Publications related to Stochastic vs. sensitivity-based integral parameter and nuclear data adjustments

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. Th ...

EPFL2023

Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

Gabriel Okasa

Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learners for estimation of heterogeneous treatment effects under the usage of sam ...

2022

True scale-free networks hidden by finite size effects

Andrea Rinaldo, Samir Suweis, Amos Maritan

We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test whether the commonly assumed hypothesis of an underlying scale-free structure is generally viable. This has recently been questioned on the basis of statisti ...

NATL ACAD SCIENCES2021

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

Michael Christoph Gastpar, Adriano Pastore

We consider a setup in which confidential i.i.d. samples X1, . . . , Xn from an unknown finite-support distribution p are passed through n copies of a discrete privatization chan- nel (a.k.a. mechanism) producing outputs Y1, . . . , Yn. The channel law gua ...

2021

Effect of Fear of Falling on Mobility Measured During Lab and Daily Activity Assessments in Parkinson’s Disease

Kamiar Aminian, Arash Atrsaei

In chronic disorders such as Parkinson’s disease (PD), fear of falling (FOF) is associated with falls and reduced quality of life. With inertial measurement units (IMUs) and dedicated algorithms, different aspects of mobility can be obtained during supervi ...

2021

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

or

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

Collaborative Sampling in Generative Adversarial Networks

Alexandre Massoud Alahi, Yuejiang Liu, Parth Ashit Kothari

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

2020

On some graph-based two-sample tests for high dimension, low sample size data

Soham Sarkar

Testing for equality of two high-dimensional distributions is a challenging problem, and this becomes even more challenging when the sample size is small. Over the last few decades, several graph-based two-sample tests have been proposed in the literature, ...

SPRINGER2020

Sampling strategy optimization to increase statistical power in landscape genomics: a simulation-based approach

Stéphane Joost, Oliver Michele Selmoni, Estelle Rochat, Elia Vajana, Annie Sandrine Guillaume

An increasing number of studies are using landscape genomics to investigate local adaptation in wild and domestic populations. The implementation of this approach requires the sampling phase to consider the complexity of environmental settings and the burd ...

2020

Transsphenoidal resection for pituitary adenoma in elderly versus younger patients: a systematic review and meta-analysis

Constantin Tuleasca

Introduction Pituitary adenomas (PA) are common intracranial tumors. In the context of the aging of the population, the question is whether postsurgical outcomes are comparable to the younger ones. The primary objective of the present study was to review p ...

2020

Stochastic vs. sensitivity-based integral parameter and nuclear data adjustments

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Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

True scale-free networks hidden by finite size effects

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

Effect of Fear of Falling on Mobility Measured During Lab and Daily Activity Assessments in Parkinson’s Disease

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Collaborative Sampling in Generative Adversarial Networks

On some graph-based two-sample tests for high dimension, low sample size data

Sampling strategy optimization to increase statistical power in landscape genomics: a simulation-based approach

Transsphenoidal resection for pituitary adenoma in elderly versus younger patients: a systematic review and meta-analysis

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

True scale-free networks hidden by finite size effects

Effect of Fear of Falling on Mobility Measured During Lab and Daily Activity Assessments in Parkinson’s Disease

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Collaborative Sampling in Generative Adversarial Networks

On some graph-based two-sample tests for high dimension, low sample size data

Transsphenoidal resection for pituitary adenoma in elderly versus younger patients: a systematic review and meta-analysis

Sampling strategy optimization to increase statistical power in landscape genomics: a simulation-based approach