no code implementations • 23 Apr 2024 • Shibo Li, Hengliang Cheng, Runze Li, Weihua Li
The widespread application of Electronic Health Records (EHR) data in the medical field has led to early successes in disease risk prediction using deep learning methods.
no code implementations • 1 Apr 2024 • Zelin He, Ying Sun, Jingyuan Liu, Runze Li
Nonasymptotic bound is provided for the estimation error of the target model, showing the robustness of the proposed method to covariate shifts.
no code implementations • 20 Mar 2024 • Zelin He, Ying Sun, Jingyuan Liu, Runze Li
We consider the transfer learning problem in the high dimensional setting, where the feature dimension is larger than the sample size.
no code implementations • 26 Apr 2023 • Shijie Cui, Agus Sudjianto, Aijun Zhang, Runze Li
Gradient-boosted decision trees (GBDT) are widely used and highly effective machine learning approach for tabular data modeling.
no code implementations • 14 Apr 2023 • Degui Li, Runze Li, Han Lin Shang
In this paper, we consider detecting and estimating breaks in heterogeneous mean functions of high-dimensional functional time series which are allowed to be cross-sectionally correlated and temporally dependent.
no code implementations • 12 Apr 2023 • Runze Li, Dahun Kim, Bir Bhanu, Weicheng Kuo
We present RECLIP (Resource-efficient CLIP), a simple method that minimizes computational resource footprint for CLIP (Contrastive Language Image Pretraining).
no code implementations • 23 Mar 2023 • Xiaorong Yang, Jia Chen, Degui Li, Runze Li
A latent group structure is imposed on the heterogenous quantile regression models so that the number of nonparametric functional coefficients to be estimated can be reduced considerably.
no code implementations • 18 Jul 2022 • Runze Li, Pan Ji, Yi Xu, Bir Bhanu
As compared to outdoor environments, estimating depth of monocular videos for indoor environments, using self-supervised methods, results in two additional challenges: (i) the depth range of indoor video sequences varies a lot across different frames, making it difficult for the depth network to induce consistent depth cues for training; (ii) the indoor sequences recorded with handheld devices often contain much more rotational motions, which cause difficulties for the pose network to predict accurate relative camera poses.
no code implementations • 17 Aug 2021 • Runze Li, Tomaso Fontanini, Luca Donati, Andrea Prati, Bir Bhanu
Gradient-based attention modeling has been used widely as a way to visualize and understand convolutional neural networks.
no code implementations • 27 Jul 2021 • Runze Li, Srikrishna Karanam, Ren Li, Terrence Chen, Bir Bhanu, Ziyan Wu
We conduct a variety of experiments on standard video mesh recovery benchmark datasets such as Human3. 6M, MPI-INF-3DHP, and 3DPW, demonstrating the efficacy of our design of modeling local dynamics as well as establishing state-of-the-art results based on standard evaluation metrics.
Ranked #44 on 3D Human Pose Estimation on 3DPW
no code implementations • ICCV 2021 • Pan Ji, Runze Li, Bir Bhanu, Yi Xu
The effectiveness of each module is shown through a carefully conducted ablation study and the demonstration of the state-of-the-art performance on three indoor datasets, \ie, EuRoC, NYUv2, and 7-scenes.
no code implementations • 28 Dec 2020 • Han Zhong, Xun Deng, Ethan X. Fang, Zhuoran Yang, Zhaoran Wang, Runze Li
In particular, we focus on a variance-constrained policy optimization problem where the goal is to find a policy that maximizes the expected value of the long-run average reward, subject to a constraint that the long-run variance of the average reward is upper bounded by a threshold.
no code implementations • 5 Oct 2020 • Runze Li, Yufei Zhang, Haixin Chen
The policy is then trained in environments based on surrogate models, of which the mean drag reduction of 200 airfoils can be effectively improved by reinforcement learning.
Computational Engineering, Finance, and Science Data Analysis, Statistics and Probability
no code implementations • 4 Sep 2020 • Yining Wang, Yi Chen, Ethan X. Fang, Zhaoran Wang, Runze Li
We consider the stochastic contextual bandit problem under the high dimensional linear model.
2 code implementations • CVPR 2020 • Wenqian Liu, Runze Li, Meng Zheng, Srikrishna Karanam, Ziyan Wu, Bir Bhanu, Richard J. Radke, Octavia Camps
We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions.
no code implementations • 19 Aug 2019 • Wanjun Liu, Yuan Ke, Jingyuan Liu, Runze Li
It can be shown that the proposed two-step approach enjoys both sure screening and FDR control if the pre-specified FDR level $\alpha$ is greater or equal to $1/s$, where $s$ is the number of active features.
no code implementations • 5 May 2015 • Chen Xu, Yongquan Zhang, Runze Li
Under mild conditions, we show that, with a proper number of segments, DKR leads to an estimator that is generalization consistent to the unknown regression function.