Jiri Hron | DeepAI

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Featured Co-authors

Michael I. Jordan
283 publications
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Zoubin Ghahramani
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∙ 05/12/2023

The Progression of Disparities within the Criminal Justice System: Differential Enforcement and Risk Assessment Instruments

Algorithmic risk assessment instruments (RAIs) increasingly inform decis...

0 Miri Zilka, et al. ∙

∙ 02/18/2023

Optimising Human-Machine Collaboration for Efficient High-Precision Information Extraction from Text Documents

While humans can extract information from unstructured text with high pr...

0 Bradley Butcher, et al. ∙

∙ 06/27/2022

Modeling Content Creator Incentives on Algorithm-Curated Platforms

Content creators compete for user attention. Their reach crucially depen...

6 Jiri Hron, et al. ∙

∙ 06/15/2022

Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling

We introduce repriorisation, a data-dependent reparameterisation which t...

0 Jiri Hron, et al. ∙

∙ 06/28/2021

On component interactions in two-stage recommender systems

Thanks to their scalability, two-stage recommenders are used by many of ...

5 Jiri Hron, et al. ∙

∙ 09/01/2020

Exploration in two-stage recommender systems

Two-stage recommender systems are widely adopted in industry due to thei...

0 Jiri Hron, et al. ∙

∙ 06/18/2020

Exact posterior distributions of wide Bayesian neural networks

Recent work has shown that the prior over functions induced by a deep Ba...

0 Jiri Hron, et al. ∙

∙ 06/18/2020

Infinite attention: NNGP and NTK for deep attention networks

There is a growing amount of literature on the relationship between wide...

0 Jiri Hron, et al. ∙

∙ 12/05/2019

Neural Tangents: Fast and Easy Infinite Neural Networks in Python

Neural Tangents is a library designed to enable research into infinite-w...

14 Roman Novak, et al. ∙

∙ 03/09/2019

Orthogonal Estimation of Wasserstein Distances

Wasserstein distances are increasingly used in a wide variety of applica...

7 Mark Rowland, et al. ∙

∙ 10/15/2018

Successor Uncertainties: exploration and uncertainty in temporal difference learning

We consider the problem of balancing exploration and exploitation in seq...

0 David Janz, et al. ∙

∙ 07/05/2018

Variational Bayesian dropout: pitfalls and fixes

Dropout, a stochastic regularisation technique for training of neural ne...

0 Jiri Hron, et al. ∙

∙ 04/30/2018

Gaussian Process Behaviour in Wide Deep Neural Networks

Whilst deep neural networks have shown great empirical success, there is...

0 Alexander G. de G. Matthews, et al. ∙

∙ 11/08/2017

Variational Gaussian Dropout is not Bayesian

Gaussian multiplicative noise is commonly used as a stochastic regularis...

0 Jiri Hron, et al. ∙

∙ 05/22/2017

Concrete Dropout

Dropout is used as a practical tool to obtain uncertainty estimates in l...

0 Yarin Gal, et al. ∙

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