Zobrazeno 1 - 10
of 155
pro vyhledávání: '"Wong, Raymond K. W."'
In this paper, we delve into the statistical analysis of the fitted Q-evaluation (FQE) method, which focuses on estimating the value of a target policy using offline data generated by some behavior policy. We provide a comprehensive theoretical under
Externí odkaz:
http://arxiv.org/abs/2406.10438
Covariate balancing methods have been widely applied to single or monotone missing patterns and have certain advantages over likelihood-based methods and inverse probability weighting approaches based on standard logistic regression. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2402.08873
We study distributional off-policy evaluation (OPE), of which the goal is to learn the distribution of the return for a target policy using offline data generated by a different policy. The theoretical foundation of many existing work relies on the s
Externí odkaz:
http://arxiv.org/abs/2402.01900
Discovering causal relationship using multivariate functional data has received a significant amount of attention very recently. In this article, we introduce a functional linear structural equation model for causal structure learning when the underl
Externí odkaz:
http://arxiv.org/abs/2310.20537
Autor:
You, Hojun, Wang, Jiayi, Wong, Raymond K. W., Schumacher, Courtney, Saravanan, R., Jun, Mikyoung
The prediction of tropical rain rates from atmospheric profiles poses significant challenges, mainly due to the heavy-tailed distribution exhibited by tropical rainfall. This study introduces over-parameterized neural networks not only to forecast tr
Externí odkaz:
http://arxiv.org/abs/2309.14358
We study treatment effect estimation with functional treatments where the average potential outcome functional is a function of functions, in contrast to continuous treatment effect estimation where the target is a function of real numbers. By consid
Externí odkaz:
http://arxiv.org/abs/2309.08039
Publikováno v:
Technometrics, 65:4, 524-536 (2023)
Tensor regression methods have been widely used to predict a scalar response from covariates in the form of a multiway array. In many applications, the regions of tensor covariates used for prediction are often spatially connected with unknown shapes
Externí odkaz:
http://arxiv.org/abs/2302.08439
We study the implicit regularization of gradient descent towards structured sparsity via a novel neural reparameterization, which we call a diagonally grouped linear neural network. We show the following intriguing property of our reparameterization:
Externí odkaz:
http://arxiv.org/abs/2301.12540
The prevalence of data collected on the same set of samples from multiple sources (i.e., multi-view data) has prompted significant development of data integration methods based on low-rank matrix factorizations. These methods decompose signal matrice
Externí odkaz:
http://arxiv.org/abs/2206.12891
In the signal processing and statistics literature, the minimum description length (MDL) principle is a popular tool for choosing model complexity. Successful examples include signal denoising and variable selection in linear regression, for which th
Externí odkaz:
http://arxiv.org/abs/2201.11171