Zoubin Ghahramani

Name:	Zoubin Ghahramani
Affiliation:	University of Cambridge
E-mail:	(only provided to users who are logged into talks.cam)
Last login:	Thu May 03 10:28:26 +0000 2018

Public lists managed by Zoubin Ghahramani

• Computational and Biological Learning Seminar Series
• Joint Machine Learning Seminars
• Machine Learning @ CUED
• Machine Learning Reading Group @ CUED
• Machine Learning and Inference (One day meeting)

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 e-mail 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.

• Control, inference and learning
• Julia: Introduction and new developments
• Variational autoencoders with latent graphical models
• Probabilistic Numerical Computation: A New Concept?
• 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.
• 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 Cross-Modal 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
• Meta-Bayesian Analysis
• A-Star Sampling Review
• Training and Understanding Deep Neural Networks for Robotics, Design, and Perception
• Information-Theoretic Bounded Rationality
• MCMC for non-linear state space models using ensembles of latent sequences
• Gradient-based hyperparameter optimization through reversible learning
• Modeling Confounding by Half-Sibling Regression
• Explaining Non-Linear Classifier Decisions with application to Deep Learning
• Deep Learning
• Random Function Classes for Machine Learning
• Do Deep Nets Really Need to be Deep?
• Direction-Only 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 Non-parametric Topic Models
• The Blended Paradigm: A Bayesian Approach to Handling Outliers and Misspecified Models
• Bayesian modeling for high-level real nursing activity recognition using accelerometers
• A Tutorial on Probabilistic Programming
• Unsupervised Many-to-many Object Matching
• On the Bethe approximation
• Bayesian monitoring for the Comprehensive Nuclear-Test-Ban Treaty
• Unifying logic and probability: A "New Dawn" for Artificial Intelligence?
• Practical Machine Learning at Facebook. Examples and Lessons Learnt.
• Bayesian inference for integer-valued Lévy processes with Non-Gaussian Ornstein-Uhlenbeck volatility modelling
• Stable Poisson-Kingman 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 dynamic-clustering 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 Non-linear 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 non-exchangeable data
• Non-parametric Bayesian Chromatin State Segmentation
• An application of HDP And IBP for stream-based 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 Matrix-variate Data in a New Gaussian Process-based Method
• Probabilistic methods for biomolecular structure simulations
• Compressed Sensing Applications in Functional Magnetic Resonance Imaging
• Multi-Label Learning with Millions of Categories
• Efficient Sampling with Kernel Herding
• Frank-Wolfe 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 Decision-Making with Information Processing Costs
• Human Behavior Classification with Infinite Hidden Conditional Random Fields
• Fast Gaussian process learning for regression, semi-supervised classification, and multiway analysis
• Optimal integration of top-down and bottom-up uncertainty in humans, monkeys, and neural networks
• Discovery of Complex Behaviors through Contact-Invariant Optimization
• Non-parametric Bayesian Method and Maximum-A-Posteriori 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 Non-Conjugate 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 Ewens-Pitman family of random partitions by a deletion property and a de Finetti-type 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
• Non-Smooth-Norm 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 Fixed-Form 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 Error-Correction 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
• Quasi-linear 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 Pitman-Yor Processes
• Spoken Dialogue Management
• A Bayesian approach to language learning
• Learning Bigrams from Unigrams
• Nonparametric Bayesian Natural Language Model Domain Adaptation: A Hierarchical, Hierarchical Pitman-Yor Process Language Model
• Bayesian approaches to autonomous Bayesian real-time learning
• Non-negative 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
• Message-passing 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 high-dimensional latent variable models
• H-Infinity 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 Kernel-Based 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 Non-parametric 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