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Author Information
Ricardo Silva (University College London)
Wei Chu (Ant Group)
Zoubin Ghahramani (Uber and University of Cambridge)
Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. He studied computer science and cognitive science at the University of Pennsylvania, obtained his PhD from MIT in 1995, and was a postdoctoral fellow at the University of Toronto. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has held a number of leadership roles as programme and general chair of the leading international conferences in machine learning including: AISTATS (2005), ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a Fellow of the Royal Society.
Related Events (a corresponding poster, oral, or spotlight)
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2007 Spotlight: Hidden Common Cause Relations in Relational Learning »
Tue. Dec 4th 05:50 -- 06:00 PM Room
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2022 : Pragmatic Fairness: Optimizing Policies with Outcome Disparity Control »
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2022 : Evaluating the Impact of Geometric and Statistical Skews on Out-Of-Distribution Generalization Performance »
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2022 : Evaluating the Impact of Geometric and Statistical Skews on Out-Of-Distribution Generalization Performance »
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2023 Poster: Intervention Generalization: A View from Factor Graph Models »
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2021 Workshop: Bayesian Deep Learning »
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2020 : Invited Talk: On Prediction, Action and Interference »
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2020 Poster: A Class of Algorithms for General Instrumental Variable Models »
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2019 Workshop: Bayesian Deep Learning »
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2019 Poster: Bayesian Learning of Sum-Product Networks »
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2018 Workshop: Bayesian Deep Learning »
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2018 Poster: Bayesian Semi-supervised Learning with Graph Gaussian Processes »
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2018 Poster: MetaGAN: An Adversarial Approach to Few-Shot Learning »
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2017 : Panel Session »
Neil Lawrence · Finale Doshi-Velez · Zoubin Ghahramani · Yann LeCun · Max Welling · Yee Whye Teh · Ole Winther -
2017 Workshop: Bayesian Deep Learning »
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2017 : Panel: On the Foundations and Future of Approximate Inference »
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2017 Workshop: From 'What If?' To 'What Next?' : Causal Inference and Machine Learning for Intelligent Decision Making »
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2017 : Panel: "Should we prioritize research on human-like AI or something different?" »
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2017 Symposium: Kinds of intelligence: types, tests and meeting the needs of society »
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2017 Poster: Counterfactual Fairness »
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2017 Oral: Counterfactual Fairness »
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2017 Poster: Tomography of the London Underground: a Scalable Model for Origin-Destination Data »
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2017 Poster: Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning »
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2017 Poster: When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness »
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2016 : Automatic Discovery of the Statistical Types of Variables in a Dataset »
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2016 : History of Bayesian neural networks »
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2016 Workshop: Bayesian Deep Learning »
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2016 Workshop: Towards an Artificial Intelligence for Data Science »
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2016 Workshop: "What If?" Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems »
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2016 : How Machine Learning Research Can Address Key Societal and Governance Issues »
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2016 Workshop: People and machines: Public views on machine learning, and what this means for machine learning researchers »
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2016 Poster: A Theoretically Grounded Application of Dropout in Recurrent Neural Networks »
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2016 Poster: Observational-Interventional Priors for Dose-Response Learning »
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2016 Poster: Distributed Flexible Nonlinear Tensor Factorization »
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2016 Poster: Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages »
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2015 : Bayesian Optimization »
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2015 Workshop: Black box learning and inference »
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2015 Poster: Particle Gibbs for Infinite Hidden Markov Models »
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2015 Poster: Neural Adaptive Sequential Monte Carlo »
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2015 Poster: MCMC for Variationally Sparse Gaussian Processes »
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2015 Poster: Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions »
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2015 Invited Talk: Probabilistic Machine Learning: Foundations and Frontiers »
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2015 Poster: Statistical Model Criticism using Kernel Two Sample Tests »
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2014 Workshop: Bayesian Optimization in Academia and Industry »
Zoubin Ghahramani · Ryan Adams · Matthew Hoffman · Kevin Swersky · Jasper Snoek -
2014 Poster: Causal Inference through a Witness Protection Program »
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2014 Poster: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2014 Poster: Gaussian Process Volatility Model »
Yue Wu · José Miguel Hernández-Lobato · Zoubin Ghahramani -
2014 Spotlight: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2014 Poster: General Table Completion using a Bayesian Nonparametric Model »
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2013 Workshop: Probabilistic Models for Big Data »
Neil D Lawrence · Joaquin Quiñonero-Candela · Tianshi Gao · James Hensman · Zoubin Ghahramani · Max Welling · David Blei · Ralf Herbrich -
2013 Session: Oral Session 5 »
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2013 Poster: Flexible sampling of discrete data correlations without the marginal distributions »
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2012 Poster: Collaborative Gaussian Processes for Preference Learning »
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2012 Poster: A nonparametric variable clustering model »
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2012 Poster: Random function priors for exchangeable graphs and arrays »
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2012 Poster: Active Learning of Model Evidence Using Bayesian Quadrature »
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2012 Poster: Continuous Relaxations for Discrete Hamiltonian Monte Carlo »
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2012 Spotlight: Continuous Relaxations for Discrete Hamiltonian Monte Carlo »
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2011 Workshop: Copulas in Machine Learning »
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2011 Poster: Testing a Bayesian Measure of Representativeness Using a Large Image Database »
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2011 Poster: Thinning Measurement Models and Questionnaire Design »
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2010 Workshop: Transfer Learning Via Rich Generative Models. »
Russ Salakhutdinov · Ryan Adams · Josh Tenenbaum · Zoubin Ghahramani · Tom Griffiths -
2010 Talk: Unifying Views in Unsupervised Learning »
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2010 Oral: Tree-Structured Stick Breaking for Hierarchical Data »
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2010 Poster: Tree-Structured Stick Breaking for Hierarchical Data »
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2010 Spotlight: Copula Processes »
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2010 Poster: Copula Processes »
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2009 Workshop: Nonparametric Bayes »
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2009 Poster: Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process »
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2008 Poster: The Infinite Factorial Hidden Markov Model »
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2008 Poster: Bayesian Exponential Family PCA »
Shakir Mohamed · Katherine Heller · Zoubin Ghahramani -
2008 Spotlight: Bayesian Exponential Family PCA »
Shakir Mohamed · Katherine Heller · Zoubin Ghahramani -
2008 Spotlight: The Infinite Factorial Hidden Markov Model »
Jurgen Van Gael · Yee Whye Teh · Zoubin Ghahramani -
2007 Poster: Gaussian Process Models for Link Analysis and Transfer Learning »
Kai Yu · Wei Chu -
2006 Poster: Relational Learning with Gaussian Processes »
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2006 Poster: Modeling Dyadic Data with Binary Latent Features »
Ted Meeds · Zoubin Ghahramani · Radford M Neal · Sam T Roweis -
2006 Spotlight: Modeling Dyadic Data with Binary Latent Features »
Ted Meeds · Zoubin Ghahramani · Radford M Neal · Sam T Roweis -
2006 Poster: Gaussian Process Models for Discriminative Link Prediction »
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