33 citations · 94 across the 13 of their papers we have counts for
17 papers
Continual Density Ratio Estimation in an Online Setting
Yu Chen, Song Liu, Tom Diethe +1
In online applications with streaming data, awareness of how far the training or test set has shifted away from the original dataset can be crucial to the performance of the model.…
Interpretable Anomaly Detection with Mondrian P{ó}lya Forests on Data Streams
Charlie Dickens, Eric Meissner, Pablo G. Moreno +1
Anomaly detection at scale is an extremely challenging problem of great practicality. When data is large and high-dimensional, it can be difficult to detect which observations do n…
Optimal Continual Learning has Perfect Memory and is NP-hard
Jeremias Knoblauch, Hisham Husain, Tom Diethe
Continual Learning (CL) algorithms incrementally learn a predictor or representation across multiple sequentially observed tasks. Designing CL algorithms that perform reliably and…
Similarity of Neural Networks with Gradients
Shuai Tang, Wesley J. Maddox, Charlie Dickens +2
A suitable similarity index for comparing learnt neural networks plays an important role in understanding the behaviour of the highly-nonlinear functions, and can provide insights…
Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text
Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake
Guaranteeing a certain level of user privacy in an arbitrary piece of text is a challenging issue. However, with this challenge comes the potential of unlocking access to vast data…
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations
Oluwaseyi Feyisetan, Borja Balle, Thomas Drake +1
Accurately learning from user data while providing quantifiable privacy guarantees provides an opportunity to build better ML models while maintaining user trust. This paper presen…