You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

2 code implementations • 21 Aug 2022 • Xuelin Yang, Louis Abraham, Sejin Kim, Petr Smirnov, Feng Ruan, Benjamin Haibe-Kains, Robert Tibshirani

The Cox proportional hazards model is a canonical method in survival analysis for prediction of the life expectancy of a patient given clinical or genetic covariates -- it is a linear model in its original form.

no code implementations • 12 Oct 2021 • Michael I. Jordan, Keli Liu, Feng Ruan

We describe an implicit sparsity-inducing mechanism based on minimization over a family of kernels: \begin{equation*} \min_{\beta, f}~\widehat{\mathbb{E}}[L(Y, f(\beta^{1/q} \odot X)] + \lambda_n \|f\|_{\mathcal{H}_q}^2~~\text{subject to}~~\beta \ge 0, \end{equation*} where $L$ is the loss, $\odot$ is coordinate-wise multiplication and $\mathcal{H}_q$ is the reproducing kernel Hilbert space based on the kernel $k_q(x, x') = h(\|x-x'\|_q^q)$, where $\|\cdot\|_q$ is the $\ell_q$ norm.

no code implementations • 17 Jun 2021 • Feng Ruan, Keli Liu, Michael I. Jordan

Kernel-based feature selection is an important tool in nonparametric statistics.

no code implementations • 14 Dec 2020 • Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan

We study two-sided matching markets in which one side of the market (the players) does not have a priori knowledge about its preferences for the other side (the arms) and is required to learn its preferences from experience.

no code implementations • 24 Nov 2020 • Keli Liu, Feng Ruan

The trick is to maximize a class of parametrized nonparametric dependence measures which we call metric learning objectives; the landscape of these nonconvex objective functions is sensitive to interactions but the objectives themselves do not explicitly model interactions.

no code implementations • 16 Mar 2020 • Koulik Khamaru, Ashwin Pananjady, Feng Ruan, Martin J. Wainwright, Michael. I. Jordan

We address the problem of policy evaluation in discounted Markov decision processes, and provide instance-dependent guarantees on the $\ell_\infty$-error under a generative model.

no code implementations • 5 Nov 2019 • Andrea Montanari, Feng Ruan, Youngtak Sohn, Jun Yan

In this case, we observe that the test error is monotone decreasing in the number of parameters.

2 code implementations • 29 Jul 2019 • Ismael Lemhadri, Feng Ruan, Louis Abraham, Robert Tibshirani

Unlike other approaches to feature selection for neural nets, our method uses a modified objective function with constraints, and so integrates feature selection with the parameter learning directly.

no code implementations • 16 Dec 2016 • John Duchi, Feng Ruan

We study local complexity measures for stochastic convex optimization problems, providing a local minimax theory analogous to that of H\'{a}jek and Le Cam for classical statistical problems.

1 code implementation • 30 Jun 2014 • Stanley Osher, Feng Ruan, Jiechao Xiong, Yuan YAO, Wotao Yin

In this paper, we recover sparse signals from their noisy linear measurements by solving nonlinear differential inclusions, which is based on the notion of inverse scale space (ISS) developed in applied mathematics.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.