21 citations · 122 across the 18 of their papers we have counts for
19 papers
Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations
Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano +1
Several recent works encourage the use of a Bayesian framework when assessing performance and fairness metrics of a classification algorithm in a supervised setting. We propose the…
A Survey on Preserving Fairness Guarantees in Changing Environments
Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano +1
Human lives are increasingly being affected by the outcomes of automated decision-making systems and it is essential for the latter to be, not only accurate, but also fair. The lit…
Okapi: Generalising Better by Making Statistical Matches Match
Myles Bartlett, Sara Romiti, Viktoriia Sharmanska +1
We propose Okapi, a simple, efficient, and general method for robust semi-supervised learning based on online statistical matching. Our method uses a nearest-neighbours-based match…
A Snapshot of the Frontiers of Client Selection in Federated Learning
Gergely Dániel Németh, Miguel Ángel Lozano, Novi Quadrianto +1
Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a num…
RealPatch: A Statistical Matching Framework for Model Patching with Real Samples
Sara Romiti, Christopher Inskip, Viktoriia Sharmanska +1
Machine learning classifiers are typically trained to minimise the average error across a dataset. Unfortunately, in practice, this process often exploits spurious correlations cau…
Addressing Missing Sources with Adversarial Support-Matching
Thomas Kehrenberg, Myles Bartlett, Viktoriia Sharmanska +1
When trained on diverse labeled data, machine learning models have proven themselves to be a powerful tool in all facets of society. However, due to budget limitations, deliberate…
Null-sampling for Interpretable and Fair Representations
Thomas Kehrenberg, Myles Bartlett, Oliver Thomas +1
We propose to learn invariant representations, in the data domain, to achieve interpretability in algorithmic fairness. Invariance implies a selectivity for high level, relevant co…
Contrastive Examples for Addressing the Tyranny of the Majority
Viktoriia Sharmanska, Lisa Anne Hendricks, Trevor Darrell +1
Computer vision algorithms, e.g. for face recognition, favour groups of individuals that are better represented in the training data. This happens because of the generalization tha…
Causal datasheet: An approximate guide to practically assess Bayesian networks in the real world
Bradley Butcher, Vincent S. Huang, Jeremy Reffin +3
In solving real-world problems like changing healthcare-seeking behaviors, designing interventions to improve downstream outcomes requires an understanding of the causal links with…
An empirical, Bayesian approach to modelling the impact of weather on crop yield: maize in the US
Raphael Shirley, Edward Pope, Myles Bartlett +8
We apply an empirical, data-driven approach for describing crop yield as a function of monthly temperature and precipitation by employing generative probabilistic models with param…
Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution
Artyom Gadetsky, Kirill Struminsky, Christopher Robinson +2
Learning models with discrete latent variables using stochastic gradient descent remains a challenge due to the high variance of gradient estimates. Modern variance reduction techn…
Discovering Fair Representations in the Data Domain
Novi Quadrianto, Viktoriia Sharmanska, Oliver Thomas
Interpretability and fairness are critical in computer vision and machine learning applications, in particular when dealing with human outcomes, e.g. inviting or not inviting for a…
Tuning Fairness by Balancing Target Labels
Thomas Kehrenberg, Zexun Chen, Novi Quadrianto
The issue of fairness in machine learning models has recently attracted a lot of attention as ensuring it will ensure continued confidence of the general public in the deployment o…
Gray-box inference for structured Gaussian process models
Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla +1
We develop an automated variational inference method for Bayesian structured prediction problems with Gaussian process (GP) priors and linear-chain likelihoods. Our approach does n…
Learning to Transfer Privileged Information
Viktoriia Sharmanska, Novi Quadrianto, Christoph H. Lampert
We introduce a learning framework called learning using privileged information (LUPI) to the computer vision field. We focus on the prototypical computer vision problem of teaching…
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
Daniel Hernández-Lobato, Viktoriia Sharmanska, Kristian Kersting +2
The learning with privileged information setting has recently attracted a lot of attention within the machine learning community, as it allows the integration of additional knowled…
The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models
Novi Quadrianto, Viktoriia Sharmanska, David A. Knowles +1
We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model allows simultaneous inference of the number of binary laten…
Bayesian Structured Prediction Using Gaussian Processes
Sebastien Bratieres, Novi Quadrianto, Zoubin Ghahramani
We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing…
The Most Persistent Soft-Clique in a Set of Sampled Graphs
Novi Quadrianto, Chao Chen, Christoph Lampert
When searching for characteristic subpatterns in potentially noisy graph data, it appears self-evident that having multiple observations would be better than having just one. Howev…