33 citations · 125 across the 19 of their papers we have counts for
9 papers · 1 filter
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…
HyperStream: a Workflow Engine for Streaming Data
Tom Diethe, Meelis Kull, Niall Twomey +5
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabili…
Distribution Calibration for Regression
Hao Song, Tom Diethe, Meelis Kull +1
We are concerned with obtaining well-calibrated output distributions from regression models. Such distributions allow us to quantify the uncertainty that the model has regarding th…
Automatic Discovery of Privacy-Utility Pareto Fronts
Brendan Avent, Javier Gonzalez, Tom Diethe +2
Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off pr…
Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients
Yu Chen, Tom Diethe, Neil Lawrence
Continual learning aims to enable machine learning models to learn a general solution space for past and future tasks in a sequential manner. Conventional models tend to forget the…
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification
Oluwaseyi Feyisetan, Thomas Drake, Borja Balle +1
Active learning holds promise of significantly reducing data annotation costs while maintaining reasonable model performance. However, it requires sending data to annotators for la…
Continual Learning in Practice
Tom Diethe, Tom Borchert, Eno Thereska +2
This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives. In environments where data evolves, we need architectures th…
-IRT: A New Item Response Model and its Applications
Yu Chen, Telmo Silva Filho, Ricardo B. C. Prudêncio +2
Item Response Theory (IRT) aims to assess latent abilities of respondents based on the correctness of their answers in aptitude test items with different difficulty levels. In this…