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Alexandra Brintrup

6 accepted papers

2026

Redirection for Erasing Memory (REM): Towards a universal unlearning method for corrupted data

Stefan Schoepf, Michael Curtis Mozer, Nicole Elyse Mitchell, Alexandra Brintrup, Georgios Kaissis, Peter Kairouz +1
ICLR 2026poster

Machine unlearning is studied for a multitude of tasks, but specialization of unlearning methods to particular tasks has made their systematic comparison challenging. To address this issue, we propose a conceptual space to characterize diverse corrupted data unlearning tasks in vision classifiers. T…

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2026

Topological Federated Clustering via Gravitational Potential Fields Under Local Differential Privacy

Yunbo Long, Jiaquan Zhang, Xi Chen, Alexandra Brintrup
AAAI 2026technical

Clustering non-independent and identically distributed (non-IID) data under local differential privacy (LDP) in federated settings presents a critical challenge: preserving privacy while maintaining accuracy without iterative communication. Existing one-shot methods rely on unstable pairwise centroi

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2024

Fast Machine Unlearning without Retraining through Selective Synaptic Dampening

Jack Foster, Stefan Schoepf, Alexandra Brintrup
AAAI 2024technical

Machine unlearning, the ability for a machine learning model to forget, is becoming increasingly important to comply with data privacy regulations, as well as to remove harmful, manipulated, or outdated information. The key challenge lies in forgetting specific information while protecting model per…

Cited by 95Source ↗PDF ↗code ↗Scholar ↗
2020

Uncertainty in Neural Networks: Approximately Bayesian Ensembling

Tim Pearce, Felix Leibfried, Alexandra Brintrup
AISTATS 2020poster

Understanding the uncertainty of a neural network’s (NN) predictions is essential for many purposes. The Bayesian framework provides a principled approach to this, however applying it to NNs is challenging due to large numbers of parameters and data. Ensembling NNs provides an easily implementable,…

Cited by 281Source ↗PDF ↗code ↗Scholar ↗
2019

Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions

Tim Pearce, Russell Tsuchida, Mohamed Zaki, Alexandra Brintrup, Andy Neely
UAI 2019poster

A simple, flexible approach to creating expressive priors in Gaussian process (GP) models makes new kernels from a combination of basic kernels, e.g. summing a periodic and linear kernel can capture seasonal variation with a long term trend. Despite a well-studied link between GPs and Bayesian neura…

Cited by 67Source ↗PDF ↗Scholar ↗
2018

High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach

Tim Pearce, Alexandra Brintrup, Mohamed Zaki, Andy Neely
ICML 2018oral

This paper considers the generation of prediction intervals (PIs) by neural networks for quantifying uncertainty in regression tasks. It is axiomatic that high-quality PIs should be as narrow as possible, whilst capturing a specified portion of data. We derive a loss function directly from this axio…

Cited by 374Source ↗PDF ↗Scholar ↗