Several recent works encourage the use of a Bayesian framework when asse...
Human lives are increasingly being affected by the outcomes of automated...
We propose Okapi, a simple, efficient, and general method for robust
sem...
Federated learning (FL) has been proposed as a privacy-preserving approa...
Machine learning classifiers are typically trained to minimise the avera...
When trained on diverse labeled data, machine learning models have prove...
We propose to learn invariant representations, in the data domain, to ac...
Computer vision algorithms, e.g. for face recognition, favour groups of
...
In solving real-world problems like changing healthcare-seeking behavior...
Learning models with discrete latent variables using stochastic gradient...
We observe a rapid increase in machine learning models for learning data...
Addressing fairness in machine learning models has recently attracted a ...
We develop an automated variational inference method for Bayesian struct...
We introduce a learning framework called learning using privileged
infor...
The learning with privileged information setting has recently attracted ...
We propose a probabilistic model to infer supervised latent variables in...
We introduce a conceptually novel structured prediction model, GPstruct,...
When searching for characteristic subpatterns in potentially noisy graph...