Author name cluster

Keyu Li

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6 papers

2 author rows

TMLR Journal 2025 Journal Article

Understanding and Robustifying Sub-domain Alignment for Domain Adaptation

Yiling Liu
Juncheng Dong
Ziyang Jiang
Ahmed Aloui
Keyu Li
Michael Hunter Klein
Vahid Tarokh
David Carlson

In unsupervised domain adaptation (UDA), aligning source and target domains improves the predictive performance of learned models on the target domain. A common methodological improvement in alignment methods is to divide the domains and align sub-domains instead. These sub-domain-based algorithms have demonstrated great empirical success but lack theoretical support. In this work, we establish a rigorous theoretical understanding of the advantages of these methods that have the potential to enhance their overall impact on the field. Our theory uncovers that sub-domain-based methods optimize an error bound that is at least as strong as non-sub-domain-based error bounds and is empirically verified to be much stronger. Furthermore, our analysis indicates that when the marginal weights of sub-domains shift between source and target tasks, the performance of these methods may be compromised. We therefore implement an algorithm to robustify sub-domain alignment for domain adaptation under sub-domain shift, offering a valuable adaptation strategy for future sub-domain-based methods. Empirical experiments across various benchmarks validate our theoretical insights, prove the necessity for the proposed adaptation strategy, and demonstrate the algorithm's competitiveness in handling label shift.

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ICML Conference 2023 Conference Paper

Estimating Causal Effects using a Multi-task Deep Ensemble

Ziyang Jiang
Zhuoran Hou
Yiling Liu
Yiman Ren
Keyu Li
David E. Carlson

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks.

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ICRA Conference 2021 Conference Paper

Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning

Keyu Li
Jian Wang 0099
Yangxin Xu
Hao Qin
Dongsheng Liu
Li Liu 0017
Max Q. -H. Meng

Autonomous ultrasound (US) acquisition is an important yet challenging task, as it involves interpretation of the highly complex and variable images and their spatial relationships. In this work, we propose a deep reinforcement learning framework to autonomously control the 6-D pose of a virtual US probe based on real-time image feedback to navigate towards the standard scan planes under the restrictions in real-world US scans. Furthermore, we propose a confidence-based approach to encode the optimization of image quality in the learning process. We validate our method in a simulation environment built with real-world data collected in the US imaging of the spine. Experimental results demonstrate that our method can perform reproducible US probe navigation towards the standard scan plane with an accuracy of 4. 91mm/4. 65° in the intra-patient setting, and accomplish the task in the intra- and inter-patient settings with a success rate of 92% and 46%, respectively. The results also show that the introduction of image quality optimization in our method can effectively improve the navigation performance.

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ICRA Conference 2021 Conference Paper

Reciprocally Rotating Magnetic Actuation and Automatic Trajectory Following for Wireless Capsule Endoscopy

Yangxin Xu
Keyu Li
Ziqi Zhao
Fei Meng
Li Liu 0017
Max Q. -H. Meng

Active wireless capsule endoscopy (WCE) under magnetic actuation is a promising technology to reduce the inspection time and relieve the burden of physicians. In this paper, we propose a reciprocally rotating magnetic actuation method for trajectory following of a capsule and develop its dynamic model. For the trajectory following task, we investigate the closed-loop tracking control strategies based on different controllers to actuate the capsule in the complex environments. The effectiveness of our method is validated in extensive experiments in a simulation environment as well as in an ex-vivo pig colon. The results demonstrate that the proposed method can accurately and efficiently actuate the capsule to follow the desired trajectory in the complex environments, achieving tracking errors on the order of millimeter. Moreover, the experiments on the ex-vivo pig colon show that the proposed reciprocally rotating magnetic actuation method has the potential to reduce the clinical risks and improve the safety and clinical acceptability of this technology.

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AAAI Conference 2021 Conference Paper

Training Binary Neural Network without Batch Normalization for Image Super-Resolution

Xinrui Jiang
Nannan Wang
Jingwei Xin
Keyu Li
Xi Yang
Xinbo Gao

Recently, binary neural network (BNN) based superresolution (SR) methods have enjoyed initial success in the SR field. However, there is a noticeable performance gap between the binarized model and the full-precision one. Furthermore, the batch normalization (BN) in binary SR networks introduces floating-point calculations, which is unfriendly to low-precision hardwares. Therefore, there is still room for improvement in terms of model performance and efficiency. Focusing on this issue, in this paper, we first explore a novel binary training mechanism based on the feature distribution, allowing us to replace all BN layers with a simple training method. Then, we construct a strong baseline by combining the highlights of recent binarization methods, which already surpasses the state-of-the-arts. Next, to train highly accurate binarized SR model, we also develop a lightweight network architecture and a multi-stage knowledge distillation strategy to enhance the model representation ability. Extensive experiments demonstrate that the proposed method not only presents advantages of lower computation as compared to conventional floating-point networks but outperforms the state-of-the-art binary methods on the standard SR networks.

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ICRA Conference 2020 Conference Paper

Improved Multiple Objects Tracking based Autonomous Simultaneous Magnetic Actuation & Localization for WCE

Yangxin Xu
Keyu Li
Ziqi Zhao
Max Q. -H. Meng

Wireless Capsule Endoscopy (WCE) has the advantage of reducing the invasiveness and pain of gastrointestinal examinations. In this work, we propose a system aimed at autonomously accelerating and locating the WCE inside the intestine for clinical applications. A rotating magnet controlled by a robotic arm is placed outside the patient's body to actuate the capsule with an internal magnetic ring, and the magnetic fields of the two sources are measured by an external sensor array. The original Multiple Objects Tracking method is improved by combining Normal Vector Fitting, Bézier Curve Gradient, and Spherical Linear Interpolation to estimate the 6-D pose of the WCE from a 5-D pose sequence. In order to close the actuation-localization loop, a strategy is presented to react to different states of the capsule. The proposed method is validated via experiments on phantoms as well as on animal intestines. The localization of the capsule shows an accuracy of 3. 5mm in position and 9. 4° in orientation, and the average update frequency of the estimated 6-D pose reaches 25Hz.

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