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Differentially private permutation tests: Applications to kernel methods
Recent years have witnessed growing concerns about the privacy of sensitive data. In
response to these concerns, differential privacy has emerged as a rigorous framework for …
response to these concerns, differential privacy has emerged as a rigorous framework for …
Monk outlier-robust mean embedding estimation by median-of-means
Mean embeddings provide an extremely flexible and powerful tool in machine learning and
statistics to represent probability distributions and define a semi-metric (MMD, maximum …
statistics to represent probability distributions and define a semi-metric (MMD, maximum …
Robust kernel hypothesis testing under data corruption
We propose two general methods for constructing robust permutation tests under data
corruption. The proposed tests effectively control the non-asymptotic type I error under data …
corruption. The proposed tests effectively control the non-asymptotic type I error under data …
Federated experiment design under distributed differential privacy
Experiment design has a rich history dating back over a century and has found many critical
applications across various fields since then. The use and collection of users' data in …
applications across various fields since then. The use and collection of users' data in …
Multivariate mean comparison under differential privacy
The comparison of multivariate population means is a central task of statistical inference.
While statistical theory provides a variety of analysis tools, they usually do not protect …
While statistical theory provides a variety of analysis tools, they usually do not protect …
Minimax Optimal Two-Sample Testing under Local Differential Privacy
We explore the trade-off between privacy and statistical utility in private two-sample testing
under local differential privacy (LDP) for both multinomial and continuous data. We begin by …
under local differential privacy (LDP) for both multinomial and continuous data. We begin by …
M-estimation and Median of Means applied to statistical learning
T Mathieu - 2021 - theses.hal.science
The main objective of this thesis is to study methods for robust statistical learning.
Traditionally, in statistics we use models or simplifying assumptions that allow us to …
Traditionally, in statistics we use models or simplifying assumptions that allow us to …
Application of kernel hypothesis testing on set-valued data
We present a general framework for kernel hypothesis testing on distributions of sets of
individual examples. Sets may represent many common data sources such as groups of …
individual examples. Sets may represent many common data sources such as groups of …
Kernel Hypothesis Testing with Set-valued Data
We present a general framework for hypothesis testing on distributions of sets of individual
examples. Sets may represent many common data sources such as groups of observations …
examples. Sets may represent many common data sources such as groups of observations …
Hypothesis testing and causal inference with heterogeneous medical data
A Bellot - 2021 - repository.cam.ac.uk
Learning from data which associations hold and are likely to hold in the future is a
fundamental part of scientific discovery. With increasingly heterogeneous data collection …
fundamental part of scientific discovery. With increasingly heterogeneous data collection …