Talks and Posters

Selected Talks

May 2017
 Parallel Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space.
Gaussian Process Approximations Workshop, workshop at Amazon Research Center, Berlin, Germany. [slides]
May 2017
 Bayesian Machine Learning for Efficient Optimization of Black-box Functions.
ARM Ltd, Cambridge, UK.
Mar 2017
 Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks.
Artificial Intelligence and Machine Learning in Cambridge, workshop at Microsoft Research Cambridge, Cambridge, UK. [slides]
Mar 2017
 Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks.
Fourth Edinburgh Deep Learning Workshop, Edinburgh, UK. [slides]
Jan 2017
 Bayesian Optimization for Accelerated Exploration of Chemical Space.
International Symposium on Machine Learning Challenges in Complex Multiscale Physical Systems, TUM, Munich, Germany. [slides]
Dec 2016
 Alpha divergence minimization for Bayesian deep learning.
NIPS workshop on Bayesian deep learning, Barcelona, Spain [slides] [talk] [workshop panel]
Nov 2016
 Approximate EP for Deep Gaussian Processes.
Dagstuhl Seminar 16481, New Directions for Learning with Kernels and Gaussian Processes, Schloss Dagsthul, Germany. [slides]
Sep 2016
 Bayesian Optimization for Accelerated Exploration of Chemical Space.
Workshop, Machine Learning Meets Many-Particle Problems, Institute for Pure and Applied Mathematics, Los Angeles, California, USA.
Sep 2016
 Bayesian Machine Learning for Efficient Optimization of Black-box Functions.
Department of Engineering, University of Oxford, UK.
Jul 2016
 Bayesian Optimization of Genetic Programs.
Foundry Annual Meeting. Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Jun 2016
 Black-Box Alpha Divergence Minimization.
International Conference on Machine Learning, New York City, USA. [slides]
Mar 2016
 Bayesian Machine Learning for Efficient Optimization of Black-box Functions.
University of Toronto, Toronto, Canada.
Mar 2016
 Bayesian Machine Learning for Efficient Optimization of Black-box Functions.
Edinburgh University, Edinburgh, UK.
Mar 2016
 Bayesian Machine Learning for Efficient Optimization of Black-box Functions.
Max Planck Institute for Intelligent Systems, Tubingen, Germany.
Mar 2016
 Bayesian Machine Learning for Efficient Optimization of Black-box Functions.
EPFL, Lausanne, Switzerland.
Feb 2016
 Bayesian Machine Learning for Efficient Optimization of Black-box Functions.
New York University, New York City, USA.
Jan 2016
 Bayesian Machine Learning for Efficient Optimization of Black-box Functions.
Amazon, Berlin, Germany.
Jul 2015
 Predictive Entropy Search for Bayesian Optimization with Unknown Constraints.
International Conference on Machine Learning, Lille, France. [slides]
Jul 2015
 Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks.
International Conference on Machine Learning, Lille, France. [slides]
May 2015
 Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks.
Workshop on Gaussian Process Approximations, Copenhagen, Denmark. [slides]
Mar 2015
 Bayesian Optimization and Information-based Approaches.
Machine Learning Meetup “Bayes Theorem & Bayesian Optimization”, Boston, Massachusetts, USA. [slides]
Jul 2014
 Probabilistic Matrix Factorization with Non-random Missing Data.
International Conference on Machine Learning, Beijing, China. [slides]
May 2014
 Stochastic Variational Inference for Large Scale Machine Learning.
Department of Computer Science, Universidad Autónoma de Madrid, Spain. [slides]
Feb 2014
 An Introduction to Determinantal Point Processes.
Machine Learning Group, Cambridge University, Cambridge, UK. [slides]
Feb 2014
 Gaussian Process Conditional Copulas.
Microsoft Research, Cambridge, UK. [slides] [talk]
Oct 2013
 Gaussian Process Conditional Copulas with Applications to Financial Time Series.
Oxford-Man Institute of Quantitative Finance, University of Oxford, UK. [slides]
Jun 2013
 Gaussian Process Vine Copulas for Multivariate Dependence.
Columbia University, New York, USA. [slides]
Apr 2013
 An Introduction to Sum Product Networks.
Department of Engineering, Cambridge University, UK. [slides]
Apr 2013
 An Introduction to Bayesian Machine Learning.
Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. [slides]
Apr 2013
 Inference in Discrete Graphical Models.
Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. [slides]
Apr 2013
 The Laplace Approximation and Variational Inference.
Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. [slides]
Apr 2013
 Expectation Propagation.
Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. [slides]
Feb 2013
 Stochastic Variational Inference for Modeling Binary Matrices.
Xerox Research, Bangalore, India. [slides]
Feb 2013
 NetBox: a Probabilistic Method for Analyzing Market Basket Data.
Infosys Limited, Bangalore, India [slides]
Feb 2012
 Ensemble Methods and Optimal Ensemble Size.
Toshiba Research Laboratory, Cambridge, UK. [slides]
Dec 2011
 Expectation Propagation for the Estimation of Conditional Bivariate Copulas.
NIPS Workshop on Copulas in Machine Learning, Granada, Spain. [slides] [talk]
Sep 2011
 Modeling Transaction Data.
Infosys Limited, Bangalore, India. [slides]
Sep 2011
 Market Basket Analysis: An Introduction.
Infosys Limited, Bangalore, India. [slides]
Jul 2011
 Gaussianity Measures for Detecting the Direction of Causal Time Series.
International Joint Conference on Artificial Intelligence, Barcelona, Spain. [slides]
Mar 2011
 Expectation Propagation in Sparse Linear Models with Spike and Slab Priors.
Department of Engineering, Cambridge University, UK. [slides]
Dec 2010
 Balancing Flexibility and Robustness in Machine Learning: Semi-parametric Methods and Sparse Linear Models.
Department of Computer Science, Universidad Autónoma de Madrid, Spain. [slides]
Sep 2010
 Hub Gene Selection Methods for the Reconstruction of Transcription Networks.
European Conference on Machine Learning (ECML), Barcelona, Spain. [slides]
Jul 2009
 Modeling Dependence in Financial Data with Semiparametric Archimedean Copulas.
International Workshop on Advances in Machine Learning for Computational Finance, London, UK. [slides] [talk]

Selected Posters

Jun 2016
 Black-box alpha-divergence minimization.
In ICML, New York City, USA, 2016. [pdf]
Jun 2016
 Predictive Entropy Search for Multi-objective Bayesian Optimization.
In ICML, New York City, USA, 2016. [pdf]
Jun 2016
 Deep Gaussian Processes for Regression using Approximate Expectation Propagation.
In ICML, New York City, USA, 2016. [pdf]
Dec 2015
 Black-box alpha-divergence minimization.
In NIPS Workshop on Black-box Learning and Inference, Montreal, Canada, 2015. [pdf]
Dec 2015
 Predictive Entropy Search for Multi-objective Bayesian Optimization.
In NIPS Workshop on Bayesian Optimization: Scalability and Flexibility, Montreal, Canada, 2015. [pdf]
Dec 2015
 Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation.
In NIPS Workshop on Advances in Approximate Bayesian Inference, Montreal, Canada, 2015. [pdf]
Dec 2015
 Stochastic Expectation Propagation for Large Scale Gaussian Process Classification.
In NIPS Workshop on Advances in Approximate Bayesian Inference, Montreal, Canada, 2015. [pdf]
Dec 2015
 Stochastic Expectation Propagation.
In NIPS, Montreal, Canada, 2015. [pdf]
July 2015
 Predictive Entropy Search for Bayesian Optimization with Unknown Constraints.
In ICML, Lille, France, 2015. [pdf]
July 2015
 Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks.
In ICML, Lille, France. [pdf]
July 2015
 A Probabilistic Model for Dirty Multi-task Feature Selection.
In ICML, Lille, France. [pdf]
Dec 2014
 Learning the Semantics of Discrete Random Variables: Ordinal or Categorical?.
In NIPS Workshop on Learning Semantics, Montreal, Canada. [pdf]
Dec 2014
 Predictive Entropy Search for Bayesian Optimization with Unknown Constraints.
In NIPS Workshop on Bayesian Optimization in Academia and Industry, Montreal, Canada. [pdf]
Dec 2014
 Gaussian Process Volatility Model.
Neural Information Processing Systems (NIPS), Montreal, Canada. [pdf]
Dec 2014
 Predictive Entropy Search for Efficient Global Optimization of Black-box Functions.
Neural Information Processing Systems (NIPS), Montreal, Canada. [pdf]
Jun 2014
 Probabilistic Matrix Factorization with Non-random Missing Data.
International Conference on Machine Learning (ICML), Beijing, China. [pdf]
Jun 2014
 Cold-start Active Learning with Robust Ordinal Matrix Factorization.
International Conference on Machine Learning (ICML), Beijing, China. [pdf]
Jun 2014
 Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices.
International Conference on Machine Learning (ICML), Beijing, China. [pdf]
Dec 2013
 Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices.
NIPS Workshop on Randomized Methods for Machine Learning, Lake Tahoe, Nevada, USA. [pdf]
Dec 2013
 Gaussian Process Conditional Copulas with Applications to Financial Time Series.
Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, USA. [pdf]
Dec 2013
 Learning Feature Selection Dependencies in Multi-task Learning.
Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, USA. [pdf]
Jun 2013
 Dynamic Covariance Models for Multivariate Financial Time Series.
International Conference on Machine Learning (ICML), Atlanta, USA. [pdf]
Jun 2013
 Gaussian Process Vine Copulas for Multivariate Dependence.
International Conference on Machine Learning (ICML), Atlanta, USA. [pdf]
Dec 2012
 Collaborative Gaussian Processes for Preference Learning.
Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, USA. [pdf]
Dec 2012
 Semi-Supervised Domain Adaptation with Non-Parametric Copulas.
Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, USA. [pdf]
Dec 2011
 Robust Multi-Class Gaussian Process Classification.
Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, USA. [pdf]
Dec 2011
 Expectation Propagation for the Estimation of Conditional Bivariate Copulas.
In NIPS Workshop on Copulas in Machine Learning, Granada, Spain, 2011. [pdf] [talk]
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