SML Seminars

All talks will be announced via the ml-talks mailing list.

2018-01-09 Jarno Vanhatalo University of Helsinki Spatial Clustering Using Gaussian Processes Embedded in a Mixture Model
2017-11-28 Nikos Nikolaou University of Manchester Boosting for Probability Estimation and Cost-Sensitive Learning
2017-07-20 Heiko Strathmann Gatsby Unit, UCL Efficient and Principled Score Estimation
2017-07-17 Sesh Kumar IST Austria Discrete Optimisation for Machine Learning
2016-01-28 Bert Kappen Radboud University Nijmegen Control, Inference and Learning
2015-12-16 Tamara Broderick MIT Feature Allocations, Probability functions, and Paintboxes Slides
2015-10-12 Roberto Calandra TU Darmstadt Bayesian Modeling for Optimization and Control in Robotics Recording
2015-08-27 Nathan Ratliff Max Planck Institute for Intelligent Systems Riemannian Motion Optimization and the Geometry of Our Workspace
2015-03-11 Matthew Hoffman University of Cambridge Bayesian Optimization
2015-03-11 Neil Lawrence University of Sheffield Probabilistic Dimensionality Reduction
2015-03-04 Daniel Polani University of Hertfordshire Information Theory in Intelligent Decision Making
2015-02-18 Shakir Mohamed Google DeepMind The Variational Approach for Inference in Probabilistic Models
2015-02-04 Mike Osborne University of Oxford Probabilistic Numerics
2015-01-21 Arthur Gretton University College London Kernel Methods for Hypothesis Testing and Inference
2014-11-26 Ricardo Silva UCL Causal Inference in Machine Learning
2014-11-20 Cedric Archambeau Amazon Amazon: A Playground for ML
2014-10-27 James Hensman University of Sheffield Variational Compression in Nested Gaussian Process Models
2014-10-22 David Barber University College London Probabilistic Machine Learning
2014-10-15 Frank Wood University of Oxford Probabilistic Programming
2014-09-11 Diego Klabjan Northwestern University Algorithms for L1-Norm Principal Component Analysis
2014-09-09 Christian Daniel TU Darmstadt Active Reward Learning
2014-06-20 Gerhard Neumann TU Darmstadt Learning Modular Control Policies in Robotics
2014-04-28 Herke van Hoof TU Darmstadt Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments
2014-03-12 Carl Edward Rasmussen University of Cambridge Gaussian Processes for Machine Learning
2014-02-26 John Shawe-Taylor University College London Kernel Methods for Pattern Analysis
2014-02-12 Iain Murray University of Edinburgh Markov Chain Monte Carlo for Probabilistic Models
2014-01-29 Zoubin Ghahramani University of Cambridge Bayesian Machine Learning and the Automated Statistician
2013-12-19 Simo Särkkä Aalto University Gaussian Processes as Prior Models for Latent Functions in Non-Linear Stochastic Differential Equation Models
2013-12-17 Igor Gilitschenski Karlsruhe Institute of Technology Dynamic State Estimation using Directional Statistics
2013-11-13 Roberto Calandra TU Darmstadt Robot Learning for Locomotion
2013-11-13 Tim Barfoot University of Toronto Visual Route Following for Mobile Robots
2013-10-16 Philipp Beckerle TU Darmstadt User-Centered Actuation of Lower Limb Prosthetic Devices
2013-10-10 Hiroto Saigo Kyushu Institute of Technology Multiple Response Regression for Graph Mining