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On weighted uncertainty sampling in active learning
This note explores probabilistic sampling weighted by uncertainty in active learning.
Easy High-Dimensional Likelihood-Free Inference
We introduce a framework using Generative Adversarial Networks (GANs) for likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC) where we replace the black-box simulator model with an approximator network and generate a rich set of summary features in a data driven fashion.
The Lovász ϑ function, SVMs and finding large dense subgraphs
We show that the random graph with a planted clique is an example of $SVM-\theta$ graph, and as a consequence a SVM based approach easily identifies the clique in large graphs and is competitive with the state-of-the-art.
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