
Zoubin Ghahramani
Name:  Zoubin Ghahramani 
Affiliation:  University of Cambridge 
Email:  (only provided to users who are logged into talks.cam) 
Last login:  Fri Oct 14 05:00:30 +0000 2016 
Public lists managed by Zoubin Ghahramani
Talks given by Zoubin Ghahramani
Obviously this only lists talks that are listed through talks.cam. Furthermore, this facility only works if the speaker's email was specified in a talk. Most talks have not done this.
Talks organised by Zoubin Ghahramani
This list is based on what was entered into the 'organiser' field in a talk. It may not mean that Zoubin Ghahramani actually organised the talk, they may have been responsible only for entering the talk into the talks.cam system.
 Variational autoencoders with latent graphical models
 Moment matching for latent variable models: from ICA to LDA and CCA
 Multiresolution Matrix Factorization
 Dynamic Models for Health Data
 Inference as Learning
 Discriminative Embeddings of Latent Variable Models for Structured Data
 Structured Dynamic Graphical Models & Scaling Multivariate Time Series Methodology
 Developments in Exact Inference in Graphical Models
 Approximation strategies for structure learning in Bayesian networks
 From Sensation to Conception: Theoretical Perspectives on Multisensory Perception and CrossModal Transfer
 Perspectives on Designing Optimal User Interfaces
 Inference and Learning in the Anglican Probabilistic Programming System
 Approximate Message Passing Algorithms
 Unsupervised Risk Estimation with only Structural Assumptions
 Inference of a partially observed kinetic Ising model
 MetaBayesian Analysis
 AStar Sampling Review
 Training and Understanding Deep Neural Networks for Robotics, Design, and Perception
 InformationTheoretic Bounded Rationality
 MCMC for nonlinear state space models using ensembles of latent sequences
 Gradientbased hyperparameter optimization through reversible learning
 Modeling Confounding by HalfSibling Regression
 Explaining NonLinear Classifier Decisions with application to Deep Learning
 Deep Learning
 Random Function Classes for Machine Learning
 Do Deep Nets Really Need to be Deep?
 DirectionOnly Optimisation for Neural Networks
 Machine Learning for Quantitative Finance: A collaboration between the Cambridge Machine Learning Group and Cambridge Capital Management
 Title to be confirmed
 Evolutionary dynamics in a continuous public goods game
 An introduction to the Mondrian Process
 A* Sampling
 Orthologous networks in biological systems
 Latent Branching Trees
 Experiments with Nonparametric Topic Models
 The Blended Paradigm: A Bayesian Approach to Handling Outliers and Misspecified Models
 Bayesian modeling for highlevel real nursing activity recognition using accelerometers
 A Tutorial on Probabilistic Programming
 Unsupervised Manytomany Object Matching
 On the Bethe approximation
 Bayesian monitoring for the Comprehensive NuclearTestBan Treaty
 Unifying logic and probability: A "New Dawn" for Artificial Intelligence?
 Practical Machine Learning at Facebook. Examples and Lessons Learnt.
 Bayesian inference for integervalued Lévy processes with NonGaussian OrnsteinUhlenbeck volatility modelling
 Stable PoissonKingman species sampling priors generated by general ordered size biased generalized gamma mixing distributions
 Probabilistic computing applications: BayesDB and stochastic digital circuits
 Probabilistic computing for Bayesian inference
 Machine Learning and Order Book Dynamics
 Policy Evaluation with Temporal Differences
 Matrix Means, Distances, Kernels, and Geometric Optimization
 Learning to Learn for Structured Sparsity
 Bayesian nonparametric dynamicclustering and genetic imputation
 Parameter estimation in deep learning architectures: Two new insights.
 Bayesian canonical correlation analysis
 Particle filters and curse of dimensionality
 Bayesian nonparametrics: Dependency and Constraint Modeling
 Bayesian Nonparametric Model for Power Disaggregation
 Probabilistic machine learning for knowledge extraction from videos and text
 Frequentist coverage of adaptive nonparametric Bayesian credible sets
 Anglican; Particle MCMC inference for Probabilistic Programs
 Sparse discriminative latent characteristics for predicting cancer drug sensitivity
 Contrastive Learning Using Spectral Methods
 Dissecting genotype to phenotype relationships
 CANCELLED: Local Deep Kernel Learning for Efficient Nonlinear SVM Prediction
 Clustering Based on Predictive Variances in Gaussian Process Regression Models
 Higher Order Learning for Classification in Emergency Situations
 Annealing Between Distributions by Averaging Moments
 Matrix Concentration Inequalities via the Method of Exchangeable Pairs
 Deep Gaussian Processes
 Approaches to statistical modeling of network data
 Bayesian nonparametric methods for nonexchangeable data
 Nonparametric Bayesian Chromatin State Segmentation
 An application of HDP And IBP for streambased action recognition and high dimensional data
 Using Context and Insight for the Analysis of LittleData?
 Feature allocations, probability functions, and paintboxes
 Modelling Reciprocating Relationships with Hawkes Processes
 Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables
 Learning of Milky Way Model Parameters Using Matrixvariate Data in a New Gaussian Processbased Method
 Probabilistic methods for biomolecular structure simulations
 Compressed Sensing Applications in Functional Magnetic Resonance Imaging
 MultiLabel Learning with Millions of Categories
 Efficient Sampling with Kernel Herding
 FrankWolfe optimization insights in machine learning
 Deep learning for vision: a case study for visual textures, and some thoughts on a general framework
 Thermodynamics as a Theory of DecisionMaking with Information Processing Costs
 Human Behavior Classification with Infinite Hidden Conditional Random Fields
 Fast Gaussian process learning for regression, semisupervised classification, and multiway analysis
 Optimal integration of topdown and bottomup uncertainty in humans, monkeys, and neural networks
 Discovery of Complex Behaviors through ContactInvariant Optimization
 Nonparametric Bayesian Method and MaximumAPosteriori Inference in Statistical Machine Translation
 Infinite Structured Explicit Duration Hidden Markov Models
 Probabilistic computing: computation as universal stochastic inference, not deterministic calculation
 Scaling Machine Learning for the Internet
 Bayesian Quadrature for Prediction and Optimisation
 Efficient MCMC for Continuous Time Discrete State Systems
 A Maximum Entropy Perspective on Spectral Dimensionality Reduction
 Variational Inference for NonConjugate Models
 Not so naive Bayesian classification
 Bayesian Nonparametrics: Latent Feature and Prediction Models, and Efficient Inference
 Machine Learning Markets
 Exclusive Pólya Urns and their applications
 Approximate Bayesian Inference for Large Scale Inverse Problems: A Computational Viewpoint
 Some Practical Reflections on Graphical Models
 Beyond Keyword Search: Discovering Relevant Scientific Literature
 Graphical Models for Bandit Problems
 Bayesian regression and classification with multivariate sparsifying priors
 An FX trading system using adaptive reinforcement learning
 An FX trading system using adaptive reinforcement learning
 Characterization of the EwensPitman family of random partitions by a deletion property and a de Finettitype theorem for exchangeable hierarchies
 Nonlinear Dynamics of Learning
 Exponential Conditional Volatility Models
 Challenges in implementing the Bayesian paradigm
 Expectation Propagation in Sparse Linear Models with Spike and Slab Priors
 Probabilistic matrix factorization for reconstruction of missing data
 Title to be confirmed
 Differential Geometric MCMC Methods
 Mining viral datasets
 Universal Bayesian Agents: Theory and Applications
 NonSmoothNorm Image Reconstruction from Noisy Data
 Crowdsourcing data modelling
 CANCELLED
 Bayesian Inference with Kernels
 Continuous control of brain computer interfaces based on a covert spatial attention paradigm
 Creating structured and ﬂexible models: some open problems
 Efficient Bayesian analysis of multiple changepoint models
 Learning Common Grammar from Multilingual Corpus / Online Multiscale Dynamic Topic Models
 Using transformed domains to sparsify Gaussian Processes
 Natural Conjugate Gradient Learning for FixedForm Variational Bayes
 Using topic models to help cure cancer
 Message Passing In Centralized Database
 Parametric Bandits, Query Learning, and the Haystack Dimension
 Slice sampling with latent Gaussian models
 Dynamic Network Tomography: Model, Algorithm, Theory, and Application
 Making Sense of Data  A Research Agenda
 Recursive CRFs for Scalable Vision
 Subspace Codes for Adversarial ErrorCorrection in Network Coding
 Title to be confirmed
 Bayesian Inference in Networks of Queues
 Stochastic Outlier Selection
 Machine Learning Course (4F13)
 CANCELLED: Learning Components for Human Sensing
 Machine Learning Course (4F13)
 Gaussian Processes for Active Data Selection, Faults, Changepoints and Sensor Selection
 KL control theory and decision making under uncertainty
 Coconut: Optimizing computations for machine learning
 CANCELLED
 Computable Probability Theory
 Algorithms for Understanding Motor Cortical Processing and Neural Prosthetic Systems
 Shrinkage regression for multivariate inference with missing data, with an application to portfolio balancing
 Quasilinear Sensor Management
 An Introduction to Transcriptomics
 Mind reading by machine learning: an ideal observer based analysis of cognitive scientific experiments
 Generalization in Learning
 Stochastic control as an inference problem
 Extending the Affinity Propagation Model
 The Block Diagonal Infinite Hidden Markov Model
 Probabilistic Graph Models for Debugging Software
 Mondrian Processes
 Deep Networks for Vision
 Consensus finding, exponential models and infinite rankings
 Efficient Sequential Monte Carlo Inference for Kingman's Coalescent
 Context in human robot interaction
 Shared Segmentation of Natural Scenes using Dependent PitmanYor Processes
 Spoken Dialogue Management
 A Bayesian approach to language learning
 Learning Bigrams from Unigrams
 Nonparametric Bayesian Natural Language Model Domain Adaptation: A Hierarchical, Hierarchical PitmanYor Process Language Model
 Bayesian approaches to autonomous Bayesian realtime learning
 Nonnegative matrix factorization with Gaussian process priors
 Matrix Factorization and Relational Learning
 Double Feature: Optimal Precoding for MIMO and Divergence Estimation for Continuous Distributions
 Nonparametric Bayesian Learning of Switching Dynamical Systems
 Messagepassing inference on graphical models
 Variational Bayesian Mixtures of Gaussians
 Bayesian analysis of complex biological systems
 Variational inference for partially observed diffusion processes
 Seeing Patterns in Randomness: Irrational Superstition or Adaptive Behavior?
 Assessing highdimensional latent variable models
 HInfinity Clustering
 Talking with Robots: A Case Study in Architectures for Cognitive Robotics
 An Introduction to Statistical Learning Theory
 Convergence analysis of the EM algorithm and joint minimization of free energy
 Discriminative Methods with Structure
 Inductive Logic Programming
 Statistical Machine Translation
 Model selection and model order adaptation for clustering
 Expectation Propagation, Experimental Design for the Sparse Linear Model
 Biomedical Image Search
 Information Retrieval
 Stable distribution and data sketching
 Reinforcement Learning
 Sparse Gaussian Process in Disease Mapping
 Graphical Models
 Gene Regulatory Network Inference: A KernelBased Learning Approach
 Error Correcting Codes
 Machine Learning Applications / Challenges in Natural Language Parsing
 Sparse Bayesian Linear Models
 Group Theory and Machine Learning
 Hidden Common Cause Relations in Relational Learning
 Autonomous Agents under Operational Closure
 Covariate Shift Adaptation: Supervised Learning When Training and Test Inputs Have Different Distributions
 Bayesian Ranking
 Dirichlet Processes and Hierarchical Dirichlet Processes
 An Introduction to Nonparametric Bayesian Methods
 Advanced MCMC Methods
 Partially Observable Markov Decision Processes (POMDPs)
 Expectation Propagation
 Nonlinear Dimensionality Reduction
 Causality
 Gaussian Processes for Machine Learning
 Mixture Models and the EM Algorithm
 Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model
