Publications related to Locally Differentially-Private Randomized Response for Discrete Distribution Learning

DATED: GUIDELINES FOR CREATING SYNTHETIC DATASETS FOR ENGINEERING DESIGN APPLICATIONS

Jürg Alexander Schiffmann, Cyril Picard, Faez Ahmed

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significan ...

Amer Soc Mechanical Engineers2023

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

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Michael Christoph Gastpar, Adrien Vandenbroucque, Amedeo Roberto Esposito

We consider the problem of parameter estimation in a Bayesian setting and propose a general lower-bound that includes part of the family of f-Divergences. The results are then applied to specific settings of interest and compared to other notable results i ...

2022

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Nicolas Henri Bernard Flammarion, Xiang Cheng

We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as optimization on the space of probability measures, with Kullback-Leibler (KL) divergence as the objective functional. We show that an under-damped form of the Langevin algorithm perfor ...

INT STATISTICAL INST2021

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables. ...

2021

Uncertain Sampling with Certain Priors

Golnooshsadat Elhami

Sampling has always been at the heart of signal processing providing a bridge between the analogue world and discrete representations of it, as our ability to process data in continuous space is quite limited. Furthermore, sampling plays a key part in unde ...

EPFL2021

Some first results on the consistency of spatial regression with partial differential equation regularization

Fabio Nobile, Eleonora Arnone

We study the consistency of the estimator in spatial regression with partial differential equa-tion (PDE) regularization. This new smoothing technique allows to accurately estimate spatial fields over complex two-dimensional domains, starting from noisy ob ...

2020

Robust Generalization via f−Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

Given two probability measures P and Q and an event E, we provide bounds on P(E) in terms of Q(E) and f-divergences. In particular, the bounds are instantiated when the measures considered are a joint distribution and the corresponding product of marginals ...

IEEE2020

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

From Data to Decisions: Distributionally Robust Optimization is Optimal

Daniel Kuhn, Peyman Mohajerin Esfahani

We study stochastic programs where the decision-maker cannot observe the distribution of the exogenous uncertainties but has access to a finite set of independent samples from this distribution. In this setting, the goal is to find a procedure that transfo ...

2020

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

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DATED: GUIDELINES FOR CREATING SYNTHETIC DATASETS FOR ENGINEERING DESIGN APPLICATIONS

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

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Uncertain Sampling with Certain Priors

Some first results on the consistency of spatial regression with partial differential equation regularization

Robust Generalization via f−Mutual Information

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

From Data to Decisions: Distributionally Robust Optimization is Optimal

DATED: GUIDELINES FOR CREATING SYNTHETIC DATASETS FOR ENGINEERING DESIGN APPLICATIONS

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

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Uncertain Sampling with Certain Priors

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Robust Generalization via f−Mutual Information

Some first results on the consistency of spatial regression with partial differential equation regularization

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

From Data to Decisions: Distributionally Robust Optimization is Optimal