A Statistical Test for Probabilistic Fairness

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Near Collision Attack Against Grain V1

Subhadeep Banik, Willi Meier, Daniel Patrick Collins

A near collision attack against the Grain v1 stream cipher was proposed by Zhang et al. in Eurocrypt 18. The attack uses the fact that two internal states of the stream cipher with very low hamming distance between them, produce similar keystream sequences ...

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Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

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In 1948, Claude Shannon laid the foundations of information theory, which grew out of a study to find the ultimate limits of source compression, and of reliable communication. Since then, information theory has proved itself not only as a quest to find the ...

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Data-driven approaches for non-invasive cuffless blood pressure monitoring

Clémentine Léa Aguet

Blood pressure (BP) is a crucial indicator of cardiovascular health. Hypertension is a common life-threatening condition and a key factor of cardiovascular diseases (CVDs). Identifying abnormal BP fluctuations can allow for early detection and management o ...

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When do Minimax-fair Learning and Empirical Risk Minimization Coincide?

Volkan Cevher

Minimax-fair machine learning minimizes the error for the worst-off group. However, empirical evidence suggests that when sophisticated models are trained with standard empirical risk minimization (ERM), they often have the same performance on the worst-of ...

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Wind, Hail, and Climate Extremes: Modelling and Attribution Studies for Environmental Data

Ophélia Mireille Anna Miralles

This thesis presents work at the junction of statistics and climate science. We first provide methodology for use by climate scientists when performing fast event attribution using extreme value theory, and then describe two interdisciplinary projects in c ...

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Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

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A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the ...

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Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

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The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and timeconsuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for foreca ...

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Out of their minds? Externalist challenges for using AI in forensic psychiatry

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Harnessing the power of machine learning (ML) and other Artificial Intelligence (AI) techniques promises substantial improvements across forensic psychiatry, supposedly offering more objective evaluations and predictions. However, AI-based predictions abou ...

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Bayes-optimal Learning of Deep Random Networks of Extensive-width

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We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

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Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

Trung-Dung Hoang, Thanh Trung Huynh

ELSEVIER2023