The Utility and Complexity of In- and Out-of-Distribution Machine Unlearning

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Epilepsy is one of the most prevalent brain disorders that disrupts the lives of millions worldwide. For patients with drug-resistant seizures, there exist implantable devices capable of monitoring neural activity, promptly triggering neurostimulation to r ...

The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, ...

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

With ever-increasing computational power, and improved sensing and communication capabilities, smart devices have altered and enhanced the way we process, perceive and interact with information. Personal and contextual data is tracked and stored extensivel ...

This paper investigates robust privacy-sensitive audio features for speaker diarization in multiparty conversations: ie., a set of audio features having low linguistic information for speaker diarization in a single and multiple distant microphone scenario ...

The Utility and Complexity of In- and Out-of-Distribution Machine Unlearning

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Enhancing Epileptic Seizure Detection with EEG Feature Embeddings

Regularization via Mass Transportation

Mirrored Langevin Dynamics

Context and Semantic Aware Location Privacy

Wordless Sounds: Robust Speaker Diarization using Privacy-Preserving Audio Representations