Author name cluster

Yiling Liu

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5 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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TMLR Journal 2024 Journal Article

Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel

Ziyang Jiang
Tongshu Zheng
Yiling Liu
David Carlson

It is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to model by choosing an appropriate kernel in a Gaussian process (GP). Many deep learning applications could be enhanced by modeling such known properties. For example, convolutional neural networks (CNNs) are frequently used in remote sensing, which is subject to strong seasonal effects. We propose to blend the strengths of NNs and the clear modeling capabilities of GPs by using a composite kernel that combines a kernel implicitly defined by a neural network with a second kernel function chosen to model known properties (e.g., seasonality). We implement this idea by combining a deep network and an efficient mapping function based on either Nystrom approximation or random Fourier features, which we call Implicit Composite Kernel (ICK). We then adopt a sample-then-optimize approach to approximate the full GP posterior distribution. We demonstrate that ICK has superior performance and flexibility on both synthetic and real-world datasets including a remote sensing dataset. The ICK framework can be used to include prior information into neural networks in many applications.

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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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IROS Conference 2019 Conference Paper

Local Pose optimization with an Attention-based Neural Network

Yiling Liu
Hesheng Wang 0001
Fan Xu 0004
Yong Wang
Weidong Chen 0001
Qirong Tang

In this paper, we propose a novel pose optimizer which can be inserted into either supervised or unsupervised end-to-end visual odometry for the purpose of local pose optimization. The pose optimizer is an analogue of the pose graph optimization used in traditional VSLAM algorithms. Local pose optimization is performed by an attention-based neural network which iteratively refines the predicted pose estimates of an image snippet. Instead of complicated graph convolutional network, the attention mechanism based on geometric consistency of trajectory constraint is utilized because pose features whose spatial distribution is not important can be flattened to vectors and then processed. The pose optimizer is aimed at improving pose estimation accuracy by redistributing errors of pose estimates. Quantitative and qualitative evaluation of the proposed approach on the KITTI Odometry dataset [1] is presented to demonstrate its effectiveness in improving pose estimation accuracy and minimizing pose drift.

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ICRA Conference 2019 Conference Paper

Unsupervised Learning of Monocular Depth and Ego-Motion Using Multiple Masks

Guangming Wang 0001
Hesheng Wang 0001
Yiling Liu
Weidong Chen 0001

A new unsupervised learning method of depth and ego-motion using multiple masks from monocular video is proposed in this paper. The depth estimation network and the ego-motion estimation network are trained according to the constraints of depth and ego-motion without truth values. The main contribution of our method is to carefully consider the occlusion of the pixels generated when the adjacent frames are projected to each other, and the blank problem generated in the projection target imaging plane. Two fine masks are designed to solve most of the image pixel mismatch caused by the movement of the camera. In addition, some relatively rare circumstances are considered, and repeated masking is proposed. To some extent, the method is to use a geometric relationship to filter the mismatched pixels for training, making unsupervised learning more efficient and accurate. The experiments on KITTI dataset show our method achieves good performance in terms of depth and ego-motion. The generalization capability of our method is demonstrated by training on the low-quality uncalibrated bike video dataset and evaluating on KITTI dataset, and the results are still good.

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