Introduction: Gait speed is a simple and safe measure with strong predictive value for negative health outcomes in clinical practice, yet in-laboratory gait speed seems not representative for daily-life gait speed. This study aimed to investigate the inter ...
We study a robust auction design problem with a minimax regret objective, where a seller seeks a mechanism for selling multiple items to multiple anonymous bidders with additive values. The seller knows that the bidders' values range over a box uncertainty ...
We introduce a sequence-dependent coarse-grain model of double-stranded DNA with an explicit description of both the bases and the phosphate groups as interacting rigid-bodies. The model parameters are trained on extensive, state-of-the-art large scale mol ...
Adversarial learning is an emergent technique that provides better security to machine learning systems by deliberately protecting them against specific vulnerabilities of the learning algorithms. Many adversarial learning problems can be cast equivalently ...
Learning to embed data into a space where similar points are together and dissimilar points are far apart is a challenging machine learning problem. In this dissertation we study two learning scenarios that arise in the context of learning embeddings and o ...
The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfil ...
In this thesis, we focus on the problem of achieving practical privacy guarantees in machine learning (ML), where the classic differential privacy (DP) fails to maintain a good trade-off between user privacy and data utility. Differential privacy guarantee ...
Based on weak lensing data from the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS), in this paper we study the mass-concentration (M-c) relation for similar to 200 redMaPPer clusters in the fields. We extract the M-c relation by measuring the den ...
Generalized Linear Models have become a commonly used tool of data analysis. Such models are used to fit regressions for univariate responses with normal, gamma, binomial or Poisson distribution. Maximum likelihood is generally applied as fitting method. I ...
