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Extending on the workshop’s success from the past 3 years, this workshop will study the developments in the field of Bayesian deep learning (BDL) over the past year. The workshop will be a platform to host the recent flourish of ideas using Bayesian approaches in deep learning, and using deep learning tools in Bayesian modelling. The program includes a mix of invited talks, contributed talks, and contributed posters. Future directions for the field will be debated in a panel discussion.
Speakers:
* Andrew Wilson
* Deborah Marks
* Jasper Snoek
* Roger Grosse
* Chelsea Finn
* Yingzhen Li
* Alexander Matthews
Workshop summary:
While deep learning has been revolutionary for machine learning, most modern deep learning models cannot represent their uncertainty nor take advantage of the well studied tools of probability theory. This has started to change following recent developments of tools and techniques combining Bayesian approaches with deep learning. The intersection of the two fields has received great interest from the community, with the introduction of new deep learning models that take advantage of Bayesian techniques, and Bayesian models that incorporate deep learning elements. Many ideas from the 1990s are now being revisited in light of recent advances in the fields of approximate inference and deep learning, yielding many exciting new results.
Fri 8:00 a.m. - 8:05 a.m.
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Opening remarks
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Talk
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Fri 8:05 a.m. - 8:25 a.m.
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Invited talk
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Talk
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Fri 8:25 a.m. - 8:40 a.m.
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Contributed talk
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Talk
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Fri 8:40 a.m. - 9:00 a.m.
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Invited talk 2
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Talk
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Alexander Matthews 🔗 |
Fri 9:00 a.m. - 9:20 a.m.
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Contributed talk 2
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Talk
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Fri 9:20 a.m. - 9:35 a.m.
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Poster spotlights
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Spotlight
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Fri 9:35 a.m. - 10:35 a.m.
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Poster session
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Poster Session
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Sebastian Farquhar · Erik Daxberger · Andreas Look · Matt Benatan · Ruiyi Zhang · Marton Havasi · Fredrik Gustafsson · James A Brofos · Nabeel Seedat · Micha Livne · Ivan Ustyuzhaninov · Adam Cobb · Felix D McGregor · Patrick McClure · Tim R. Davidson · Gaurush Hiranandani · Sanjeev Arora · Masha Itkina · Didrik Nielsen · William Harvey · Matias Valdenegro-Toro · Stefano Peluchetti · Riccardo Moriconi · Tianyu Cui · Vaclav Smidl · Taylan Cemgil · Jack Fitzsimons · He Zhao · · mariana vargas vieyra · Apratim Bhattacharyya · Rahul Sharma · Geoffroy Dubourg-Felonneau · Jonathan Warrell · Slava Voloshynovskiy · Mihaela Rosca · Jiaming Song · Andrew Ross · Homa Fashandi · Ruiqi Gao · Hooshmand Shokri Razaghi · Joshua Chang · Zhenzhong Xiao · Vanessa Boehm · Giorgio Giannone · Ranganath Krishnan · Joe Davison · Arsenii Ashukha · Jeremiah Liu · Sicong (Sheldon) Huang · Evgenii Nikishin · Sunho Park · Nilesh Ahuja · Mahesh Subedar · · Artyom Gadetsky · Jhosimar Arias Figueroa · Tim G. J. Rudner · Waseem Aslam · Adrián Csiszárik · John Moberg · Ali Hebbal · Kathrin Grosse · Pekka Marttinen · Bang An · Hlynur Jónsson · Samuel Kessler · Abhishek Kumar · Mikhail Figurnov · Omesh Tickoo · Steindor Saemundsson · Ari Heljakka · Dániel Varga · Niklas Heim · Simone Rossi · Max Laves · Waseem Gharbieh · Nicholas Roberts · Luis Armando Pérez Rey · Matthew Willetts · Prithvijit Chakrabarty · Sumedh Ghaisas · Carl Shneider · Wray Buntine · Kamil Adamczewski · Xavier Gitiaux · Suwen Lin · Hao Fu · Gunnar Rätsch · Aidan Gomez · Erik Bodin · Dinh Phung · Lennart Svensson · Juliano Tusi Amaral Laganá Pinto · Milad Alizadeh · Jianzhun Du · Kevin Murphy · Beatrix Benkő · Shashaank Vattikuti · Jonathan Gordon · Christopher Kanan · Sontje Ihler · Darin Graham · Michael Teng · Louis Kirsch · Tomas Pevny · Taras Holotyak
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Fri 10:35 a.m. - 10:55 a.m.
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Invited talk 3
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Talk
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Fri 10:55 a.m. - 11:10 a.m.
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Contributed talk 3
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Talk
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Fri 11:10 a.m. - 11:30 a.m.
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Invited talk 4
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Talk
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Fri 11:30 a.m. - 11:50 a.m.
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Contributed talk 4
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Talk
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Fri 1:20 p.m. - 1:40 p.m.
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Invited talk 5
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Talk
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Fri 1:40 p.m. - 1:55 p.m.
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Contributed talk 5
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Talk
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Fri 1:55 p.m. - 2:10 p.m.
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Invited talk 6
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Talk
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Fri 2:10 p.m. - 2:30 p.m.
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Contributed talk 6
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Talk
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Fri 2:30 p.m. - 3:30 p.m.
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Poster session 2
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Poster session
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Fri 3:30 p.m. - 3:50 p.m.
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Contributed talk 7
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Talk
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Fri 3:50 p.m. - 4:05 p.m.
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Invited talk 7
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Talk
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Fri 4:05 p.m. - 4:25 p.m.
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Contributed talk 8
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Talk
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Fri 4:30 p.m. - 5:30 p.m.
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Panel session
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Fri 5:30 p.m. - 6:30 p.m.
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Poster session 3
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Poster session
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Author Information
Yarin Gal (University of Oxford)

Yarin leads the Oxford Applied and Theoretical Machine Learning (OATML) group. He is an Associate Professor of Machine Learning at the Computer Science department, University of Oxford. He is also the Tutorial Fellow in Computer Science at Christ Church, Oxford, and a Turing Fellow at the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence. Prior to his move to Oxford he was a Research Fellow in Computer Science at St Catharine’s College at the University of Cambridge. He obtained his PhD from the Cambridge machine learning group, working with Prof Zoubin Ghahramani and funded by the Google Europe Doctoral Fellowship. He made substantial contributions to early work in modern Bayesian deep learning—quantifying uncertainty in deep learning—and developed ML/AI tools that can inform their users when the tools are “guessing at random”. These tools have been deployed widely in industry and academia, with the tools used in medical applications, robotics, computer vision, astronomy, in the sciences, and by NASA. Beyond his academic work, Yarin works with industry on deploying robust ML tools safely and responsibly. He co-chairs the NASA FDL AI committee, and is an advisor with Canadian medical imaging company Imagia, Japanese robotics company Preferred Networks, as well as numerous startups.
José Miguel Hernández-Lobato (University of Cambridge)
Christos Louizos (University of Amsterdam)
Eric Nalisnick (University of Cambridge & DeepMind)
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.
Kevin Murphy (Google)
Max Welling (University of Amsterdam / Qualcomm AI Research)
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2020 Poster: Barking up the right tree: an approach to search over molecule synthesis DAGs »
John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato -
2020 Poster: Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models »
Andrew Jesson · Sören Mindermann · Uri Shalit · Yarin Gal -
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 Poster: How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? »
Mrinank Sharma · Sören Mindermann · Jan Brauner · Gavin Leech · Anna Stephenson · Tomáš Gavenčiak · Jan Kulveit · Yee Whye Teh · Leonid Chindelevitch · Yarin Gal -
2020 Spotlight: How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? »
Mrinank Sharma · Sören Mindermann · Jan Brauner · Gavin Leech · Anna Stephenson · Tomáš Gavenčiak · Jan Kulveit · Yee Whye Teh · Leonid Chindelevitch · Yarin Gal -
2020 Spotlight: Experimental design for MRI by greedy policy search »
Tim Bakker · Herke van Hoof · Max Welling -
2020 Spotlight: Barking up the right tree: an approach to search over molecule synthesis DAGs »
John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato -
2020 Session: Orals & Spotlights Track 15: COVID/Applications/Composition »
José Miguel Hernández-Lobato · Oliver Stegle -
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 : TBD »
Max Welling -
2019 : Poster session »
Sebastian Farquhar · Erik Daxberger · Andreas Look · Matt Benatan · Ruiyi Zhang · Marton Havasi · Fredrik Gustafsson · James A Brofos · Nabeel Seedat · Micha Livne · Ivan Ustyuzhaninov · Adam Cobb · Felix D McGregor · Patrick McClure · Tim R. Davidson · Gaurush Hiranandani · Sanjeev Arora · Masha Itkina · Didrik Nielsen · William Harvey · Matias Valdenegro-Toro · Stefano Peluchetti · Riccardo Moriconi · Tianyu Cui · Vaclav Smidl · Taylan Cemgil · Jack Fitzsimons · He Zhao · · mariana vargas vieyra · Apratim Bhattacharyya · Rahul Sharma · Geoffroy Dubourg-Felonneau · Jonathan Warrell · Slava Voloshynovskiy · Mihaela Rosca · Jiaming Song · Andrew Ross · Homa Fashandi · Ruiqi Gao · Hooshmand Shokri Razaghi · Joshua Chang · Zhenzhong Xiao · Vanessa Boehm · Giorgio Giannone · Ranganath Krishnan · Joe Davison · Arsenii Ashukha · Jeremiah Liu · Sicong (Sheldon) Huang · Evgenii Nikishin · Sunho Park · Nilesh Ahuja · Mahesh Subedar · · Artyom Gadetsky · Jhosimar Arias Figueroa · Tim G. J. Rudner · Waseem Aslam · Adrián Csiszárik · John Moberg · Ali Hebbal · Kathrin Grosse · Pekka Marttinen · Bang An · Hlynur Jónsson · Samuel Kessler · Abhishek Kumar · Mikhail Figurnov · Omesh Tickoo · Steindor Saemundsson · Ari Heljakka · Dániel Varga · Niklas Heim · Simone Rossi · Max Laves · Waseem Gharbieh · Nicholas Roberts · Luis Armando Pérez Rey · Matthew Willetts · Prithvijit Chakrabarty · Sumedh Ghaisas · Carl Shneider · Wray Buntine · Kamil Adamczewski · Xavier Gitiaux · Suwen Lin · Hao Fu · Gunnar Rätsch · Aidan Gomez · Erik Bodin · Dinh Phung · Lennart Svensson · Juliano Tusi Amaral Laganá Pinto · Milad Alizadeh · Jianzhun Du · Kevin Murphy · Beatrix Benkő · Shashaank Vattikuti · Jonathan Gordon · Christopher Kanan · Sontje Ihler · Darin Graham · Michael Teng · Louis Kirsch · Tomas Pevny · Taras Holotyak -
2019 : Keynote - ML »
Max Welling -
2019 Poster: Invert to Learn to Invert »
Patrick Putzky · Max Welling -
2019 Poster: Bayesian Batch Active Learning as Sparse Subset Approximation »
Robert Pinsler · Jonathan Gordon · Eric Nalisnick · José Miguel Hernández-Lobato -
2019 Poster: BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning »
Andreas Kirsch · Joost van Amersfoort · Yarin Gal -
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: Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model »
Wenbo Gong · Sebastian Tschiatschek · Sebastian Nowozin · Richard Turner · José Miguel Hernández-Lobato · Cheng Zhang -
2019 Poster: Bayesian Learning of Sum-Product Networks »
Martin Trapp · Robert Peharz · Hong Ge · Franz Pernkopf · Zoubin Ghahramani -
2019 Poster: A Model to Search for Synthesizable Molecules »
John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato -
2019 Poster: The Functional Neural Process »
Christos Louizos · Xiahan Shi · Klamer Schutte · Max Welling -
2019 Poster: Language as an Abstraction for Hierarchical Deep Reinforcement Learning »
YiDing Jiang · Shixiang (Shane) Gu · Kevin Murphy · Chelsea Finn -
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: Unsupervised learning of object structure and dynamics from videos »
Matthias Minderer · Chen Sun · Ruben Villegas · Forrester Cole · Kevin Murphy · Honglak Lee -
2019 Poster: Combinatorial Bayesian Optimization using the Graph Cartesian Product »
Changyong Oh · Jakub Tomczak · Stratis Gavves · Max Welling -
2019 Poster: Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning »
David Janz · Jiri Hron · Przemysław Mazur · Katja Hofmann · José Miguel Hernández-Lobato · Sebastian Tschiatschek -
2018 : Making the Case for using more Inductive Bias in Deep Learning »
Max Welling -
2018 Workshop: Machine Learning for Molecules and Materials »
José Miguel Hernández-Lobato · Klaus-Robert Müller · Brooks Paige · Matt Kusner · Stefan Chmiela · Kristof Schütt -
2018 : Panel disucssion »
Max Welling · Tim Genewein · Edwin Park · Song Han -
2018 : TBC 15 »
Yarin Gal -
2018 : Invited Speaker #5 Yarin Gal »
Yarin Gal -
2018 : Efficient Computation of Deep Convolutional Neural Networks: A Quantization Perspective »
Max Welling -
2018 : Prof. Max Welling »
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 : Opening Remarks »
Yarin Gal -
2018 Poster: BRUNO: A Deep Recurrent Model for Exchangeable Data »
Iryna Korshunova · Jonas Degrave · Ferenc Huszar · Yarin Gal · Arthur Gretton · Joni Dambre -
2018 Poster: Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo »
Marton Havasi · José Miguel Hernández-Lobato · Juan J. Murillo-Fuentes -
2018 Poster: MetaGAN: An Adversarial Approach to Few-Shot Learning »
Ruixiang ZHANG · Tong Che · Zoubin Ghahramani · Yoshua Bengio · Yangqiu Song -
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 : Panel Session »
Neil Lawrence · Finale Doshi-Velez · Zoubin Ghahramani · Yann LeCun · Max Welling · Yee Whye Teh · Ole Winther -
2017 : Deep Bayes for Distributed Learning, Uncertainty Quantification and Compression »
Max Welling -
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: Bayesian optimization for science and engineering »
Ruben Martinez-Cantin · José Miguel Hernández-Lobato · Javier Gonzalez -
2017 : Closing remarks »
José Miguel Hernández-Lobato -
2017 : Panel session »
Iain Murray · Max Welling · Juan Carrasquilla · Anatole von Lilienfeld · Gilles Louppe · Kyle Cranmer -
2017 : Panel: On the Foundations and Future of Approximate Inference »
David Blei · Zoubin Ghahramani · Katherine Heller · Tim Salimans · Max Welling · Matthew D. Hoffman -
2017 : Coffee break and Poster Session II »
Mohamed Kane · Albert Haque · Vagelis Papalexakis · John Guibas · Peter Li · Carlos Arias · Eric Nalisnick · Padhraic Smyth · Frank Rudzicz · Xia Zhu · Theodore Willke · Noemie Elhadad · Hans Raffauf · Harini Suresh · Paroma Varma · Yisong Yue · Ognjen (Oggi) Rudovic · Luca Foschini · Syed Rameel Ahmad · Hasham ul Haq · Valerio Maggio · Giuseppe Jurman · Sonali Parbhoo · Pouya Bashivan · Jyoti Islam · Mirco Musolesi · Chris Wu · Alexander Ratner · Jared Dunnmon · Cristóbal Esteban · Aram Galstyan · Greg Ver Steeg · Hrant Khachatrian · Marc Górriz · Mihaela van der Schaar · Anton Nemchenko · Manasi Patwardhan · Tanay Tandon -
2017 : Poster Spotlights »
Francesco Locatello · Ari Pakman · Da Tang · Thomas Rainforth · Zalan Borsos · Marko Järvenpää · Eric Nalisnick · Gabriele Abbati · XIAOYU LU · Jonathan Huggins · Rachit Singh · Rui Luo -
2017 : Invited talk 1: Deep recurrent inverse modeling for radio astronomy and fast MRI imaging »
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 Workshop: Machine Learning for Molecules and Materials »
Kristof Schütt · Klaus-Robert Müller · Anatole von Lilienfeld · José Miguel Hernández-Lobato · Klaus-Robert Müller · Alan Aspuru-Guzik · Bharath Ramsundar · Matt Kusner · Brooks Paige · Stefan Chmiela · Alexandre Tkatchenko · Anatole von Lilienfeld · Koji Tsuda -
2017 : Panel: "Should we prioritize research on human-like AI or something different?" »
Cynthia Dwork · David Runciman · Zoubin Ghahramani -
2017 Symposium: Kinds of intelligence: types, tests and meeting the needs of society »
José Hernández-Orallo · Zoubin Ghahramani · Tomaso Poggio · Adrian Weller · Matthew Crosby -
2017 Poster: Concrete Dropout »
Yarin Gal · Jiri Hron · Alex Kendall -
2017 Poster: Causal Effect Inference with Deep Latent-Variable Models »
Christos Louizos · Uri Shalit · Joris Mooij · David Sontag · Richard Zemel · Max Welling -
2017 Poster: What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? »
Alex Kendall · Yarin Gal -
2017 Spotlight: What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? »
Alex Kendall · Yarin Gal -
2017 Poster: Bayesian Compression for Deep Learning »
Christos Louizos · Karen Ullrich · Max Welling -
2017 Poster: Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning »
Shixiang (Shane) Gu · Timothy Lillicrap · Richard Turner · Zoubin Ghahramani · Bernhard Schölkopf · Sergey Levine -
2017 Poster: Real Time Image Saliency for Black Box Classifiers »
Piotr Dabkowski · Yarin Gal -
2016 : Panel Discussion »
Shakir Mohamed · David Blei · Ryan Adams · José Miguel Hernández-Lobato · Ian Goodfellow · Yarin Gal -
2016 : Automatic Chemical Design using Variational Autoencoders »
José Miguel Hernández-Lobato -
2016 : Alpha divergence minimization for Bayesian deep learning »
José Miguel Hernández-Lobato -
2016 : Max Welling : Making Deep Learning Efficient Through Sparsification »
Max Welling -
2016 : Automatic Discovery of the Statistical Types of Variables in a Dataset »
Isabel Valera · Zoubin Ghahramani -
2016 : History of Bayesian neural networks »
Zoubin Ghahramani -
2016 Workshop: Bayesian Deep Learning »
Yarin Gal · Christos Louizos · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2016 Workshop: Towards an Artificial Intelligence for Data Science »
Charles Sutton · James Geddes · Zoubin Ghahramani · Padhraic Smyth · Chris Williams -
2016 : How Machine Learning Research Can Address Key Societal and Governance Issues »
Zoubin Ghahramani -
2016 Workshop: People and machines: Public views on machine learning, and what this means for machine learning researchers »
Susannah Odell · Peter Donnelly · Jessica Montgomery · Sabine Hauert · Zoubin Ghahramani · Katherine Gorman -
2016 Workshop: Advances in Approximate Bayesian Inference »
Tamara Broderick · Stephan Mandt · James McInerney · Dustin Tran · David Blei · Kevin Murphy · Andrew Gelman · Michael I Jordan -
2016 Poster: A Theoretically Grounded Application of Dropout in Recurrent Neural Networks »
Yarin Gal · Zoubin Ghahramani -
2016 Poster: Improving Variational Autoencoders with Inverse Autoregressive Flow »
Diederik Kingma · Tim Salimans · Rafal Jozefowicz · Peter Chen · Xi Chen · Ilya Sutskever · Max Welling -
2016 Poster: Distributed Flexible Nonlinear Tensor Factorization »
Shandian Zhe · Kai Zhang · Pengyuan Wang · Kuang-chih Lee · Zenglin Xu · Yuan Qi · Zoubin Ghahramani -
2015 : Bayesian Optimization »
Zoubin Ghahramani · Bobak Shahriari -
2015 Workshop: Black box learning and inference »
Josh Tenenbaum · Jan-Willem van de Meent · Tejas Kulkarni · S. M. Ali Eslami · Brooks Paige · Frank Wood · Zoubin Ghahramani -
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 : *Max Welling* Optimization Monte Carlo »
Max Welling -
2015 Symposium: Deep Learning Symposium »
Yoshua Bengio · Marc'Aurelio Ranzato · Honglak Lee · Max Welling · Andrew Y Ng -
2015 Poster: Particle Gibbs for Infinite Hidden Markov Models »
Nilesh Tripuraneni · Shixiang (Shane) Gu · Hong Ge · Zoubin Ghahramani -
2015 Poster: Neural Adaptive Sequential Monte Carlo »
Shixiang (Shane) Gu · Zoubin Ghahramani · Richard Turner -
2015 Poster: Bayesian dark knowledge »
Anoop Korattikara Balan · Vivek Rathod · Kevin Murphy · Max Welling -
2015 Poster: MCMC for Variationally Sparse Gaussian Processes »
James Hensman · Alexander Matthews · Maurizio Filippone · Zoubin Ghahramani -
2015 Poster: Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference »
Ted Meeds · Max Welling -
2015 Poster: Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions »
Amar Shah · Zoubin Ghahramani -
2015 Poster: Stochastic Expectation Propagation »
Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner -
2015 Spotlight: Stochastic Expectation Propagation »
Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner -
2015 Invited Talk: Probabilistic Machine Learning: Foundations and Frontiers »
Zoubin Ghahramani -
2015 Poster: Statistical Model Criticism using Kernel Two Sample Tests »
James R Lloyd · Zoubin Ghahramani -
2015 Poster: Variational Dropout and the Local Reparameterization Trick »
Diederik Kingma · Tim Salimans · Max Welling -
2014 Workshop: Bayesian Optimization in Academia and Industry »
Zoubin Ghahramani · Ryan Adams · Matthew Hoffman · Kevin Swersky · Jasper Snoek -
2014 Workshop: ABC in Montreal »
Max Welling · Neil D Lawrence · Richard D Wilkinson · Ted Meeds · Christian X Robert -
2014 Poster: Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models »
Yarin Gal · Mark van der Wilk · Carl Edward Rasmussen -
2014 Poster: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2014 Poster: Semi-supervised Learning with Deep Generative Models »
Diederik Kingma · Shakir Mohamed · Danilo Jimenez Rezende · Max Welling -
2014 Poster: Gaussian Process Volatility Model »
Yue Wu · José Miguel Hernández-Lobato · Zoubin Ghahramani -
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 -
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 »
Isabel Valera · Zoubin Ghahramani -
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 Poster: Learning Feature Selection Dependencies in Multi-task Learning »
Daniel Hernández-lobato · José Miguel Hernández-Lobato -
2013 Poster: Gaussian Process Conditional Copulas with Applications to Financial Time Series »
José Miguel Hernández-Lobato · James R Lloyd · Daniel Hernández-lobato -
2013 Session: Oral Session 5 »
Zoubin Ghahramani -
2012 Poster: Collaborative Gaussian Processes for Preference Learning »
Neil Houlsby · José Miguel Hernández-Lobato · Ferenc Huszar · Zoubin Ghahramani -
2012 Poster: A nonparametric variable clustering model »
David A Knowles · Konstantina Palla · Zoubin Ghahramani -
2012 Poster: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Poster: Random function priors for exchangeable graphs and arrays »
James R Lloyd · Daniel Roy · Peter Orbanz · Zoubin Ghahramani -
2012 Poster: Active Learning of Model Evidence Using Bayesian Quadrature »
Michael A Osborne · David Duvenaud · Roman Garnett · Carl Edward Rasmussen · Stephen J Roberts · Zoubin Ghahramani -
2012 Poster: Continuous Relaxations for Discrete Hamiltonian Monte Carlo »
Zoubin Ghahramani · Yichuan Zhang · Charles Sutton · Amos Storkey -
2012 Spotlight: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Spotlight: Continuous Relaxations for Discrete Hamiltonian Monte Carlo »
Zoubin Ghahramani · Yichuan Zhang · Charles Sutton · Amos Storkey -
2012 Poster: The Time-Marginalized Coalescent Prior for Hierarchical Clustering »
Levi Boyles · Max Welling -
2011 Workshop: Copulas in Machine Learning »
Gal Elidan · Zoubin Ghahramani · John Lafferty -
2011 Poster: Testing a Bayesian Measure of Representativeness Using a Large Image Database »
Joshua T Abbott · Katherine Heller · Zoubin Ghahramani · Tom Griffiths -
2011 Poster: Statistical Tests for Optimization Efficiency »
Levi Boyles · Anoop Korattikara · Deva Ramanan · Max Welling -
2011 Poster: Robust Multi-Class Gaussian Process Classification »
Daniel Hernández-lobato · José Miguel Hernández-Lobato · Pierre Dupont -
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 »
Zoubin Ghahramani -
2010 Oral: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Poster: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Poster: On Herding and the Perceptron Cycling Theorem »
Andrew E Gelfand · Yutian Chen · Laurens van der Maaten · Max Welling -
2010 Spotlight: Copula Processes »
Andrew Wilson · Zoubin Ghahramani -
2010 Poster: Copula Processes »
Andrew Wilson · Zoubin Ghahramani -
2009 Workshop: Nonparametric Bayes »
Dilan Gorur · Francois Caron · Yee Whye Teh · David B Dunson · Zoubin Ghahramani · Michael Jordan -
2009 Poster: Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process »
Shakir Mohamed · David A Knowles · Zoubin Ghahramani · Finale P Doshi-Velez -
2008 Session: Oral session 10: Nonparametric Processes, Scene Processing and Image Statistics »
Max Welling -
2008 Poster: The Infinite Factorial Hidden Markov Model »
Jurgen Van Gael · Yee Whye Teh · Zoubin Ghahramani -
2008 Poster: Bayesian Exponential Family PCA »
Shakir Mohamed · Katherine Heller · Zoubin Ghahramani -
2008 Poster: Asynchronous Distributed Learning of Topic Models »
Arthur Asuncion · Padhraic Smyth · Max Welling -
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 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: Hidden Common Cause Relations in Relational Learning »
Ricardo Silva · Wei Chu · Zoubin Ghahramani -
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 Poster: Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach »
José Miguel Hernández-Lobato · Tjeerd M Dijkstra · Tom Heskes -
2007 Spotlight: Infinite State Bayes-Nets for Structured Domains »
Max Welling · Ian Porteous · Evgeniy Bart -
2007 Spotlight: Hidden Common Cause Relations in Relational Learning »
Ricardo Silva · Wei Chu · Zoubin Ghahramani -
2006 Poster: Relational Learning with Gaussian Processes »
Wei Chu · Vikas Sindhwani · Zoubin Ghahramani · Sathiya Selvaraj Keerthi -
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 Poster: Modeling Dyadic Data with Binary Latent Features »
Ted Meeds · Zoubin Ghahramani · Radford M Neal · Sam T Roweis -
2006 Spotlight: Accelerated Variational Dirichlet Process Mixtures »
Kenichi Kurihara · Max Welling · Nikos Vlassis -
2006 Spotlight: Modeling Dyadic Data with Binary Latent Features »
Ted Meeds · Zoubin Ghahramani · Radford M Neal · Sam T Roweis -
2006 Poster: A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation »
Yee Whye Teh · David Newman · Max Welling