Author name cluster

You Zhou

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8 papers

1 author row

AAAI Conference 2026 Conference Paper

Efficient Few-Step Solution Generation via Discrete Flow Matching for Combinatorial Optimization

Yuanshu Li
Di Wang
Wei Du
Xuan Wu
Peng Zhao
Yubin Xiao
You Zhou

Combinatorial optimization problems (COPs) are fundamental to many real-world applications where efficiently producing high-quality solutions is critical. Recent advances in diffusion-based non-autoregressive models have reformulated solving COPs as a generative process, achieving promising results. However, almost all of these methods still suffer from accumulated errors and high inference costs due to the multi-step stochastic denoising process. To address these issues, we propose EFLOCO, an efficient discrete flow matching method for solving COPs, learning structured and deterministic solution trajectories. EFLOCO replaces noise-driven updates with smooth and guided transitions, thereby improves inference stability and quality. Furthermore, we introduce an adaptive time-step scheduler that makes more efforts in critical transition regions, yielding strong performance under few-step constraints. Experiments on standard Traveling Salesman Problems (TSPs) and Asymmetric TSPs (ATSPs) show that our method consistently outperforms both learning-based and heuristic baselines in terms of solution quality and inference speed.

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AAAI Conference 2026 Conference Paper

Learning Whom to Align With: Progressive Anomaly Combination Detection for Partially View-Aligned Clustering

Hang Gao
Zuosong Cai
Yuze Li
Cheng Liu
Gaoyang Li
Ying Li
Wei Du
You Zhou

Partially View-aligned Clustering (PVC) addresses the challenge of partial view alignment in multi-view learning by leveraging complementary and consistent information. While existing PVC methods show promise, most rely on distance-based strategies that are sensitive to view-specific details and noise, limiting their robustness. In this work, we propose a novel view alignment strategy that reformulates the alignment task as an anomaly detection problem. Rather than learning a view-alignment matrix that enforces strict one-to-one correspondences across views, we adopt a progressive approach to identify well-aligned samples. Specifically, we sample subsets of data by generating random view combinations from unaligned samples and propose an anomaly combination detection module to evaluate the alignment consistency of these combinations. In addition, our progressive training framework alternates between updating model parameters and selecting high-confidence view combinations for subsequent optimization. By reformulating view alignment as an anomaly detection task, our approach provides a more robust and effective solution to partial view alignment. Experiments on benchmark datasets demonstrate that our method outperforms state-of-the-art approaches in the PVC problem.

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AAAI Conference 2026 Conference Paper

Spike Imaging Velocimetry: Dense Motion Estimation of Fluids Using Spike Streams

Yunzhong Zhang
You Zhou
Changqing Su
Zhen Cheng
Zhaofei Yu
Bo Xiong
Tiejun Huang
Xun Cao

Particle Image Velocimetry (PIV) is a widely adopted non-invasive imaging technique that tracks the motion of tracer particles across image sequences to capture the velocity distribution of fluid flows. It is commonly employed to analyze complex flow structures and validate numerical simulations. This study explores the untapped potential of spike cameras—ultra-high-speed, high-dynamic-range vision sensors—in high-speed fluid velocimetry. We propose a deep learning framework, Spike Imaging Velocimetry (SIV), tailored for high-resolution fluid motion estimation. To enhance the network’s performance, we design three novel modules specifically adapted to the characteristics of fluid dynamics and spike streams: the Detail-Preserving Hierarchical Transform (DPHT), the Graph Encoder (GE), and the Multi-scale Velocity Refinement (MSVR). Furthermore, we introduce a spike-based PIV dataset, Particle Scenes with Spike and Displacement (PSSD), which contains labeled samples from three representative fluid-dynamics scenarios: steady turbulence, high-speed flow, and high-dynamic-range conditions. Our proposed method outperforms existing baselines across all these scenarios, demonstrating its effectiveness.

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IJCAI Conference 2025 Conference Paper

DGL: Dynamic Global-Local Information Aggregation for Scalable VRP Generalization with Self-Improvement Learning

Yubin Xiao
Yuesong Wu
Rui Cao
Di Wang
Zhiguang Cao
Xuan Wu
Peng Zhao
Yuanshu Li

The Vehicle Routing Problem (VRP) is a critical combinatorial optimization problem with wide-reaching real-world applications, particularly in logistics, transportation. While neural network-based VRP solvers have shown impressive results on test instances similar to training data, their performance often degrades when faced with varying scales and unseen distributions, limiting their practical applicability. To overcome these limitations, we introduce DGL (Dynamic Global-Local Information Aggregation), a novel model that combines global and local information to effectively solve VRPs. DGL dynamically adjusts local node selections within a localized range, capturing local invariance across problems of different scales and distributions, thereby enhancing generalization. At the same time, DGL integrates global context into the decision-making process, providing richer information for more informed decisions. Additionally, we propose a replacement-based self-improvement learning framework that leverages data augmentation and random replacement techniques, further enhancing DGL's robustness. Extensive experiments on synthetic datasets, benchmark datasets, and real-world country map instances demonstrate that DGL achieves state-of-the-art performance, particularly in generalizing to large-scale VRPs and real-world scenarios. These results showcase DGL's effectiveness in solving complex, realistic optimization challenges and highlight its potential for practical applications.

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NeurIPS Conference 2025 Conference Paper

Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling

Jiaqi Wang
Zhiguang Cao
Peng Zhao
Rui Cao
Yubin Xiao
Yuan Jiang
You Zhou

The rise of smart manufacturing under Industry 4. 0 introduces mass customization and dynamic production, demanding more advanced and flexible scheduling techniques. The flexible job-shop scheduling problem (FJSP) has attracted significant attention due to its complex constraints and strong alignment with real-world production scenarios. Current deep reinforcement learning (DRL)-based approaches to FJSP predominantly employ constructive methods. While effective, they often fall short of reaching (near-)optimal solutions. In contrast, improvement-based methods iteratively explore the neighborhood of initial solutions and are more effective in approaching optimality. However, the flexible machine allocation in FJSP poses significant challenges to the application of this framework, including accurate state representation, effective policy learning, and efficient search strategies. To address these challenges, this paper proposes a $\textbf{M}$emory-enhanced $\textbf{I}$mprovement $\textbf{S}$earch framework with he$\textbf{t}$erogeneous gr$\textbf{a}$ph $\textbf{r}$epresentation—$\textit{MIStar}$. It employs a novel heterogeneous disjunctive graph that explicitly models the operation sequences on machines to accurately represent scheduling solutions. Moreover, a memory-enhanced heterogeneous graph neural network (MHGNN) is designed for feature extraction, leveraging historical trajectories to enhance the decision-making capability of the policy network. Finally, a parallel greedy search strategy is adopted to explore the solution space, enabling superior solutions with fewer iterations. Extensive experiments on synthetic data and public benchmarks demonstrate that $\textit{MIStar}$ significantly outperforms both traditional handcrafted improvement heuristics and state-of-the-art DRL-based constructive methods.

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EAAI Journal 2025 Journal Article

Synergistic co-evolution with neural networks for evolutionary optimization

Kun Bian
Juntao Zhang
Hong Han
You Zhou
Yifei Sun
Shi Cheng
Jun Zhou

Evolutionary computation (EC) algorithms, which simulate natural selection and genetic mechanisms, are widely applied to solve complex optimization and search problems. During the evolutionary process, EC algorithms inherently generate a wealth of evolutionary data. However, conventional EC algorithms often fail to effectively utilize this data, limiting their optimization effectiveness and overall performance. Neural networks, known for their powerful learning capabilities, excel in extracting features and recognizing patterns within large datasets. They can automatically identify complex relationships through their hierarchical architectures. Inspired by this, we propose a neural-synergized co-evolutionary optimization (NSCO) framework that integrates neural networks to learn from successfully evolved individuals during the EC process. This approach extracts valuable evolutionary knowledge to guide algorithms toward improved solutions and higher-quality data. Additionally, the enhanced data enables neural networks to derive richer evolutionary insights, creating a positive feedback loop that consistently improves performance. Notably, this framework operates without the need for additional expert knowledge, relying solely on the data generated by the algorithms themselves. To validate its effectiveness, we integrate this framework with 10 distinct EC algorithms and evaluate its performance using the CEC2014 benchmark suite. Results demonstrate significant enhancements in the algorithms’ performance.

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IJCAI Conference 2024 Conference Paper

CLIP-FSAC: Boosting CLIP for Few-Shot Anomaly Classification with Synthetic Anomalies

Zuo Zuo
Yao Wu
Baoqiang Li
Jiahao Dong
You Zhou
Lei Zhou
Yanyun Qu
Zongze Wu

Few-shot anomaly classification (FSAC) is a vital task in manufacturing industry. Recent methods focus on utilizing CLIP in zero/few normal shot anomaly detection instead of custom models. However, there is a lack of specific text prompts in anomaly classification and most of them ignore the modality gap between image and text. Meanwhile, there is distribution discrepancy between the pre-trained and the target data. To provide a remedy, in this paper, we propose a method to boost CLIP for few-normal-shot anomaly classification, dubbed CLIP-FSAC, which contains two-stage of training and alternating fine-tuning with two modality-specific adapters. Specifically, in the first stage, we train image adapter with text representation output from text encoder and introduce an image-to-text tuning to enhance multi-modal interaction and facilitate a better language-compatible visual representation. In the second stage, we freeze the image adapter to train the text adapter. Both of them are constrained by fusion-text contrastive loss. Comprehensive experiment results are provided for evaluating our method in few-normal-shot anomaly classification, which outperforms the state-of-the-art method by 12. 2%, 10. 9%, 10. 4% AUROC on VisA for 1, 2, and 4-shot settings.

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AAAI Conference 2024 Conference Paper

Distilling Autoregressive Models to Obtain High-Performance Non-autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed

Yubin Xiao
Di Wang
Boyang Li
Mingzhao Wang
Xuan Wu
Changliang Zhou
You Zhou

Neural construction models have shown promising performance for Vehicle Routing Problems (VRPs) by adopting either the Autoregressive (AR) or Non-Autoregressive (NAR) learning approach. While AR models produce high-quality solutions, they generally have a high inference latency due to their sequential generation nature. Conversely, NAR models generate solutions in parallel with a low inference latency but generally exhibit inferior performance. In this paper, we propose a generic Guided Non-Autoregressive Knowledge Distillation (GNARKD) method to obtain high-performance NAR models having a low inference latency. GNARKD removes the constraint of sequential generation in AR models while preserving the learned pivotal components in the network architecture to obtain the corresponding NAR models through knowledge distillation. We evaluate GNARKD by applying it to three widely adopted AR models to obtain NAR VRP solvers for both synthesized and real-world instances. The experimental results demonstrate that GNARKD significantly reduces the inference time (4-5 times faster) with acceptable performance drop (2-3%). To the best of our knowledge, this study is first-of-its-kind to obtain NAR VRP solvers from AR ones through knowledge distillation.

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