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Powerpropagation: A sparsity inducing weight reparameterisation


Oct 06, 2021
Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh

* Accepted at NeurIPS 2021 

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InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents


Jun 25, 2021
Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth


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On Contrastive Representations of Stochastic Processes


Jun 18, 2021
Emile Mathieu, Adam Foster, Yee Whye Teh


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Group Equivariant Subsampling


Jun 10, 2021
Jin Xu, Hyunjik Kim, Tom Rainforth, Yee Whye Teh


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BayesIMP: Uncertainty Quantification for Causal Data Fusion


Jun 07, 2021
Siu Lun Chau, Jean-Fran├žois Ton, Javier Gonz├ílez, Yee Whye Teh, Dino Sejdinovic

* 10 pages main text, 10 pages supplementary materials 

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COIN: COmpression with Implicit Neural representations


Mar 03, 2021
Emilien Dupont, Adam Goliński, Milad Alizadeh, Yee Whye Teh, Arnaud Doucet


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Generative Models as Distributions of Functions


Feb 09, 2021
Emilien Dupont, Yee Whye Teh, Arnaud Doucet


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LieTransformer: Equivariant self-attention for Lie Groups


Dec 20, 2020
Michael Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim


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Equivariant Conditional Neural Processes


Nov 25, 2020
Peter Holderrieth, Michael Hutchinson, Yee Whye Teh


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Attentive Clustering Processes


Oct 29, 2020
Ari Pakman, Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski


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Behavior Priors for Efficient Reinforcement Learning


Oct 27, 2020
Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess

* Submitted to Journal of Machine Learning Research (JMLR) 

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Importance Weighted Policy Learning and Adaption


Sep 10, 2020
Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins, Jan Humplik, Leonard Hasenclever, Rae Jeong, Yee Whye Teh, Nicolas Heess


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Bootstrapping Neural Processes


Aug 07, 2020
Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh


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Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning


Aug 06, 2020
Bryn Elesedy, Varun Kanade, Yee Whye Teh

* 16 pages, 1 figure. Corrections to previous version 

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On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission


Jul 27, 2020
Mrinank Sharma, S├Âren Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tom├í┼í Gaven─Źiak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal


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Bayesian Deep Ensembles via the Neural Tangent Kernel


Jul 11, 2020
Bobby He, Balaji Lakshminarayanan, Yee Whye Teh


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Neural Ensemble Search for Performant and Calibrated Predictions


Jun 15, 2020
Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris Holmes, Frank Hutter, Yee Whye Teh


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Non-exchangeable feature allocation models with sublinear growth of the feature sizes


Mar 30, 2020
Giuseppe Di Benedetto, Fran├žois Caron, Yee Whye Teh

* Accepted to AISTATS 2020 

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Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network


Mar 04, 2020
Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal


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Pruning untrained neural networks: Principles and Analysis


Feb 19, 2020
Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh

* 50 pages, 12 figures 

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Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise


Feb 13, 2020
Umut ┼×im┼čekli, Lingjiong Zhu, Yee Whye Teh, Mert G├╝rb├╝zbalaban

* 26 pages 

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MetaFun: Meta-Learning with Iterative Functional Updates


Dec 05, 2019
Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam R. Kosiorek, Yee Whye Teh


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Amortized Rejection Sampling in Universal Probabilistic Programming


Nov 30, 2019
Saeid Naderiparizi, Adam ┼Ücibior, Andreas Munk, Mehrdad Ghadiri, At─▒l─▒m G├╝ne┼č Baydin, Bradley Gram-Hansen, Christian Schroeder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood


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A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments


Nov 01, 2019
Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth


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Continual Unsupervised Representation Learning


Oct 31, 2019
Dushyant Rao, Francesco Visin, Andrei A. Rusu, Yee Whye Teh, Razvan Pascanu, Raia Hadsell

* NeurIPS 2019 

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Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support


Oct 29, 2019
Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth


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Deep Amortized Clustering


Sep 30, 2019
Juho Lee, Yoonho Lee, Yee Whye Teh


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