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You Lu

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9 papers
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9

AAAI Conference 2026 Conference Paper

Denoising Mixup for Regression

  • Zhengzhang Hou
  • Zhanshan Li
  • Yanbo Liu
  • Geoff Nitschke
  • You Lu
  • Ximing Li

Data augmentation is an intuitive solution to increase the diversity of training instances in the machine learning community. Mixup is acknowledged as an effective and efficient mix-based data augmentation method, following a linear alignment assumption that the linear interpolations of features align the corresponding linear interpolations of labels. Unfortunately, this assumption can be violated in many complex scenarios, resulting in augmented instances with noisy labels, especially for regression problems. To solve this problem, we propose an easy-to-implement mixup method, namely DEnosing MIXUP (DE-mixup), which iteratively corrects the noisy response targets by leveraging an auxiliary noise estimation task with mixup deep features. Additionally, we suggest an efficient optimization method with alternating direction method of multipliers. We compare DE-mixup with the existing mixup variants and other prevalent data augmentation methods across benchmark regression datasets. Empirical results indicate the effectiveness of DE-mixup under the in-distribution and out-of-distribution cases.

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AAAI Conference 2026 Conference Paper

ExpertAD: Enhancing Autonomous Driving Systems with Mixture of Experts

  • Haowen Jiang
  • Xinyu Huang
  • You Lu
  • Dingji Wang
  • Yuheng Cao
  • Chaofeng Sha
  • Bihuan Chen
  • Keyu Chen

Recent advancements in end-to-end autonomous driving systems (ADSs) underscore their potential for perception and planning capabilities. However, challenges remain. Complex driving scenarios contain rich semantic information, yet ambiguous or noisy semantics can compromise decision reliability, while interference between multiple driving tasks may hinder optimal planning. Furthermore, prolonged inference latency slows decision-making, increasing the risk of unsafe driving behaviors. To address these challenges, we propose ExpertAD, a novel framework that enhances the performance of ADS with Mixture of Experts (MoE) architecture. We introduce a Perception Adapter (PA) to amplify task-critical features, ensuring contextually relevant scene understanding, and a Mixture of Sparse Experts (MoSE) to minimize task interference during prediction, allowing for effective and efficient planning. Our experiments show that ExpertAD reduces average collision rates by up to 20% and inference latency by 25% compared to prior methods. We further evaluate its multi-skill planning capabilities in rare scenarios (e.g., accidents, yielding to emergency vehicles) and demonstrate strong generalization to unseen urban environments. Additionally, we present a case study that illustrates its decision-making process in complex driving scenarios.

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AAAI Conference 2026 Conference Paper

Semi-Supervised Regression by Preserving Ranking Relationships Between Close Unlabeled Samples

  • Ximing Li
  • Jiaxuan Jiang
  • Changchun Li
  • You Lu
  • Renchu Guan

Semi-Supervised Learning (SSL) aims to improve the learning performance of supervised learning with a large number of unlabeled samples. The existing SSL methods such as FixMatch and FlexMatch select unlabeled samples with high-confident pseudo-labels and make consistency constraints between their weak and strong augmentations. Unfortunately, they cannot be applied Semi-Supervised Regression (SSR) because regression predictions can not reflect the confidence of pseudo-labels. To solve this, a recent SSR method RankUp incorporates an auxiliary ranking task by leveraging sample pairs with high-confident pseudo-ranks. In this paper, we upgrade Rankup to a novel SSR method, namely Semi-Supervised Regression by Ranking Close Unlabeled Samples (SSR-RCUS). Its basic idea is reconstructing closed mixup augmented samples with high-confident pseudo-ranks under a monotonicity assumption, and then applying them to the auxiliary ranking task to improve regression performance. We conduct extensive experiments to evaluate the performance of SSR-RCUS on benchmark datasets, and empirical results demonstrate that SSR-RCUS can outperform the existing baselines in various settings, especially when labeled data are scarce.

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JBHI Journal 2024 Journal Article

LoGo-GR: A Local to Global Graphical Reasoning Framework for Extracting Structured Information From Biomedical Literature

  • Xueyang Zhou
  • Qiming Fu
  • Youbing Xia
  • Yunzhe Wang
  • You Lu
  • Yanming Chen
  • Jianping Chen

In the biomedical literature, entities are often distributed within multiple sentences and exhibit complex interactions. As the volume of literature has increased dramatically, it has become impractical to manually extract and maintain biomedical knowledge, which would entail enormous costs. Fortunately, document-level relation extraction can capture associations between entities from complex text, helping researchers efficiently mine structured knowledge from the vast medical literature. However, how to effectively synthesize rich global information from context and accurately capture local dependencies between entities is still a great challenge. In this paper, we propose a Local to Global Graphical Reasoning framework (LoGo-GR) based on a novel Biased Graph Attention mechanism (B-GAT). It learns global context feature and information of local relation path dependencies from mention-level interaction graph and entity-level path graph respectively, and collaborates with global and local reasoning to capture complex interactions between entities from document-level text. In particular, B-GAT integrates structural dependencies into the standard graph attention mechanism (GAT) as attention biases to adaptively guide information aggregation in graphical reasoning. We evaluate our method on three publicly biomedical document-level datasets: Drug-Mutation Interaction (DV), Chemical-induced Disease (CDR), and Gene-Disease Association (GDA). LoGo-GR has advanced and stable performance compared to other state-of-the-art methods (it achieves state-of-the-art performance with 96. 14%−97. 39% F1 on DV dataset, advanced performance with 68. 89% F1 and 84. 22% F1 on CDR and GDA datasets, respectively). In addition, LoGo-GR also shows advanced performance on general-domain document-level relation extraction dataset, DocRED, which proves that it is an effective and robust document-level relation extraction framework.

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AAAI Conference 2020 Conference Paper

Structured Output Learning with Conditional Generative Flows

  • You Lu
  • Bert Huang

Traditional structured prediction models try to learn the conditional likelihood, i. e. , p(y|x), to capture the relationship between the structured output y and the input features x. For many models, computing the likelihood is intractable. These models are therefore hard to train, requiring the use of surrogate objectives or variational inference to approximate likelihood. In this paper, we propose conditional Glow (c-Glow), a conditional generative flow for structured output learning. C- Glow benefits from the ability of flow-based models to compute p(y|x) exactly and efficiently. Learning with c-Glow does not require a surrogate objective or performing inference during training. Once trained, we can directly and efficiently generate conditional samples. We develop a samplebased prediction method, which can use this advantage to do efficient and effective inference. In our experiments, we test c-Glow on five different tasks. C-Glow outperforms the stateof-the-art baselines in some tasks and predicts comparable outputs in the other tasks. The results show that c-Glow is versatile and is applicable to many different structured prediction problems.

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NeurIPS Conference 2020 Conference Paper

Woodbury Transformations for Deep Generative Flows

  • You Lu
  • Bert Huang

Normalizing flows are deep generative models that allow efficient likelihood calculation and sampling. The core requirement for this advantage is that they are constructed using functions that can be efficiently inverted and for which the determinant of the function's Jacobian can be efficiently computed. Researchers have introduced various such flow operations, but few of these allow rich interactions among variables without incurring significant computational costs. In this paper, we introduce Woodbury transformations, which achieve efficient invertibility via the Woodbury matrix identity and efficient determinant calculation via Sylvester's determinant identity. In contrast with other operations used in state-of-the-art normalizing flows, Woodbury transformations enable (1) high-dimensional interactions, (2) efficient sampling, and (3) efficient likelihood evaluation. Other similar operations, such as 1x1 convolutions, emerging convolutions, or periodic convolutions allow at most two of these three advantages. In our experiments on multiple image datasets, we find that Woodbury transformations allow learning of higher-likelihood models than other flow architectures while still enjoying their efficiency advantages.

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AAAI Conference 2019 Conference Paper

Block Belief Propagation for Parameter Learning in Markov Random Fields

  • You Lu
  • Zhiyuan Liu
  • Bert Huang

Traditional learning methods for training Markov random fields require doing inference over all variables to compute the likelihood gradient. The iteration complexity for those methods therefore scales with the size of the graphical models. In this paper, we propose block belief propagation learning (BBPL), which uses block-coordinate updates of approximate marginals to compute approximate gradients, removing the need to compute inference on the entire graphical model. Thus, the iteration complexity of BBPL does not scale with the size of the graphs. We prove that the method converges to the same solution as that obtained by using full inference per iteration, despite these approximations, and we empirically demonstrate its scalability improvements over standard training methods.

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AAAI Conference 2018 Conference Paper

Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas

  • Kiri Wagstaff
  • You Lu
  • Alice Stanboli
  • Kevin Grimes
  • Thamme Gowda
  • Jordan Padams

NASA has acquired more than 22 million images from the planet Mars. To help users find images of interest, we developed a content-based search capability for Mars rover surface images and Mars orbital images. We started with the AlexNet convolutional neural network, which was trained on Earth images, and used transfer learning to adapt the network for use with Mars images. We report on our deployment of these classifiers within the PDS Imaging Atlas, a publicly accessible web interface, to enable the first content-based image search for NASA’s Mars images.

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AAAI Conference 2018 Conference Paper

Mars Target Encyclopedia: Rock and Soil Composition Extracted From the Literature

  • Kiri Wagstaff
  • Raymond Francis
  • Thamme Gowda
  • You Lu
  • Ellen Riloff
  • Karanjeet Singh
  • Nina Lanza

We have constructed an information extraction system called the Mars Target Encyclopedia that takes in planetary science publications and extracts scientific knowledge about target compositions. The extracted knowledge is stored in a searchable database that can greatly accelerate the ability of scientists to compare new discoveries with what is already known. To date, we have applied this system to ∼6000 documents and achieved 41–56% precision in the extracted information.

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