no code implementations • ICML 2020 • Hengrui Cai, Wenbin Lu, Rui Song
Estimation of optimal decision rules (ODR) has been extensively investigated recently, however, at present, no testing procedure is proposed to verify whether these ODRs are significantly better than the naive decision rule that always assigning individuals to a fixed treatment option.
no code implementations • 25 Dec 2022 • Runzhe Wan, YingYing Li, Wenbin Lu, Rui Song
Latent factor model estimation typically relies on either using domain knowledge to manually pick several observed covariates as factor proxies, or purely conducting multivariate analysis such as principal component analysis.
no code implementations • 4 Mar 2022 • Kevin Gunn, Wenbin Lu, Rui Song
Simulation studies are conducted to assess the empirical performance of the proposed method and to compare with a fully supervised method using only the labeled data.
no code implementations • 25 Feb 2022 • Haoyu Chen, Wenbin Lu, Rui Song, Pulak Ghosh
Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly.
no code implementations • 17 Jan 2022 • Jianing Chu, Wenbin Lu, Shu Yang
We consider the problem of treatment regime estimation when the source and target populations may be heterogeneous, individual-level data is available from the source population, and only the summary information of covariates, such as moments, is accessible from the target population.
no code implementations • 17 Nov 2021 • Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu
To derive an optimal I2DR, our jump interval-learning method estimates the conditional mean of the outcome given the treatment and the covariates via jump penalized regression, and derives the corresponding optimal I2DR based on the estimated outcome regression function.
no code implementations • 6 Nov 2021 • Ye Liu, Rui Song, Wenbin Lu, Yanghua Xiao
A large number of models and algorithms have been proposed to perform link prediction, among which tensor factorization method has proven to achieve state-of-the-art performance in terms of computation efficiency and prediction accuracy.
1 code implementation • 11 Oct 2021 • Hengrui Cai, Wenbin Lu, Rachel Marceau West, Devan V. Mehrotra, Lingkang Huang
In this paper, we present an optimal subgroup selection rule (SSR) that maximizes the number of selected patients, and in the meantime, achieves the pre-specified clinically meaningful mean outcome, such as the average treatment effect.
1 code implementation • 21 Apr 2021 • Hengrui Cai, Wenbin Lu, Rui Song
We consider the optimal decision-making problem in a primary sample of interest with multiple auxiliary sources available.
no code implementations • 21 Apr 2021 • Hengrui Cai, Rui Song, Wenbin Lu
We propose an auGmented inverse propensity weighted Experimental and Auxiliary sample-based decision Rule (GEAR) by maximizing the augmented inverse propensity weighted value estimator over a class of decision rules using the experimental sample, with the primary outcome being imputed based on the auxiliary sample.
no code implementations • ICLR 2021 • Hengrui Cai, Rui Song, Wenbin Lu
Under a general causal graph, the exposure may have a direct effect on the outcome and also an indirect effect regulated by a set of mediators.
no code implementations • 1 Jan 2021 • Sheng Zhang, Rui Song, Wenbin Lu
In a number of experiments on benchmark datasets, we show that the proposed GraphCGAN outperforms the baseline methods by a significant margin.
no code implementations • 1 Jan 2021 • Haoyu Chen, Wenbin Lu, Rui Song, Pulak Ghosh
Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly.
1 code implementation • NeurIPS 2021 • Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu
To handle continuous treatments, we develop a novel estimation method for OPE using deep jump learning.
1 code implementation • 14 Oct 2020 • Haoyu Chen, Wenbin Lu, Rui Song
Focusing on the statistical inference of online decision making, we establish the asymptotic normality of the parameter estimator produced by our algorithm and the online inverse probability weighted value estimator we used to estimate the optimal value.
no code implementations • 14 Oct 2020 • Haoyu Chen, Wenbin Lu, Rui Song
Based on the properties of the parameter estimators, we further show that the in-sample inverse propensity weighted value estimator is asymptotically normal.
no code implementations • 28 Sep 2020 • Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu
To handle continuous action space, we develop a brand-new deep jump Q-evaluation method for OPE.
1 code implementation • 19 Jul 2020 • Liangyu Zhu, Wenbin Lu, Michael R. Kosorok, Rui Song
In this article, we propose a kernel assisted learning method for estimating the optimal individualized dose rule.
1 code implementation • ICML 2020 • Chengchun Shi, Runzhe Wan, Rui Song, Wenbin Lu, Ling Leng
The Markov assumption (MA) is fundamental to the empirical validity of reinforcement learning.
no code implementations • 28 Feb 2019 • Wenbin Lu, Anthony L. Piro
Although there has recently been tremendous progress in studies of fast radio bursts (FRBs), the nature of their progenitors remains a mystery.
High Energy Astrophysical Phenomena
2 code implementations • 16 Jun 2018 • X. Jessie Jeng, Huimin Peng, Wenbin Lu
In this paper, we consider variable selection from a new perspective motivated by the frequently occurred phenomenon that relevant variables are not completely distinguishable from noise variables on the solution path.
Methodology
no code implementations • 15 Oct 2015 • Chengchun Shi, Rui Song, Wenbin Lu
In this paper, we propose a two-step estimation procedure for deriving the optimal treatment regime under NP dimensionality.
no code implementations • 20 May 2014 • Ailin Fan, Wenbin Lu, Rui Song
Gunter et al. (2011) proposed S-score which characterizes the magnitude of qualitative interaction of each variable with treatment individually.