Timezone: »
Approximate Bayesian computation (ABC) or likelihood-free (LF) methods have developed mostly beyond the radar of the machine learning community, but are important tools for a large segment of the scientific community. This is particularly true for systems and population biology, computational psychology, computational chemistry, computational finance, etc. Recent work has applied both machine learning models and algorithms to general ABC inference (e.g., NN, forests, GPs, LDA) and ABC inference to machine learning (e.g. using computer graphics to solve computer vision using ABC). In general, however, there is significant room for more intense collaboration between both communities. Submissions on the following topics are encouraged (but not limited to):
Examples of topics of interest in the workshop include (but are not limited to):
* Applications of ABC to machine learning, e.g., computer vision, other inverse problems (RL)…
* ABC Reinforcement Learning (other inverse problems)
* Machine learning models of simulations, e.g., NN models of simulation responses, GPs etc.
* Selection of sufficient statistics and massive dimension reduction methods
* Online and post-hoc error
* ABC with very expensive simulations and acceleration methods (surrogate modeling, choice of design/simulation points)
* Relation between ABC and probabilistic programming
* Posterior evaluation of scientific problems/interaction with scientists
* Post-computational error assessment
* Impact on resulting ABC inference
Author Information
Max Welling (University of Amsterdam / Qualcomm AI Research)
Neil D Lawrence (Amazon)
Richard D Wilkinson ( University of Sheffield)
I am Professor of Statistics at the University of Sheffield. I graduated with a BA, MMath and PhD in Mathematics from the University of Cambridge in 2008. My research is primarily in the field of uncertainty quantification - particularly on how to do parameter estimation for complex computer models. My main technical interests are on approximate Bayesian Computation (ABC) and Gaussian processes (GP). My current research goal is to develop GP models that include mechanistic/physical elements, in order to develop machine learning methods that encode scientific knowledge.
Ted Meeds (University of Amsterdam)
Christian X Robert (Université Paris-Dauphine)
More from the Same Authors
-
2020 Poster: Natural Graph Networks »
Pim de Haan · Taco Cohen · Max Welling -
2020 Poster: SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks »
Fabian Fuchs · Daniel E Worrall · Volker Fischer · Max Welling -
2020 Poster: SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows »
Didrik Nielsen · Priyank Jaini · Emiel Hoogeboom · Ole Winther · Max Welling -
2020 Oral: SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows »
Didrik Nielsen · Priyank Jaini · Emiel Hoogeboom · Ole Winther · Max Welling -
2020 Poster: The Convolution Exponential and Generalized Sylvester Flows »
Emiel Hoogeboom · Victor Garcia Satorras · Jakub Tomczak · Max Welling -
2020 Poster: Bayesian Bits: Unifying Quantization and Pruning »
Mart van Baalen · Christos Louizos · Markus Nagel · Rana Ali Amjad · Ying Wang · Tijmen Blankevoort · Max Welling -
2020 Poster: Experimental design for MRI by greedy policy search »
Tim Bakker · Herke van Hoof · Max Welling -
2020 Spotlight: Experimental design for MRI by greedy policy search »
Tim Bakker · Herke van Hoof · Max Welling -
2020 Poster: MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning »
Elise van der Pol · Daniel E Worrall · Herke van Hoof · Frans Oliehoek · Max Welling -
2019 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Eric Nalisnick · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2019 Poster: Invert to Learn to Invert »
Patrick Putzky · Max Welling -
2019 Poster: Deep Scale-spaces: Equivariance Over Scale »
Daniel Worrall · Max Welling -
2019 Poster: Integer Discrete Flows and Lossless Compression »
Emiel Hoogeboom · Jorn Peters · Rianne van den Berg · Max Welling -
2019 Poster: The Functional Neural Process »
Christos Louizos · Xiahan Shi · Klamer Schutte · Max Welling -
2019 Poster: Combining Generative and Discriminative Models for Hybrid Inference »
Victor Garcia Satorras · Zeynep Akata · Max Welling -
2019 Spotlight: Combining Generative and Discriminative Models for Hybrid Inference »
Victor Garcia Satorras · Max Welling · Zeynep Akata -
2019 Poster: Combinatorial Bayesian Optimization using the Graph Cartesian Product »
Changyong Oh · Jakub Tomczak · Efstratios Gavves · Max Welling -
2018 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2018 Workshop: NIPS 2018 workshop on Compact Deep Neural Networks with industrial applications »
Lixin Fan · Zhouchen Lin · Max Welling · Yurong Chen · Werner Bailer -
2018 Poster: Graphical Generative Adversarial Networks »
Chongxuan LI · Max Welling · Jun Zhu · Bo Zhang -
2018 Poster: 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data »
Maurice Weiler · Wouter Boomsma · Mario Geiger · Max Welling · Taco Cohen -
2017 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Andrew Wilson · Diederik Kingma · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2017 Workshop: Advances in Approximate Bayesian Inference »
Francisco Ruiz · Stephan Mandt · Cheng Zhang · James McInerney · James McInerney · Dustin Tran · Dustin Tran · David Blei · Max Welling · Tamara Broderick · Michalis Titsias -
2017 Poster: Causal Effect Inference with Deep Latent-Variable Models »
Christos Louizos · Uri Shalit · Joris M Mooij · David Sontag · Richard Zemel · Max Welling -
2017 Poster: Bayesian Compression for Deep Learning »
Christos Louizos · Karen Ullrich · Max Welling -
2017 Tutorial: Deep Probabilistic Modelling with Gaussian Processes »
Neil D Lawrence -
2016 Workshop: Bayesian Deep Learning »
Yarin Gal · Christos Louizos · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2016 Poster: Improving Variational Autoencoders with Inverse Autoregressive Flow »
Diederik Kingma · Tim Salimans · Rafal Jozefowicz · Peter Chen · Xi Chen · Ilya Sutskever · Max Welling -
2015 Workshop: Scalable Monte Carlo Methods for Bayesian Analysis of Big Data »
Babak Shahbaba · Yee Whye Teh · Max Welling · Arnaud Doucet · Christophe Andrieu · Sebastian J. Vollmer · Pierre Jacob -
2015 Workshop: ABC in Montreal »
Ted Meeds · Michael Gutmann · Dennis Prangle · Jean-Michel Marin · Richard Everitt -
2015 Workshop: Advances in Approximate Bayesian Inference »
Dustin Tran · Tamara Broderick · Stephan Mandt · James McInerney · Shakir Mohamed · Alp Kucukelbir · Matthew D. Hoffman · Neil Lawrence · David Blei -
2015 Symposium: Deep Learning Symposium »
Yoshua Bengio · Marc'Aurelio Ranzato · Honglak Lee · Max Welling · Andrew Y Ng -
2015 Poster: Bayesian dark knowledge »
Anoop Korattikara Balan · Vivek Rathod · Kevin Murphy · Max Welling -
2015 Poster: Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference »
Ted Meeds · Max Welling -
2015 Poster: Variational Dropout and the Local Reparameterization Trick »
Diederik Kingma · Tim Salimans · Max Welling -
2014 Poster: Semi-supervised Learning with Deep Generative Models »
Diederik Kingma · Shakir Mohamed · Danilo Jimenez Rezende · Max Welling -
2014 Demonstration: Machine Learning in the Browser »
Ted Meeds · Remco Hendriks · Said Al Faraby · Magiel Bruntink · Max Welling -
2014 Spotlight: Semi-supervised Learning with Deep Generative Models »
Diederik Kingma · Shakir Mohamed · Danilo Jimenez Rezende · Max Welling -
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 1 »
Neil D Lawrence -
2013 Tutorial: Approximate Bayesian Computation (ABC) »
Richard D Wilkinson -
2012 Poster: Fast Variational Inference in the Conjugate Exponential Family »
James Hensman · Magnus Rattray · Neil D Lawrence -
2012 Poster: The Time-Marginalized Coalescent Prior for Hierarchical Clustering »
Levi Boyles · Max Welling -
2011 Poster: Learning sparse inverse covariance matrices in the presence of confounders »
Oliver Stegle · Christoph Lippert · Joris M Mooij · Neil D Lawrence · Karsten Borgwardt -
2011 Poster: Statistical Tests for Optimization Efficiency »
Levi Boyles · Anoop Korattikara · Deva Ramanan · Max Welling -
2011 Poster: Variational Gaussian Process Dynamical Systems »
Andreas Damianou · Michalis Titsias · Neil D Lawrence -
2010 Placeholder: Opening Remarks »
Terrence Sejnowski · Neil D Lawrence -
2010 Spotlight: Switched Latent Force Models for Movement Segmentation »
Mauricio A Alvarez · Jan Peters · Bernhard Schölkopf · Neil D Lawrence -
2010 Poster: On Herding and the Perceptron Cycling Theorem »
Andrew E Gelfand · Yutian Chen · Laurens van der Maaten · Max Welling -
2010 Poster: Switched Latent Force Models for Movement Segmentation »
Mauricio A Alvarez · Jan Peters · Bernhard Schölkopf · Neil D Lawrence -
2009 Workshop: Kernels for Multiple Outputs and Multi-task Learning: Frequentist and Bayesian Points of View »
Mauricio A Alvarez · Lorenzo Rosasco · Neil D Lawrence -
2008 Session: Oral session 10: Nonparametric Processes, Scene Processing and Image Statistics »
Max Welling -
2008 Poster: Sparse Convolved Gaussian Processes for Multi-ouptut Regression »
Mauricio A Alvarez · Neil D Lawrence -
2008 Poster: Asynchronous Distributed Learning of Topic Models »
Arthur Asuncion · Padhraic Smyth · Max Welling -
2008 Poster: Efficient Sampling for Gaussian Process Inference using Control Variables »
Michalis Titsias · Neil D Lawrence · Magnus Rattray -
2008 Spotlight: Efficient Sampling for Gaussian Process Inference using Control Variables »
Michalis Titsias · Neil D Lawrence · Magnus Rattray -
2008 Poster: Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes »
Ben Calderhead · Mark A Girolami · Neil D Lawrence -
2007 Workshop: Approximate Bayesian Inference in Continuous/Hybrid Models »
Matthias Seeger · David Barber · Neil D Lawrence · Onno Zoeter -
2007 Spotlight: Collapsed Variational Inference for HDP »
Yee Whye Teh · Kenichi Kurihara · Max Welling -
2007 Spotlight: Distributed Inference for Latent Dirichlet Allocation »
David Newman · Arthur Asuncion · Padhraic Smyth · Max Welling -
2007 Poster: Infinite State Bayes-Nets for Structured Domains »
Max Welling · Ian Porteous · Evgeniy Bart -
2007 Poster: Collapsed Variational Inference for HDP »
Yee Whye Teh · Kenichi Kurihara · Max Welling -
2007 Poster: Distributed Inference for Latent Dirichlet Allocation »
David Newman · Arthur Asuncion · Padhraic Smyth · Max Welling -
2007 Spotlight: Infinite State Bayes-Nets for Structured Domains »
Max Welling · Ian Porteous · Evgeniy Bart -
2006 Workshop: Learning when test and training inputs have different distributions »
Joaquin Quiñonero Candela · Masashi Sugiyama · Anton Schwaighofer · Neil D Lawrence -
2006 Poster: Modelling transcriptional regulation using Gaussian Processes »
Neil D Lawrence · Guido Sanguinetti · Magnus Rattray -
2006 Poster: Structure Learning in Markov Random Fields »
Sridevi Parise · Max Welling -
2006 Poster: Accelerated Variational Dirichlet Process Mixtures »
Kenichi Kurihara · Max Welling · Nikos Vlassis -
2006 Spotlight: Accelerated Variational Dirichlet Process Mixtures »
Kenichi Kurihara · Max Welling · Nikos Vlassis -
2006 Poster: A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation »
Yee Whye Teh · David Newman · Max Welling