huggins (Columbia University))

2012-05-02 11:15: Non-parametric Bayesian Method and Maximum-A-Posteriori Inference in Statistical Machine Translation (Tsuyoshi Okita (Dublin City University))

2012-05-04 11:15: Past work and future interests: respectively, the scattering of Anyons and Monte Carlo methods (Alexander Matthews)

2012-05-08 11:00: Learning with nonparametric dependence and divergence estimation (Barnabas Poczos (Carnegie Mellon University))

2012-06-19 11:00: Discovery of Complex Behaviors through Contact-Invariant Optimization (Igor Mordatch (University of Washington))

2012-07-03 11:00: Structured Prediction using Linear Programming Relaxations (David Sontag (NYU))

2012-07-04 11:00: Non-parametric Bayesian Learning of User Preferences: Elicitation, Sparsification and Beyond (Edwin Bonilla (NICTA/ANU))

2012-07-04 14:00: Fast Gaussian process learning for regression, semi-supervised classification, and multiway analysis (Prof Alan Qi (Purdue U))

2012-07-27 11:00: Human Behavior Classification with Infinite Hidden Conditional Random Fields (Konstantinos Bousmalis and Stefanos Zafeiriou (Imperial College))

2012-08-03 14:00: Thermodynamics as a Theory of Decision-Making with Information Processing Costs (Pedro Ortega (Max Planck Institute for Intelligent Systems))

2012-08-08 14:00: Deep learning for vision: a case study for visual textures,  and some thoughts on a general framework (Prof. Chris Williams ( School of Informatics, University of Edinburgh))

2012-08-24 11:00: Frank-Wolfe optimization insights in machine learning (Simon Lacoste-Julien (INRIA, ENS, Paris))

2012-09-12 16:00: Efficient Sampling with Kernel Herding (Yutian Chen (University of California at Irvine) - talk given by videolink)

2012-09-24 11:00: Multi-Label Learning with Millions of Categories (Manik Varma (Microsoft Research India))

2012-09-25 15:00: Convergent and Scalable Algorithms for Expectation Propagation Approximate Bayesian Inference (Matthias Seeger, EPFL)

2012-09-26 11:00: Compressed Sensing Applications in Functional Magnetic Resonance Imaging (Christine Law (Oxford University))

2012-10-16 16:00: Distributed, Real-Time Bayesian Learning in Online Services (Ralf Herbrich (Facebook))

2012-10-22 11:00: Probabilistic methods for biomolecular structure simulations (Jes Frellsen (University of Copenhagen))

2012-10-25 14:30: Identification of causal effects (Nevena Lazic)

2012-11-15 11:30: Learning of Milky Way Model Parameters Using Matrix-variate Data in a New Gaussian Process-based Method (Dr Dalia Chakrabarty (University of Warwick))

2012-11-20 13:00: The combinatorial structure underlying a beta processes is that of a continuum of Blackwell-MacQueen urn schemes (Dr Daniel Roy (University of Cambridge))

2013-01-30 11:00: Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables  (Nevena Lazic (Microsoft Research Cambridge))

2013-02-22 11:00: Modelling Reciprocating Relationships with Hawkes Processes (Charles Blundell (Gatsby Unit, UCL))

2013-02-27 11:30: Feature allocations, probability functions, and paintboxes (Tamara Broderick (UC Berkeley))

2013-03-08 11:00: Using Context and Insight for the Analysis of LittleData? (Philipp Moritz (U Cambridge))

2013-03-11 11:30: An application of HDP And IBP for stream-based action recognition and high dimensional data (Ava Bargi : University of Technology, Sydney)

2013-03-25 11:00: Fast Variational Inference in the Conjugate Exponential Family (James Hensman, The Sheffield Institute for Translational Neuroscience)

2013-04-08 11:00: Non-parametric Bayesian Chromatin State Segmentation  (Will Allen (University of Cambridge and MRC-LMB))

2013-04-15 11:00: Bayesian nonparametric methods for non-exchangeable data (Nick Foti (Dartmouth College))

2013-04-30 11:00: Approaches to statistical modeling of network data (Maxim Nazarov (Bocconi University))

2013-05-01 15:00: Deep Gaussian Processes (Prof. Neil Lawrence (Sheffield))

2013-05-09 16:00: Matrix Concentration Inequalities via the Method of Exchangeable Pairs (Professor Michael I Jordan (UC Berkeley))

2013-05-22 10:20: Google's Approach to Building Relationships with Universities: Presentation and Talk by Dr David J Harper (Dr David J Harper )

2013-06-06 13:00: The combinatorial structure of conditionally i.i.d. negative binomial processes directed by a beta process (Creighton Heaukulani (University of Cambridge))

2013-07-01 11:00: Applied statistical genetics and next-generation association studies (Dr. Eleftheria Zeggini and Dr Ioanna Tachmazidou (Sanger Institute))

2013-07-01 11:00: Applied statistical genetics and next-generation association studies (Dr. Eleftheria Zeggini and Dr Ioanna Tachmazidou (Sanger Institute))

2013-07-31 14:00: Recursive Deep Learning for Modeling Semantic Compositionality (Richard Socher -  Stanford University)

2013-08-02 11:00: Annealing Between Distributions by Averaging Moments (Chris Maddison (U Toronto))

2013-08-06 11:00: Higher Order Learning for Classification in Emergency Situations (Hannah Pauline Keiler (Columbia University and DIMACS))

2013-08-08 11:00: Non-Parametric Conditional Random Fields in Computer Vision and Image Processing (Jeremy Jancsary (Microsoft Research Cambridge))

2013-09-13 11:00: Clustering Based on Predictive Variances in Gaussian Process Regression Models (Dr Hyun-Chul Kim )

2013-09-27 11:00: CANCELLED: Local Deep Kernel Learning for Efficient Non-linear SVM Prediction (Manik Varma (Microsoft Research India))

2013-10-10 11:00: Dissecting genotype to phenotype relationships (Oliver Stegle (EMBL-European Bioinformatics Institute))

2013-10-11 12:00: Nonparametric Bayesian statistics with exchangeable random structures (Daniel Roy)

2013-11-06 13:30: Contrastive Learning Using Spectral Methods (James Zou (Harvard))

2014-01-10 11:00: Sparse discriminative latent characteristics for predicting cancer drug sensitivity (David Knowles (Stanford University))

2014-01-15 11:00: Anglican; Particle MCMC inference for Probabilistic Programs (Jan-Willem van de Meent (Columbia University))

2014-02-03 11:00: Frequentist coverage of adaptive nonparametric  Bayesian credible sets (Botond Szabo (Eindhoven University of Technology))

2014-02-12 11:00: Probabilistic machine learning for knowledge extraction from videos and text (Kevin Murphy (Google))

2014-02-17 11:00: Bayesian Nonparametric Model for Power Disaggregation (Isabel Valera (University Carlos III in Madrid))

2014-02-20 10:00: Bayesian nonparametrics: Dependency and Constraint Modeling (Changyou Chen (ANU))

2014-02-21 14:00: Particle filters and curse of dimensionality (Patrick Rebeschini (Princeton))

2014-02-24 11:00: Generative probabilistic programming: applications and new ideas (Yura Perov (Oxford))

2014-02-25 09:00: Cambridge - Tuebingen PhD Applicant Talks (via Skype) (Speaker to be confirmed)

2014-02-28 11:00: Bayesian canonical correlation analysis (Seppo Virtanen (Aalto University))

2014-02-28 12:00: Parameter estimation in deep learning architectures: Two new insights. (Prof. Nando de Freitas (Oxford))

2014-03-04 11:00: Bayesian nonparametric dynamic-clustering and genetic imputation (Lloyd Elliott (Gatsby Unit, UCL / Oxford))

2014-03-11 11:00: Learning to Learn for Structured Sparsity (Nino Shervashidze (INRIA))

2014-03-14 16:00: Alpha-Stable Poisson-Kingman Processes: Some Applications and Methodologies (Yee Whye Teh, University of Oxford)

2014-03-17 11:30: Matrix Means, Distances, Kernels, and Geometric Optimization (Suvrit Sra (Max-Planck Institute for Intelligent Systems and CMU))

2014-03-28 11:00: Policy Evaluation with Temporal Differences (Christoph Dann (Technische Universität Darmstadt))

2014-04-02 11:00: Machine Learning and Order Book Dynamics (Tristan Fletcher )

2014-04-02 13:30: Probabilistic computing for Bayesian inference (Vikash K. Mansinghka (MIT))

2014-04-04 11:00: Probabilistic computing applications: BayesDB and stochastic digital circuits (Vikash K. Mansinghka (MIT))

2014-05-08 11:00: Stable Poisson-Kingman species sampling priors generated by general ordered size biased generalized gamma mixing distributions  (Prof. Lancelot James (HKUST))

2014-05-14 11:00: Density Estimation in Infinite Dimensional Exponential Families (Bharath Sriperumbudur (University of Cambridge))

2014-05-16 10:30: Bayesian inference for integer-valued Lévy processes with Non-Gaussian Ornstein-Uhlenbeck volatility modelling (Andrea Cremaschi (Kent))

2014-05-20 11:00: Practical Machine Learning at Facebook. Examples and Lessons Learnt. (Joaquin Quinonero Candela (Facebook))

2014-06-02 11:00: Unifying logic and probability: A "New Dawn" for Artificial Intelligence? (Professor Stuart Russell (UC Berkeley))

2014-06-03 11:00: Bayesian monitoring for the Comprehensive Nuclear-Test-Ban Treaty (Professor Stuart Russell (UC Berkeley))

2014-06-10 11:00: Active Learning of Linear Embeddings for Gaussian Processes (Roman Garnett, University of Bonn)

2014-07-01 11:00: Scalable Deep Gaussian Processes (James Hensman (University of Sheffield))

2014-07-02 11:00: Implicit Representation Networks (David Barber (University College London))

2014-07-16 11:00: Gaussian process regression on graphs (Peter Sollich (King's College London))

2014-08-11 11:00: On the Bethe approximation (Adrian Weller (Columbia University))

2014-09-11 11:00: Probabilistic Numerics - a snapshot of an emerging community (Philipp Hennig (Max Planck Institute for Intelligent Systems, Tübingen))

2014-09-12 11:00: Unsupervised Many-to-many Object Matching (Dr Tomoharu Iwata (NTT))

2014-09-17 11:00: Local Deep Kernel Learning for Efficient Non-linear SVM Prediction (Manik Varma (Microsoft Research India))

2014-09-24 11:00: A marginal sampler for σ-Stable Poisson-Kingman mixture models (Maria Lomeli-Garcia (Gatsby Unit, UCL))

2014-09-25 11:00: New Methods in Bayesian Optimization for Machine Learning (Jasper Snoek (Harvard University))

2014-10-21 14:30: Optimal control and optimal sampling: A statistical physics perspective. (Bert Kappen, Radboud University Nijmegen, and UCL London)

2014-10-23 14:00: A Tutorial on Probabilistic Programming (Prof. Frank Wood (Oxford))

2014-11-14 11:00: Bayesian modeling for high-level real nursing activity recognition using accelerometers (Prof. Naonori Ueda (Director Machine Learning and Data Science, NTT Labs))

2014-11-26 11:00: Oracle Variational Inference (James McInerney (Columbia University))

2014-11-28 10:30: Machine Translation with LSTMs (Ilya Sutskever (Google))

2014-12-15 10:00: The Blended Paradigm:  A Bayesian Approach to Handling Outliers and Misspecified Models  (Prof. Steven MacEachern (Ohio State University))

2015-01-23 11:00: Experiments with Non-parametric Topic Models (Prof. Wray Buntine (Monash University))

2015-01-29 12:00: Latent Branching Trees (Dr Theodore Kypraios (University of Nottingham))

2015-02-11 11:00: Orthologous networks in biological systems (Dr Christopher Penfold (Warwick))

2015-02-24 11:00: A* Sampling (Chris Maddison  (U Toronto))

2015-03-02 11:00: Probabilistic approaches to understanding bird conversations (Dan Stowell (Queen Mary University of London))

2015-03-04 11:00: Population Inference for Functional Brain Connectivity (Genevera I. Allen (Rice University))

2015-03-12 11:00: An introduction to the Mondrian Process (Matej Balog (Oxford))

2015-03-19 11:00: Evolutionary dynamics in a continuous public goods game (Matthias Bauer )

2015-04-15 11:00: Inference for infinite mixture models and Gaussian Process mixtures of experts using simple approximate MAP Inference (Alexis Boukouvalas (Aston University))

2015-04-23 11:00: Title to be confirmed (Tuan Anh Le (Oxford))

2015-04-30 11:00: Machine Learning for Quantitative Finance: A collaboration between the Cambridge Machine Learning Group and Cambridge Capital Management (Creighton Heaukulani, Matt Hoffman, Zoubin Ghahramani, and Andrew Baxter, )

2015-05-06 11:00: Direction-Only Optimisation for Neural Networks (Mark Rowland (Cambridge))

2015-05-12 11:00: Probabilistic numerics: treating numerical computation as learning, or; it's Bayes all the way down (Michael Osborne (Oxford University))

2015-06-02 11:00: Do Deep Nets Really Need to be Deep? (Dr Rich Caruana (Microsoft))

2015-06-08 17:00: Mean Field Approaches to Two Sequential Decision Making Problems. (Ramki Gummadi)

2015-06-09 11:00: Structural Markov laws / Geometry and HMC (Dr Simon Byrne)

2015-06-16 11:00: Unsupervised Learning with Latent Variable Models (Zhenwen Dai)

2015-06-18 11:00: Unsupervised Learning with Latent Variable Models (Zhenwen Dai)

2015-06-24 11:00: Random Function Classes for Machine Learning (Prof. Alex Smola (CMU))

2015-06-25 11:00: Deep Learning (Professor Geoffrey Hinton FRS (U. Toronto and Google))

2015-06-29 11:00: Deep Gaussian processes and variational propagation of uncertainty (Andreas Damianou - Sheffield University)

2015-06-29 15:00: Explaining Non-Linear Classifier Decisions with application to Deep Learning (Prof Klaus-Robert Müller (TU Berlin))

2015-06-30 11:00: Random Function Classes for Machine Learning (Prof Alexander Smola, Carnegie Mellon University)

2015-07-01 11:00: Convex and non-convex worlds in machine learning (Anna Choromanska (New York University))

2015-07-02 11:00: Extreme Classification: A New Paradigm for Ranking & Recommendation (Manik Varma (Microsoft Research India))

2015-07-03 12:30: Modeling Confounding by Half-Sibling Regression (Prof Bernhard Schölkopf (MPI Tuebingen))

2015-07-16 11:00: A new MCMC hybrid scheme for Poisson-Kingman Bayesian Nonparametric mixture models (Maria Lomeli-Garcia, UCL)

2015-07-17 11:00: Rich Component Analysis (James Zou (Microsoft Research New England))

2015-07-20 11:00: Gradient-based hyperparameter optimization through reversible learning (Dr David Duvenaud (Harvard))

2015-07-27 15:00: MCMC for non-linear state space models using ensembles of latent sequences (Alex Shestopaloff (University of Toronto))

2015-07-29 11:00: Scalable Gaussian Processes for Scientific Discovery (Dr Andrew Wilson, Carnegie Mellon University)

2015-08-27 15:00: The limits of MAP inference by MWSS on perfect graphs (Dr Adrian Weller (MLG, University of Cambridge))

2015-09-07 11:00: Information-Theoretic Bounded Rationality (Pedro A. Ortega (University of Pennsylvania))

2015-09-09 11:00: Training and Understanding Deep Neural Networks for Robotics, Design, and Perception (Jason Yosinski (Cornell))

2015-09-10 11:00: Belief and Truth in Hypothesised Behaviours (Stefano V. Albrecht -  School of Informatics at The University of Edinburgh)

2015-09-14 11:00: Convex Factorization Machines (Mathieu Blondel (NTT Communication Science Laboratorie))

2015-09-14 12:00: A-Star Sampling Review (Chris Maddison  (U Toronto))

2015-09-15 10:30: Higher Order Fused Regularization for Supervised Learning with Grouped Parameters (Koh Takeuchi (NTT Communication Science Laboratories))

2015-09-16 11:00:  Harmonic Exponential Families and Group-Equivariant Convolution Networks (Taco Cohen (University of Amsterdam))

2015-09-25 11:00: Efficient multi-task Gaussian process models for genome-wide association studies (Francesco Paolo Casale, European Bioinformatics Institute)

2015-10-14 11:00: Efficient Inference and Learning with Intractable Posteriors? Yes, Please. (Diederik P. Kingma (University of Amsterdam))

2015-10-15 11:00: Meta-Bayesian Analysis (Prof. Daniel Roy (University of Toronto))

2015-11-11 12:00: Inference of a partially observed kinetic Ising model (Dr Yasser Roudi (Kavli Inst, Trondheim & Inst for Advanced Study, Princeton))

2015-12-16 11:00: General Reinforcement Learning (Jan Leike (Australian National University))

2016-02-19 11:30: A more Automated Statistician (David Janz, Oxford University)

2016-03-09 14:00: The geometry of uncertainty (Fabio Cuzzolin, Head of AI and vision, Oxford Brookes University)

2016-03-16 14:00: Unsupervised Risk Estimation with only Structural Assumptions (Jacob Steinhardt (Stanford University))

2016-04-05 11:00: Inference and Learning in the Anglican Probabilistic Programming System (Jan-Willem van de Meent (Oxford))

2016-05-13 11:00: MLE-Struct: Bethe Learning of Graphical Models (Kui Tang, Columbia University)

2016-05-24 14:00: Sum-Product Networks for Probabilistic Modeling (Robert Peharz (TU Graz))

2016-05-27 11:00: Towards Weaker Supervision and Simpler Pipelines in Speech Recognition  (Gabriel Synnaeve)

2016-06-02 09:30: Turing: Rejuvenating Probabilistic Programming in Julia (Hong Ge (University of Cambridge))

2016-06-02 10:30: Novel MCMC and SMC schemes for Poisson-Kingman Bayesian Nonparametric mixture models (Maria Lomeli (University of Cambridge))

2016-06-02 11:30: Variational inference for scalable Gaussian process approximations (Alexander Matthews (University of Cambridge))

2016-06-03 14:00: Automated Reasoning and AI for Large Formal Mathematics (Josef Urban)

2016-06-07 09:30: Approximation strategies for structure learning in Bayesian networks  (Teppo Niinimäki (Helsinki))

2016-06-07 10:30: Developments in Exact Inference in Graphical Models (Stephen Pasteris (UCL))

2016-06-09 11:00: Modeling the Dynamics of Online Learning Activity (Isabel Valera )

2016-06-14 10:00: Distributed stochastic optimization for deep learning (Sixin Zhang (NYU))

2016-06-14 11:00: Learning Task Relations in Multi-Task Learning (Yu Zhang (Hong Kong University of Science and Technology))

2016-06-14 15:00: A modular architecture for Unicode text compression (Adam Gleave (University of Cambridge))

2016-07-12 11:00: Scaling and Generalizing Approximate Bayesian Inference (Prof. David Blei (Columbia University))

2016-07-21 11:00: Structured Dynamic Graphical Models & Scaling Multivariate Time Series Methodology (Professor Mike West (Statistical Science, Duke University))

2016-07-25 11:00: Discriminative Embeddings of Latent Variable Models for Structured Data (Prof Le Song (Georgia Tech))

2016-07-29 11:00: Interpretable and interactive machine learning (Dr Been Kim)

2016-08-08 11:00: Inference as Learning (George Papamakarios (University of Edinburgh))

2016-09-02 11:00: Data Driven Discrete Time Modeling of Continuous Time Nonlinear Systems: Problems, Challenges, Success Stories (Johan Schoukens, Vrije Universiteit Brussel)

2016-09-08 11:00: Learning with Memory Embeddings (Professor Volker Tresp (Ludwig Maximilian University of Munich))

2016-09-12 11:00: Dynamic Models for Health Data (Professor Katherine A Heller (Duke University))

2016-09-13 11:00: Multiresolution Matrix Factorization (Prof Risi Kondor (U Chicago))

2016-10-06 11:00: Moment matching for latent variable models: from ICA to LDA and CCA (Professor Francis Bach (INRIA, ENS))

2016-10-20 10:00: Probabilistic modeling for position and orientation estimation using inertial sensors (Manon Kok, Department of Electrical Engineering, Linköping University)

2016-11-02 11:00: Robots learning on the move: deep learning from lots of demonstration (Dushyant Rao, Oxford Robotics Institute)

2016-11-03 11:00: Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models  (Tomoharu Iwata - Learning and Intelligent Systems Research Group of NTT Communication Science Laboratories, Kyoto, Japan)

2016-11-07 11:00: A talk in two parts: (1) AI Neuroscience: How much do deep neural networks understand about the images they classify? (2) Robots that can adapt like animals.  (Prof. Jeff Clune (U Wyoming))

2016-11-17 11:00: Probabilistic Numerical Computation: A New Concept? (Prof. Mark Girolami (University of Warwick))

2016-11-22 11:30: Rejection Sampling Variational Inference (Francisco J. R. Ruiz (Columbia University & University of Cambridge))

2016-11-24 11:00: A tale of P-matrices and TripleSpinners - the unreasonable effectiveness of structured models in nonlinear embeddings (Krzysztof Choromanski, Google NY)

2016-12-01 11:00: Learning with limited supervision (Stefano Ermon, Stanford)

2016-12-13 11:00: Variational autoencoders with latent graphical models (Prof David Duvenaud (University of Toronto))

2016-12-14 15:00: Hierarchical Multi-Agent Reinforcement Learning through Communicative Actions for Human-Robot Collaboration (Elena Corina Grigore (Yale University))

2016-12-15 11:00: Bayesian optimality and frequentist extended admissibility are equivalent in saturated models (Daniel Roy (University of Toronto))

2017-01-24 11:00: Julia: Introduction and new developments (Dr Simon Byrne (Julia Computing))

2017-01-25 15:00: Variational Bayes In Private Settings (Mijung Park, University of Amsterdam)

2017-01-31 13:30: Deep Learning Book - Meeting 1 - Deep Feedforward NNs (Group Discussion)

2017-02-07 13:30: Deep Learning Book - Meeting 2 - Regularization (Group Discussion)

2017-02-10 11:00: Bayesian optimisation in many dimensions with bespoke probabilistic programs (Valentin Dalibart)

2017-02-14 13:30: Deep Learning Book - Meeting 3 - Optimization (Group Discussion)

2017-02-23 13:30: Deep Learning Book - Meeting 4 - Convolutional Networks (Group Discussion)

2017-02-23 15:00: Cooperative Inverse Reinforcement Learning (Dylan Hadfield-Menell, UC Berkeley)

2017-03-02 13:30: Deep Learning Book - Meeting 5 - ResNets and DenseNets (Group Discussion)

2017-03-09 13:30: Differential Privacy Tutorial (Alex Matthews and John Bradshaw, University of Cambridge)

2017-03-10 11:00: Control, inference and learning (Prof. dr. H.J. (Bert) Kappen (Radboud University Nijmegen))

2017-03-14 11:00: Differentially Private Bayesian Learning (Dr Antti Honkela, University of Helsinki)

2017-03-14 13:30: Deep Learning Book - Meeting 6 - Recurrent Neural Networks (Group Discussion)

2017-03-15 11:00: Bayesian Optimization for Probabilistic Programs (Tom Rainforth, University of Oxford)

2017-03-21 13:30: Deep Learning Book - Meeting 7 - Neural Turing Machines & Conditional Random Fields as RNNs (Group Discussion)

2017-04-04 13:30: Deep Learning Book - Meeting 8 - Autoencoders (Group Discussion)

2017-04-11 13:30: Deep Learning Book - Meeting 9 - Representation Learning (Group Discussion)

2017-05-25 11:00: On Different Distances Between Distributions and Generative Adversarial Networks (Martin Arjovsky)

2017-07-03 11:00: Future technology: machine learning using memristors networks (Francesco Caravelli (LANL))

2017-07-03 11:00: Future technology: machine learning using memristors networks (Francesco Caravelli (LANL))

2017-07-06 11:00: A simple neural network module for relational reasoning (David Barrett, DeepMind)

2017-07-20 14:00: Understanding Black-box Predictions via Influence Functions (Pang Wei Koh, Stanford University)

2017-09-08 11:00: Accelerating computation of SVM and DNN by binary approximation (Hironobu Fujiyoshi (Chubu University) )

2017-09-08 11:00: Towards User-Friendly Image Inpainting: Learning-to-Rank based Image Quality Assessment for Image Inpainting (Mariko Isogawa (NTT Media Intelligence Laboratories) )

2017-09-08 11:00: Deep learning for autonomous driving  (Takayoshi Yamashita (Chubu University) )

2017-09-12 11:00:  The Grammar Variational Autoencoder & Counterfactual Fairness (Dr Matt Kusner)

2017-09-14 11:00: Unbiased Estimation of the Eigenvalues of Large Implicit Matrices (Professor Ryan Adams, Princeton)

2017-09-26 15:00: A Bayesian Treatment for Uncertainty -- and its application in health care  (Dr Cheng Zhang)

2017-10-13 14:00: Transfer Learning for NLP (Sebastian Ruder, INSIGHT Centre)

2017-10-19 11:00: Nonlinear ICA using temporal structure: a principled framework for unsupervised deep learning (Prof. Aapo Hyvarinen)

2017-11-01 11:00: Targeted Disclosure to Support Auditing and Accountability for Automated Decision-making (Joshua Kroll)

2017-11-17 11:00: Towards true end-to-end learning & optimization (Dr Frank Hutter)

2017-11-24 11:00: Learning to Learn without Gradient Descent by Gradient Descent (Yutian Chen, DeepMind)

2017-11-27 11:00: Backprop through the Void: Optimizing Control Variates for Black-Box Gradient Estimation. (Geoff Roeder (University of Toronto))

2017-12-01 11:00: AI for Inclusive Finance (Alan Qi and Le Song)

2017-12-13 11:00: Bayesian Generative Adversarial Networks (Professor Andrew Wilson, Cornell University)

2017-12-13 13:30: Variational inference for some models with Polya-Gamma latent variables and Gaussian process priors (Manfred Opper, TU Berlin)

2018-02-16 11:00: Lipschitz Global Optimization (Professor Yaroslav D Sergeyev, Universita della Calabria)

2018-03-06 11:00: Structure in tensor-variate data: a trivial byproduct of simpler phenomena? (John P. Cunningham)

2018-04-15 11:00: Algorithmic Glass Ceiling in Social Networks (Ana Stoica, Columbia University)

2018-04-17 11:00: A Bayesian Perspective on Generalization and SGD (Dr. Samuel L. Smith, Google Brain)

2018-05-09 11:00: Algorithmic Glass Ceiling in Social Networks (Ana Stoica, Columbia University)

2018-05-23 11:30: Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features (Julius von Kugelgen)

2018-06-14 11:00: Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness (Prof. Michael Kearns (University of Pennsylvania))

2018-06-21 11:00: Structured deep models: Deep learning on graphs and beyond (Thomas Kipf (Uni of Amsterdam))

2018-07-04 11:00: Variance in Policy Gradient methods and Learning Sequential Latent Variable Models (George Tucker, Google Brain)

2018-07-16 11:00: Fast yet Simple Natural-Gradient Variational Inference in Complex Models (Emtiyaz Khan, team leader (equivalent to Full Professor) at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo)

2018-08-24 11:00: Constraint-based causal Discovery from NOnstationary/heterogeneous Data (CD-NOD) (Biwei Huang, CMU)

2018-09-10 11:00: Interpretability - the myth, questions, and some answers (Been Kim, Google Brain)

2018-10-11 11:00: Causal Inference for Treatment Effects: A Theory and Associated Learning Algorithms (Mihaela van der Schaar )

2018-12-18 10:30: Gaussian processes for inferring latent functions in complex data models (Martin Tegner)

2019-01-17 11:00: Fairness for Sequential Decision Making Algorithms (Hoda Heidari)

2019-01-25 11:00: Observation and Intervention Incentives in Causal Influence Diagrams: Towards an Understanding of Powerful Machine Learning Systems (Tom Everitt (DeepMind))

2019-01-31 13:30: Gauge Equivariant Convolutional Networks on Manifolds (Taco Cohen)

2019-02-01 12:30: High-dimensional dynamics of generalization error in neural networks: implications for experience replay (Dr. Andrew Saxe, University of Oxford)

2019-02-14 11:00: Online Meta-Learning (Massimiliano Pontil, University College London)

2019-02-20 13:45: Generative models for few-shot prediction tasks (Marta Garnelo (Google DeepMind))

2019-03-14 14:00: The K-FAC method for neural network optimization (James Martens, Google Deep Mind)

2019-03-27 11:00: Robust machine learning for causal inference in health care (David Sontag, MIT)

2019-04-04 16:00: Global model explainability via aggregation (Umang Bhatt, CMU)

2019-04-08 11:00: NeVAE: A Deep Generative Model for Molecular Graphs (Abir De, Max Planck Institute for Software Systems)

2019-05-07 11:00: Nonparametric Generative Modeling via Optimal Transport and Diffusions with Provable Guarantees (Umut Şimşekli, Télécom Paristech)

2019-05-17 11:00: Why Do We Need New Hardware & Software for Machine Intelligence? (Victoria Rege, Scott Griffiths)

2019-06-11 15:00: Information Consumption on Social Media: Efficiency, Trust, and Divisiveness (Reza Babaei)

2019-06-20 11:00: Deep Neural Networks: A Nonparametric Bayesian Approach with Local Competition (Konstantinos P. Panousis)

2019-06-26 11:00: Learning via Data Compression: Bayesian Coresets and Sparse Variational Inference (Trevor Campbell, University of British Columbia)

2019-07-12 11:00: Efficiency and Transferability of Neural Networks (Amos Storkey, School of Informatics, University of Edinburgh)

2019-07-17 14:00: Semi-Unsupervised Learning with Deep Generative Models / Disentangling Improves VAEs' Robustness to Adversarial Attacks (Matthew Willetts and Alexander Camuto, University of Oxford / Alan Turing Institute)

2019-08-23 11:00: Two Approximate Sampling Methods for Bayesian Deep Learning (Wesley Maddox (New York University))

2019-09-04 11:00: Rotation Invariant Householder Parameterization for Bayesian PCA (Rajbir Nirwan, Goethe University, Frankfurt)

2019-09-24 10:00: Learning-Algorithms from Bayesian Principles (Emti Khan, RIKEN center for Advanced Intelligence Project)

2019-10-25 11:00: Explaining Neural Networks: Post-hoc and Natural Language Explanations (Oana Camburu, University of Oxford)

2019-10-28 11:00: Constructing temporal latent spaces: Representation learning for clustering and imputation on time series (Vincent Fortuin, ETH Zurich)

2019-10-30 10:00: Reinforcement Learning at Huawei: Robustness, Safety, and Efficiency (Haitham Ammar, Huawei)

2019-11-12 11:00: Models and inference for temporal Gaussian processes (William Wilkinson, Aalto University, Finland)

2020-01-24 11:00: Robust Deep Learning Under Distribution Shift (Zack Lipton, CMU)

2020-03-05 11:00: Game Playing Meets Game Theory: Strategic Learning from Simulated Play (wellman@umich.edu)

2020-07-22 11:00: Efficient and Structured Uncertainty: Challenges and Opportunities (Andrey Malinin, Yandex Research)

2022-04-21 11:00: Graph neural network approach for decentralized multi-robot coordination (University of Cambridge)

2022-05-13 11:30: Beyond Conformal Prediction: Distribution-Free Uncertainty Quantification for Complex Machine Learning Tasks (Anastasios Angelopoulos, PhD student at UC Berkeley)

2022-06-16 11:00: Estimating RSV seasonality from pandemic disruptions: a modelling study (Fabienne Krauer, The London School of Hygiene & Tropical Medicine)

2022-06-28 11:00: Noise-Aware Differentially Private Synthetic Data (Antti Honkela, University of Helsinki)

2022-11-14 13:00: Presenting Hawk-Eye’s Skeletrack: Our machine learning approach to building a real time skeletal tracking system for sports, and how we're using it to shape the future of fan engagement  (Lachan Thorpe, Hawk-Eye Innovations)

2022-11-17 11:00: Deep Reinforcement Learning for Multi-Agent Interaction  (Stefano Albrecht, Edinburgh)

2022-11-22 11:00: Gaussian processes, spectral analysis kernels and optimal transport (Felipe Tobar, Universidad de Chile)

2022-11-24 11:30: Measuring Alignment Between Perceptual Systems: An Analysis Through The Lens of Shared Invariances (Vedant Nanda, MPI-SWS + University of Maryland )

2022-12-15 11:00: Scalable simulation and inference in non-Gaussian stochastic PDEs (David Duvenaud (University of Toronto))

2023-01-30 11:00: Calls to F the Algorithm: Lessons from the 2020 Exam Debacle (Roger Taylor, former Chair of Ofqual)

2023-05-11 11:00: Convergence bounds for the Random Walk Metropolis algorithm - Perspectives from Isoperimetry (Sam Power, University of Bristol)

2023-07-14 11:00: Compositional mathematics and automatic gradient descent (Jeremy Bernstein, MIT)

2023-09-14 15:00: Visit and talk by Jay McClelland: "Some thoughts on the differences between human and machine intelligence" (Jay McClelland, Stanford University )

2023-11-28 11:00: Modern Bayesian Experimental Design (Dr Tom Rainforth, OxCSML Group in the Department of Statistics at the University of Oxford)

2024-02-23 13:00: Consistent Validation for Predictive Methods in Spatial Settings (David Burt, MIT)

2024-06-13 11:00: Application of the optimal transport Gromov-Wasserstein problem to manifold learning and graph analysis (Hugues Van Assel; École Normale Supérieure de Lyon)

2024-06-13 11:00: Application of the optimal transport Gromov-Wasserstein problem to manifold learning and graph analysis (Hugues Van Assel; École Normale Supérieure de Lyon)

2024-06-13 11:00: Application of the optimal transport Gromov-Wasserstein problem to manifold learning and graph analysis (Hugues Van Assel; École Normale Supérieure de Lyon)