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Featured Co-authors

Trevor Darrell
209 publications
Chao Chen
123 publications
Kristian Kersting
114 publications
Zoubin Ghahramani
90 publications
Dmitry Vetrov
62 publications
Christoph H. Lampert
37 publications
Lisa Anne Hendricks
28 publications
Daniel Hernández-Lobato
24 publications
Edwin V. Bonilla
19 publications
Jose A. Lozano
19 publications
Nuria Oliver
19 publications

∙ 02/02/2023

Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations

Several recent works encourage the use of a Bayesian framework when asse...

0 Ainhize Barrainkua, et al. ∙

∙ 11/14/2022

A Survey on Preserving Fairness Guarantees in Changing Environments

Human lives are increasingly being affected by the outcomes of automated...

0 Ainhize Barrainkua, et al. ∙

∙ 11/07/2022

Okapi: Generalising Better by Making Statistical Matches Match

We propose Okapi, a simple, efficient, and general method for robust sem...

0 Myles Bartlett, et al. ∙

∙ 09/27/2022

A Snapshot of the Frontiers of Client Selection in Federated Learning

Federated learning (FL) has been proposed as a privacy-preserving approa...

0 Gergely Dániel Németh, et al. ∙

∙ 08/03/2022

RealPatch: A Statistical Matching Framework for Model Patching with Real Samples

Machine learning classifiers are typically trained to minimise the avera...

7 Sara Romiti, et al. ∙

∙ 03/24/2022

Addressing Missing Sources with Adversarial Support-Matching

When trained on diverse labeled data, machine learning models have prove...

0 Thomas Kehrenberg, et al. ∙

∙ 08/12/2020

Null-sampling for Interpretable and Fair Representations

We propose to learn invariant representations, in the data domain, to ac...

0 Thomas Kehrenberg, et al. ∙

∙ 04/14/2020

Contrastive Examples for Addressing the Tyranny of the Majority

Computer vision algorithms, e.g. for face recognition, favour groups of ...

6 Viktoriia Sharmanska, et al. ∙

∙ 03/12/2020

Causal datasheet: An approximate guide to practically assess Bayesian networks in the real world

In solving real-world problems like changing healthcare-seeking behavior...

0 Bradley Butcher, et al. ∙

∙ 11/22/2019

Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution

Learning models with discrete latent variables using stochastic gradient...

0 Artyom Gadetsky, et al. ∙

∙ 10/15/2018

Neural Styling for Interpretable Fair Representations

We observe a rapid increase in machine learning models for learning data...

0 Novi Quadrianto, et al. ∙

∙ 10/12/2018

Interpretable Fairness via Target Labels in Gaussian Process Models

Addressing fairness in machine learning models has recently attracted a ...

0 Thomas Kehrenberg, et al. ∙

∙ 09/14/2016

Gray-box inference for structured Gaussian process models

We develop an automated variational inference method for Bayesian struct...

0 Pietro Galliani, et al. ∙

∙ 10/01/2014

Learning to Transfer Privileged Information

We introduce a learning framework called learning using privileged infor...

0 Viktoriia Sharmanska, et al. ∙

∙ 07/01/2014

Mind the Nuisance: Gaussian Process Classification using Privileged Noise

The learning with privileged information setting has recently attracted ...

0 Daniel Hernández-Lobato, et al. ∙

∙ 09/26/2013

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

We propose a probabilistic model to infer supervised latent variables in...

0 Novi Quadrianto, et al. ∙

∙ 07/15/2013

Bayesian Structured Prediction Using Gaussian Processes

We introduce a conceptually novel structured prediction model, GPstruct,...

0 Sebastien Bratieres, et al. ∙

∙ 06/18/2012

The Most Persistent Soft-Clique in a Set of Sampled Graphs

When searching for characteristic subpatterns in potentially noisy graph...

0 Novi Quadrianto, et al. ∙

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