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Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects

Apr 21, 2020
Will Y. Zou, Smitha Shyam, Michael Mui, Mingshi Wang, Jan Pedersen, Zoubin Ghahramani


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Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

Apr 13, 2020
Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani


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DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models

Feb 07, 2020
Mohamed Tarek, Kai Xu, Martin Trapp, Hong Ge, Zoubin Ghahramani


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Resource-Efficient Neural Networks for Embedded Systems

Jan 07, 2020
Wolfgang Roth, Günther Schindler, Matthias Zöhrer, Lukas Pfeifenberger, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani

* arXiv admin note: text overlap with arXiv:1812.02240 

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Bayesian Learning of Sum-Product Networks

May 26, 2019
Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani


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Efficient and Robust Machine Learning for Real-World Systems

Dec 05, 2018
Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer, Lukas Pfeifenberger, Guenther Schindler, Holger Froening, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin Ghahramani


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Handling Incomplete Heterogeneous Data using VAEs

Oct 30, 2018
Alfredo Nazabal, Pablo M. Olmos, Zoubin Ghahramani, Isabel Valera


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Automatic Bayesian Density Analysis

Oct 03, 2018
Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera


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Probabilistic Meta-Representations Of Neural Networks

Oct 01, 2018
Theofanis Karaletsos, Peter Dayan, Zoubin Ghahramani

* presented at UAI 2018 Uncertainty In Deep Learning Workshop (UDL AUG. 2018) 

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Gaussian Process Behaviour in Wide Deep Neural Networks

Aug 16, 2018
Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani

* This work substantially extends the work of Matthews et al. (2018) published at the International Conference on Learning Representations (ICLR) 2018 

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Ergodic Measure Preserving Flows

Aug 13, 2018
Yichuan Zhang, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani


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Antithetic and Monte Carlo kernel estimators for partial rankings

Jul 25, 2018
Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani


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Few-shot learning of neural networks from scratch by pseudo example optimization

Jul 05, 2018
Akisato Kimura, Zoubin Ghahramani, Koh Takeuchi, Tomoharu Iwata, Naonori Ueda

* 14 pages, 2 figures, will be presented at BMVC2018 

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Variational Bayesian dropout: pitfalls and fixes

Jul 05, 2018
Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani

* Extended version of the paper accepted to ICML 2018: more details in the proofs, few minor modifications 

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Probabilistic Deep Learning using Random Sum-Product Networks

Jun 22, 2018
Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani


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The Mirage of Action-Dependent Baselines in Reinforcement Learning

Apr 06, 2018
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine

* Updated to address comments from ICLR workshop reviewers 

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General Latent Feature Models for Heterogeneous Datasets

Mar 08, 2018
Isabel Valera, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani

* Software library available at https://github.com/ivaleraM/GLFM 

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Weakly supervised collective feature learning from curated media

Feb 13, 2018
Yusuke Mukuta, Akisato Kimura, David B Adrian, Zoubin Ghahramani

* Published in the Proceedings of AAAI Conferenrence on Artificial Intelligence (AAAI2018) 

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Variational Gaussian Dropout is not Bayesian

Nov 08, 2017
Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani


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Magnetic Hamiltonian Monte Carlo

Aug 19, 2017
Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner

* 34th International Conference on Machine Learning (ICML 2017) 

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General Latent Feature Modeling for Data Exploration Tasks

Jul 26, 2017
Isabel Valera, Melanie F. Pradier, Zoubin Ghahramani

* presented at 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017), Sydney, NSW, Australia 

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Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes

Jul 19, 2017
Tomoharu Iwata, Zoubin Ghahramani


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One-Shot Learning in Discriminative Neural Networks

Jul 18, 2017
Jordan Burgess, James Robert Lloyd, Zoubin Ghahramani

* 3 pages, 3 figures 

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Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks

Jul 08, 2017
John Bradshaw, Alexander G. de G. Matthews, Zoubin Ghahramani


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Bayesian inference on random simple graphs with power law degree distributions

Jun 18, 2017
Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James, Seungjin Choi


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Lost Relatives of the Gumbel Trick

Jun 13, 2017
Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller

* 34th International Conference on Machine Learning (ICML 2017) 

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Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

Jun 01, 2017
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine


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Deep Bayesian Active Learning with Image Data

Mar 08, 2017
Yarin Gal, Riashat Islam, Zoubin Ghahramani


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Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic

Feb 27, 2017
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine

* Conference Paper at the International Conference on Learning Representations (ICLR) 2017 

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GPflow: A Gaussian process library using TensorFlow

Oct 27, 2016
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman


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