Zobrazeno 1 - 10
of 334
pro vyhledávání: '"Garivier A."'
Autor:
Garivier, Aurélien, Pilliat, Emmanuel
We provide a simple proof of the Johnson-Lindenstrauss lemma for sub-Gaussian variables. We extend the analysis to identify how sparse projections can be, and what the cost of sparsity is on the target dimension.The Johnson-Lindenstrauss lemma is the
Externí odkaz:
http://arxiv.org/abs/2409.06275
What are the functionals of the reward that can be computed and optimized exactly in Markov Decision Processes?In the finite-horizon, undiscounted setting, Dynamic Programming (DP) can only handle these operations efficiently for certain classes of s
Externí odkaz:
http://arxiv.org/abs/2310.20266
We study non-parametric density estimation for densities in Lipschitz and Sobolev spaces, and under central privacy. In particular, we investigate regimes where the privacy budget is not supposed to be constant. We consider the classical definition o
Externí odkaz:
http://arxiv.org/abs/2306.14535
In the problem of quantum channel certification, we have black box access to a quantum process and would like to decide if this process matches some predefined specification or is $\varepsilon$-far from this specification. The objective is to achieve
Externí odkaz:
http://arxiv.org/abs/2303.01188
Publikováno v:
ICML 2023 - 40th International Conference on Machine Learning, Jul 2023, Honolulu, United States
This work studies the estimation of many statistical quantiles under differential privacy. More precisely, given a distribution and access to i.i.d. samples from it, we study the estimation of the inverse of its cumulative distribution function (the
Externí odkaz:
http://arxiv.org/abs/2302.06943
Publikováno v:
Dependence Modeling, Vol 4, Iss 1 (2016)
In thiswork,we extend some parameters built on a probability distribution introduced before to the casewhere the proximity between real numbers is measured by using a Bregman divergence. This leads to the definition of the Bregman superquantile (that
Externí odkaz:
https://doaj.org/article/4a4ad7da24094aa5b1d6a2d04405754a
Publikováno v:
Transactions on Machine Learning Research Journal, 2024
Motivated by programmatic advertising optimization, we consider the task of sequentially allocating budget across a set of resources. At every time step, a feasible allocation is chosen and only a corresponding random return is observed. The goal is
Externí odkaz:
http://arxiv.org/abs/2210.05222
Publikováno v:
Transactions on Machine Learning Research Journal, 2023
The challenge of producing accurate statistics while respecting the privacy of the individuals in a sample is an important area of research. We study minimax lower bounds for classes of differentially private estimators. In particular, we show how to
Externí odkaz:
http://arxiv.org/abs/2210.02215
Publikováno v:
ALT 2023 - The 34th International Conference on Algorithmic Learning Theory, Feb 2023, Singapour, Singapore
We lay the foundations of a non-parametric theory of best-arm identification in multi-armed bandits with a fixed budget T. We consider general, possibly non-parametric, models D for distributions over the arms; an overarching example is the model D =
Externí odkaz:
http://arxiv.org/abs/2210.00895
What advantage do \emph{sequential} procedures provide over batch algorithms for testing properties of unknown distributions? Focusing on the problem of testing whether two distributions $\mathcal{D}_1$ and $\mathcal{D}_2$ on $\{1,\dots, n\}$ are equ
Externí odkaz:
http://arxiv.org/abs/2205.06069