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pro vyhledávání: '"A Chambaz"'
Conformal Inference (CI) is a popular approach for generating finite sample prediction intervals based on the output of any point prediction method when data are exchangeable. Adaptive Conformal Inference (ACI) algorithms extend CI to the case of seq
Externí odkaz:
http://arxiv.org/abs/2312.00448
Autor:
Susmann, Herbert, Chambaz, Antoine
Estimating quantiles of an outcome conditional on covariates is of fundamental interest in statistics with broad application in probabilistic prediction and forecasting. We propose an ensemble method for conditional quantile estimation, Quantile Supe
Externí odkaz:
http://arxiv.org/abs/2310.19343
Policy learning utilizing observational data is pivotal across various domains, with the objective of learning the optimal treatment assignment policy while adhering to specific constraints such as fairness, budget, and simplicity. This study introdu
Externí odkaz:
http://arxiv.org/abs/2310.06969
Autor:
Reisach, Alexander G., Tami, Myriam, Seiler, Christof, Chambaz, Antoine, Weichwald, Sebastian
Additive Noise Models (ANMs) are a common model class for causal discovery from observational data and are often used to generate synthetic data for causal discovery benchmarking. Specifying an ANM requires choosing all parameters, including those no
Externí odkaz:
http://arxiv.org/abs/2303.18211
Autor:
Susmann, Herbert, Chambaz, Antoine
Two of the principle tasks of causal inference are to define and estimate the effect of a treatment on an outcome of interest. Formally, such treatment effects are defined as a possibly functional summary of the data generating distribution, and are
Externí odkaz:
http://arxiv.org/abs/2301.10630
Autor:
Ecoto, Geoffrey, Chambaz, Antoine
Drought events are the second most expensive type of natural disaster within the French legal framework known as the natural disasters compensation scheme. In recent years, drought events have been remarkable in their geographical scale and intensity
Externí odkaz:
http://arxiv.org/abs/2206.11545
Autor:
Susmann, Herbert, Chambaz, Antoine, Josse, Julie, Aegerter, Philippe, Wargon, Mathias, Bacry, Emmanuel
Publikováno v:
In International Journal of Medical Informatics March 2025 195
Autor:
Malenica, Ivana, Phillips, Rachael V., Pirracchio, Romain, Chambaz, Antoine, Hubbard, Alan, van der Laan, Mark J.
In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degrees of personalization. Namely, POSL optimizes predictions with respect
Externí odkaz:
http://arxiv.org/abs/2109.10452
Suppose that we observe a short time series where each time-t-specific data-structure consists of many slightly dependent data indexed by a and that we want to estimate a feature of the law of the experiment that depends neither on t nor on a. We dev
Externí odkaz:
http://arxiv.org/abs/2107.13291
Autor:
Nguyen, Thi Thanh Yen, Harchaoui, Warith, Mégret, Lucile, Mendoza, Cloe, Bouaziz, Olivier, Neri, Christian, Chambaz, Antoine
We present several algorithms designed to learn a pattern of correspondence between two data sets in situations where it is desirable to match elements that exhibit a relationship belonging to a known parametric model. In the motivating case study, t
Externí odkaz:
http://arxiv.org/abs/2107.11192