Zobrazeno 1 - 10
of 830
pro vyhledávání: '"P. Alquier"'
Autor:
Alquier, Pierre, Kengne, William
In a groundbreaking work, Schmidt-Hieber (2020) proved the minimax optimality of deep neural networks with ReLu activation for least-square regression estimation over a large class of functions defined by composition. In this paper, we extend these r
Externí odkaz:
http://arxiv.org/abs/2410.21702
This work investigates the offline formulation of the contextual bandit problem, where the goal is to leverage past interactions collected under a behavior policy to evaluate, select, and learn new, potentially better-performing, policies. Motivated
Externí odkaz:
http://arxiv.org/abs/2405.14335
Autor:
Wolfer, Geoffrey, Alquier, Pierre
The convergence rate of a Markov chain to its stationary distribution is typically assessed using the concept of total variation mixing time. However, this worst-case measure often yields pessimistic estimates and is challenging to infer from observa
Externí odkaz:
http://arxiv.org/abs/2402.10506
Autor:
Greco, Giuseppe, Fiorenza, Patrick, Schilirò, Emanuela, Bongiorno, Corrado, Di Franco, Salvatore, Coulon, Pierre-Marie, Frayssinet, Eric, Bartoli, Florian, Giannazzo, Filippo, Alquier, Daniel, Cordier, Yvon, Roccaforte, Fabrizio
In this paper, the Ni Schottky barrier on GaN epilayer grown on free standing substrates has been characterized. First, transmission electrical microscopy (TEM) images and nanoscale electrical analysis by conductive atomic force microscopy (C-AFM) of
Externí odkaz:
http://arxiv.org/abs/2304.11680
Bernstein's condition is a key assumption that guarantees fast rates in machine learning. For example, the Gibbs algorithm with prior $\pi$ has an excess risk in $O(d_{\pi}/n)$, as opposed to the standard $O(\sqrt{d_{\pi}/n})$, where $n$ denotes the
Externí odkaz:
http://arxiv.org/abs/2302.11709
This paper introduces a new principled approach for off-policy learning in contextual bandits. Unlike previous work, our approach does not derive learning principles from intractable or loose bounds. We analyse the problem through the PAC-Bayesian le
Externí odkaz:
http://arxiv.org/abs/2210.13132
We study the deviation inequality for a sum of high-dimensional random matrices and operators with dependence and arbitrary heavy tails. There is an increase in the importance of the problem of estimating high-dimensional matrices, and dependence and
Externí odkaz:
http://arxiv.org/abs/2210.09756
Autor:
Wolfer, Geoffrey, Alquier, Pierre
An important feature of kernel mean embeddings (KME) is that the rate of convergence of the empirical KME to the true distribution KME can be bounded independently of the dimension of the space, properties of the distribution and smoothness features
Externí odkaz:
http://arxiv.org/abs/2210.06672
Autor:
Mai, The Tien, Alquier, Pierre
The aim of reduced rank regression is to connect multiple response variables to multiple predictors. This model is very popular, especially in biostatistics where multiple measurements on individuals can be re-used to predict multiple outputs. Unfort
Externí odkaz:
http://arxiv.org/abs/2206.08619
There has been an increasing interest on summary-free versions of approximate Bayesian computation (ABC), which replace distances among summaries with discrepancies between the empirical distributions of the observed data and the synthetic samples ge
Externí odkaz:
http://arxiv.org/abs/2206.06991