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no code implementations • 25 Jan 2023 • Hanyang Liu, Shuai Yang, Feng Qi, Shuaiwen Wang

We also introduce a novel differentiable indexing method for the proposed adaptive curve transformation, which allows gradients with respect to the discrete indices to flow freely through the curve transformation layer, enabling the learned window sizes to be updated flexibly during training.

no code implementations • 20 Sep 2019 • Shuaiwen Wang, Haolei Weng, Arian Maleki

A recently proposed SLOPE estimator (arXiv:1407. 3824) has been shown to adaptively achieve the minimax $\ell_2$ estimation rate under high-dimensional sparse linear regression models (arXiv:1503. 08393).

1 code implementation • 4 Oct 2018 • Shuaiwen Wang, Wenda Zhou, Arian Maleki, Haihao Lu, Vahab Mirrokni

$\mathcal{C} \subset \mathbb{R}^{p}$ is a closed convex set.

2 code implementations • ICML 2018 • Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni

Consider the following class of learning schemes: $$\hat{\boldsymbol{\beta}} := \arg\min_{\boldsymbol{\beta}}\;\sum_{j=1}^n \ell(\boldsymbol{x}_j^\top\boldsymbol{\beta}; y_j) + \lambda R(\boldsymbol{\beta}),\qquad\qquad (1) $$ where $\boldsymbol{x}_i \in \mathbb{R}^p$ and $y_i \in \mathbb{R}$ denote the $i^{\text{th}}$ feature and response variable respectively.

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