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Yong Jiang

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37 papers
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37

AAAI Conference 2026 Conference Paper

RatioSketch: Towards More Accurate Frequency Estimation in Data Streams via a Lightweight Neural Network

  • Mengbo Wang
  • Zhuochen Fan
  • Dayu Wang
  • Guorui Xie
  • Qing Li
  • Zeyu Luan
  • Yong Jiang
  • Tong Yang

Sketch-based solutions are widely used to estimate item frequencies in infinite data streams.Traditional hand-crafted sketches face the bottleneck of further eliminating errors because they cannot fully utilize the data stream distribution.Although recent neural sketches represented by MetaSketch and LegoSketch have improved generalization capabilities, they face bottlenecks such as high computational overhead and parameter sensitivity.Meanwhile, they ignore load information, fail to fully utilize the local information in hand-crafted sketches, and do not focus on the frequent items that are usually more important in data streams.In this paper, we propose RatioSketch, a novel lightweight neural network correction framework that synergizes the advantages of hand-crafted sketches and neural sketches in a ``micro-correction'' paradigm.The key idea is to retain the efficient underlying data structure of the hand-crafted sketch and to build a neural correction layer in its output space. We select multiple representative hand-crafted sketches as use cases to study the correction performance of RatioSketch on them.Extensive experimental evaluations on several real-world datasets show that RatioSketch-corrected sketches achieve consistently higher estimation accuracy than their uncorrected counterparts, as well as outperforming neural baselines such as MetaSketch and LegoSketch under identical memory budgets.

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AAAI Conference 2026 Conference Paper

Suit the Remedy to the Retriever: Interpretable Query Optimization with Retriever Preference Alignment for Vision-Language Retrieval

  • GuangHao Meng
  • Jinpeng Wang
  • Jieming Zhu
  • Letian Zhang
  • Yong Jiang
  • Dan Zhao
  • Qing Li

Vision-language retrieval (VLR), which uses text or image queries to retrieve corresponding cross-modal content, plays a crucial role in multimedia and computer vision tasks. However, challenging concepts in queries often confuse retrievers, limiting their ability to align concepts with visual content. Existing query optimization methods neglect retrievers’ preferences (i.e., text descriptions that better match their corresponding visual content), resulting in unadapted to the retriever and leading to suboptimal performance. To address this, we propose the Retriever-Adaptive Query Optimization (RAQO), an interpretable framework that rewrites queries based on retriever-specific preferences. Specifically, we first leverages multimodal large language Models (MLLMs) and retrieval's feedback to construct the MLLMs-Driven Preference-Aware Dataset Engine (MPADE), which automatically refine queries offline, capturing the retriever’s implicit preferences. Then, we introduce a ``detect-then-rewrite" chain-of-thought rewriting (ReCoT) strategy equipped with a progressive preference alignment pipeline, including three stages: ambiguity detection fine-tuning, query rewriting fine-tuning, and preference rank optimization. This design enables the rewriter to focus on confusing concepts and produce retriever-adapted, high-quality queries. Extensive VLR benchmark experiments have demonstrated the superiority of RAQO in cross-modal retrieval, as well as its interpretability, generalizability and transferability.

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AAAI Conference 2025 Conference Paper

CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning

  • Peiyuan Liu
  • Hang Guo
  • Tao Dai
  • Naiqi Li
  • Jigang Bao
  • Xudong Ren
  • Yong Jiang
  • Shu-Tao Xia

Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data. However, current LLM-based MTSF methods usually focus on adapting and fine-tuning LLMs, while neglecting the distribution discrepancy between textual and temporal input tokens, thus leading to sub-optimal performance. To address this issue, we propose a novel Cross-Modal LLM Fine-Tuning (CALF) framework for MTSF by reducing the distribution discrepancy between textual and temporal data, which mainly consists of the temporal target branch with temporal input and the textual source branch with aligned textual input. To reduce the distribution discrepancy, we develop the cross-modal match module to first align cross-modal input distributions. Additionally, to minimize the modality distribution gap in both feature and output spaces, feature regularization loss is developed to align the intermediate features between the two branches for better weight updates, while output consistency loss is introduced to allow the output representations of both branches to correspond effectively. Thanks to the modality alignment, CALF establishes state-of-the-art performance for both long-term and short-term forecasting tasks with low computational complexity, and exhibits favorable few-shot and zero-shot abilities similar to that in LLMs.

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IJCAI Conference 2025 Conference Paper

Efficient Differentiable Approximation of Generalized Low-rank Regularization

  • Naiqi Li
  • Yuqiu Xie
  • Peiyuan Liu
  • Tao Dai
  • Yong Jiang
  • Shu-Tao Xia

Low-rank regularization (LRR) has been widely applied in various machine learning tasks, but the associated optimization is challenging. Directly optimizing the rank function under constraints is NP-hard in general. To overcome this difficulty, various relaxations of the rank function were studied. However, optimization of these relaxed LRRs typically depends on singular value decomposition, which is a time-consuming and nondifferentiable operator that cannot be optimized with gradient-based techniques. To address these challenges, in this paper we propose an efficient differentiable approximation of the generalized LRR. The considered LRR form subsumes many popular choices like the nuclear norm, the Schatten-p norm, and various nonconvex relaxations. Our method enables LRR terms to be appended to loss functions in a plug-and-play fashion, and the GPU-friendly operations enable efficient and convenient implementation. Furthermore, convergence analysis is presented, which rigorously shows that both the bias and the variance of our rank estimator rapidly reduce with increased sample size and iteration steps. In the experimental study, the proposed method is applied to various tasks, which demonstrates its versatility and efficiency. Code is available at https: //github. com/naiqili/EDLRR.

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AAAI Conference 2025 Conference Paper

EvdCLIP: Improving Vision-Language Retrieval with Entity Visual Descriptions from Large Language Models

  • GuangHao Meng
  • Sunan He
  • Jinpeng Wang
  • Tao Dai
  • Letian Zhang
  • Jieming Zhu
  • Qing Li
  • Gang Wang

Vision-language retrieval (VLR) has attracted significant attention in both academia and industry, which involves using text (or images) as queries to retrieve corresponding images (or text). However, existing methods often neglect the rich visual semantics knowledge of entities, thus leading to incorrect retrieval results. To address this problem, we propose the Entity Visual Description enhanced CLIP (EvdCLIP), designed to leverage the visual knowledge of entities to enrich queries. Specifically, since humans recognize entities through visual cues, we employ a large language model (LLM) to generate Entity Visual Descriptions (EVDs) as alignment cues to complement textual data. These EVDs are then integrated into raw queries to create visually-rich, EVD-enhanced queries. Furthermore, recognizing that EVD-enhanced queries may introduce noise or low-quality expansions, we develop a novel, trainable EVD-aware Rewriter (EaRW) for vision-language retrieval tasks. EaRW utilizes EVD knowledge and the generative capabilities of the language model to effectively rewrite queries. With our specialized training strategy, EaRW can generate high-quality and low-noise EVD-enhanced queries. Extensive quantitative and qualitative experiments on image-text retrieval benchmarks validate the superiority of EvdCLIP on vision-language retrieval tasks.

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NeurIPS Conference 2025 Conference Paper

WebDancer: Towards Autonomous Information Seeking Agency

  • Jialong Wu
  • Baixuan Li
  • Runnan Fang
  • Wenbiao Yin
  • Liwen Zhang
  • Zhenglin Wang
  • Zhengwei Tao
  • Ding-Chu Zhang

Addressing intricate real-world problems necessitates in-depth information seeking and multi-step reasoning. Recent progress in agentic systems, exemplified by Deep Research, underscores the potential for autonomous multi-step research. In this work, we present a cohesive paradigm for building end-to-end agentic information seeking agents from a data-centric and training-stage perspective. Our approach consists of four key stages: (1) browsing data construction, (2) trajectories sampling, (3) supervised fine-tuning for effective cold start, and (4) reinforcement learning for enhanced generalisation. We instantiate this framework in a web agent based on the ReAct format, WebDancer. Empirical evaluations on the challenging GAIA and WebWalkerQA benchmarks demonstrate the strong performance of WebDancer, achieving considerable results and highlighting the efficacy of our training paradigm. Further analysis of agent training provides valuable insights and actionable, systematic pathways for developing more capable agentic models.

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AIIM Journal 2024 Journal Article

A clinically actionable and explainable real-time risk assessment framework for stroke-associated pneumonia

  • Lutao Dai
  • Xin Yang
  • Hao Li
  • Xingquan Zhao
  • Lin Lin
  • Yong Jiang
  • Yongjun Wang
  • Zixiao Li

The current medical practice is more responsive rather than proactive, despite the widely recognized value of early disease detection, including improving the quality of care and reducing medical costs. One of the cornerstones of early disease detection is clinically actionable predictions, where predictions are expected to be accurate, stable, real-time and interpretable. As an example, we used stroke-associated pneumonia (SAP), setting up a transformer-encoder-based model that analyzes highly heterogeneous electronic health records in real-time. The model was proven accurate and stable on an independent test set. In addition, it issued at least one warning for 98. 6 % of SAP patients, and on average, its alerts were ahead of physician diagnoses by 2. 71 days. We applied Integrated Gradient to glean the model's reasoning process. Supplementing the risk scores, the model highlighted critical historical events on patients' trajectories, which were shown to have high clinical relevance.

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NeurIPS Conference 2024 Conference Paper

Agent Planning with World Knowledge Model

  • Shuofei Qiao
  • Runnan Fang
  • Ningyu Zhang
  • Yuqi Zhu
  • Xiang Chen
  • Shumin Deng
  • Yong Jiang
  • Pengjun Xie

Recent endeavors towards directly using large language models (LLMs) as agent models to execute interactive planning tasks have shown commendable results. Despite their achievements, however, they still struggle with brainless trial-and-error in global planning and generating hallucinatory actions in local planning due to their poor understanding of the "real" physical world. Imitating humans' mental world knowledge model which provides global prior knowledge before the task and maintains local dynamic knowledge during the task, in this paper, we introduce parametric World Knowledge Model (WKM) to facilitate agent planning. Concretely, we steer the agent model to self-synthesize knowledge from both expert and sampled trajectories. Then we develop WKM, providing prior task knowledge to guide the global planning and dynamic state knowledge to assist the local planning. Experimental results on three real-world simulated datasets with Mistral-7B, Gemma-7B, and Llama-3-8B demonstrate that our method can achieve superior performance compared to various strong baselines. Besides, we analyze to illustrate that our WKM can effectively alleviate the blind trial-and-error and hallucinatory action issues, providing strong support for the agent's understanding of the world. Other interesting findings include: 1) our instance-level task knowledge can generalize better to unseen tasks, 2) weak WKM can guide strong agent model planning, and 3) unified WKM training has promising potential for further development.

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NeurIPS Conference 2024 Conference Paper

Dissect Black Box: Interpreting for Rule-Based Explanations in Unsupervised Anomaly Detection

  • Yu Zhang
  • Ruoyu Li
  • Nengwu Wu
  • Qing Li
  • Xinhan Lin
  • Yang Hu
  • Tao Li
  • Yong Jiang

In high-stakes sectors such as network security, IoT security, accurately distinguishing between normal and anomalous data is critical due to the significant implications for operational success and safety in decision-making. The complexity is exacerbated by the presence of unlabeled data and the opaque nature of black-box anomaly detection models, which obscure the rationale behind their predictions. In this paper, we present a novel method to interpret the decision-making processes of these models, which are essential for detecting malicious activities without labeled attack data. We put forward the Segmentation Clustering Decision Tree (SCD-Tree), designed to dissect and understand the structure of normal data distributions. The SCD-Tree integrates predictions from the anomaly detection model into its splitting criteria, enhancing the clustering process with the model's insights into anomalies. To further refine these segments, the Gaussian Boundary Delineation (GBD) algorithm is employed to define boundaries within each segmented distribution, effectively delineating normal from anomalous data points. At this point, this approach addresses the curse of dimensionality by segmenting high-dimensional data and ensures resilience to data drift and perturbations through flexible boundary fitting. We transform the intricate operations of anomaly detection into an interpretable rule's format, constructing a comprehensive set of rules for understanding. Our method's evaluation on diverse datasets and models demonstrates superior explanation accuracy, fidelity, and robustness over existing method, proving its efficacy in environments where interpretability is paramount.

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AAAI Conference 2024 Conference Paper

EcomGPT: Instruction-Tuning Large Language Models with Chain-of-Task Tasks for E-commerce

  • Yangning Li
  • Shirong Ma
  • Xiaobin Wang
  • Shen Huang
  • Chengyue Jiang
  • Hai-Tao Zheng
  • Pengjun Xie
  • Fei Huang

Recently, instruction-following Large Language Models (LLMs), represented by ChatGPT, have exhibited exceptional performance in general Natural Language Processing (NLP) tasks. However, the unique characteristics of E-commerce data pose significant challenges to general LLMs. An LLM tailored specifically for E-commerce scenarios, possessing robust cross-dataset/task generalization capabilities, is a pressing necessity. To solve this issue, in this work, we proposed the first E-commerce instruction dataset EcomInstruct, with a total of 2.5 million instruction data. EcomInstruct scales up the data size and task diversity by constructing atomic tasks with E-commerce basic data types, such as product information, user reviews. Atomic tasks are defined as intermediate tasks implicitly involved in solving a final task, which we also call Chain-of-Task tasks. We developed EcomGPT with different parameter scales by training the backbone model BLOOMZ with the EcomInstruct. Benefiting from the fundamental semantic understanding capabilities acquired from the Chain-of-Task tasks, EcomGPT exhibits excellent zero-shot generalization capabilities. Extensive experiments and human evaluations demonstrate that EcomGPT outperforms ChatGPT in term of cross-dataset/task generalization on E-commerce tasks. The EcomGPT will be public at https://github.com/Alibaba-NLP/EcomGPT.

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IJCAI Conference 2024 Conference Paper

FactCHD: Benchmarking Fact-Conflicting Hallucination Detection

  • Xiang Chen
  • Duanzheng Song
  • Honghao Gui
  • Chenxi Wang
  • Ningyu Zhang
  • Yong Jiang
  • Fei Huang
  • Chengfei Lyu

Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting hallucinations in real-world applications. The accurate identification of hallucinations in texts generated by LLMs, especially in complex inferential scenarios, is a relatively unexplored area. To address this gap, we present FactCHD, a dedicated benchmark designed for the detection of fact-conflicting hallucinations from LLMs. FactCHD features a diverse dataset that spans various factuality patterns, including vanilla, multi-hop, comparison, and set operation. A distinctive element of FactCHD is its integration of fact-based evidence chains, significantly enhancing the depth of evaluating the detectors' explanations. Experiments on different LLMs expose the shortcomings of current approaches in detecting factual errors accurately. Furthermore, we introduce TRUTH-TRIANGULATOR which synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2, aiming to yield more credible detection through the amalgamation of predictive results and evidence.

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YNIMG Journal 2024 Journal Article

Relationships between brain structure-function coupling in normal aging and cognition: A cross-ethnicity population-based study

  • Chang Liu
  • Jing Jing
  • Jiyang Jiang
  • Wei Wen
  • Wanlin Zhu
  • Zixiao Li
  • Yuesong Pan
  • Xueli Cai

Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly.

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AAAI Conference 2024 Conference Paper

SeqGPT: An Out-of-the-Box Large Language Model for Open Domain Sequence Understanding

  • Tianyu Yu
  • Chengyue Jiang
  • Chao Lou
  • Shen Huang
  • Xiaobin Wang
  • Wei Liu
  • Jiong Cai
  • Yangning Li

Large language models (LLMs) have shown impressive abilities for open-domain NLP tasks. However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which always have restricted output and input format. Their performances on NLU tasks are highly related to prompts or demonstrations and are shown to be poor at performing several representative NLU tasks, such as event extraction and entity typing. To this end, we present SeqGPT, a bilingual (i.e., English and Chinese) open-source autoregressive model specially enhanced for open-domain natural language understanding. We express all NLU tasks with two atomic tasks, which define fixed instructions to restrict the input and output format but still ``open'' for arbitrarily varied label sets. The model is first instruction-tuned with extremely fine-grained labeled data synthesized by ChatGPT and then further fine-tuned by 233 different atomic tasks from 152 datasets across various domains. The experimental results show that SeqGPT has decent classification and extraction ability, and is capable of performing language understanding tasks on unseen domains. We also conduct empirical studies on the scaling of data and model size as well as on the transfer across tasks. Our models are accessible at https://github.com/Alibaba-NLP/SeqGPT.

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AAAI Conference 2024 Conference Paper

Three Heads Are Better than One: Improving Cross-Domain NER with Progressive Decomposed Network

  • Xuming Hu
  • Zhaochen Hong
  • Yong Jiang
  • Zhichao Lin
  • Xiaobin Wang
  • Pengjun Xie
  • Philip S. Yu

Cross-domain named entity recognition (NER) tasks encourage NER models to transfer knowledge from data-rich source domains to sparsely labeled target domains. Previous works adopt the paradigms of pre-training on the source domain followed by fine-tuning on the target domain. However, these works ignore that general labeled NER source domain data can be easily retrieved in the real world, and soliciting more source domains could bring more benefits. Unfortunately, previous paradigms cannot efficiently transfer knowledge from multiple source domains. In this work, to transfer multiple source domains' knowledge, we decouple the NER task into the pipeline tasks of mention detection and entity typing, where the mention detection unifies the training object across domains, thus providing the entity typing with higher-quality entity mentions. Additionally, we request multiple general source domain models to suggest the potential named entities for sentences in the target domain explicitly, and transfer their knowledge to the target domain models through the knowledge progressive networks implicitly. Furthermore, we propose two methods to analyze in which source domain knowledge transfer occurs, thus helping us judge which source domain brings the greatest benefit. In our experiment, we develop a Chinese cross-domain NER dataset. Our model improved the F1 score by an average of 12.50% across 8 Chinese and English datasets compared to models without source domain data.

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NeurIPS Conference 2024 Conference Paper

WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models

  • Peng Wang
  • Zexi Li
  • Ningyu Zhang
  • Ziwen Xu
  • Yunzhi Yao
  • Yong Jiang
  • Pengjun Xie
  • Fei Huang

Large language models (LLMs) need knowledge updates to meet the ever-growing world facts and correct the hallucinated responses, facilitating the methods of lifelong model editing. Where the updated knowledge resides in memories is a fundamental question for model editing. In this paper, we find that editing either long-term memory (direct model parameters) or working memory (non-parametric knowledge of neural network activations/representations by retrieval) will result in an impossible triangle---reliability, generalization, and locality can not be realized together in the lifelong editing settings. For long-term memory, directly editing the parameters will cause conflicts with irrelevant pretrained knowledge or previous edits (poor reliability and locality). For working memory, retrieval-based activations can hardly make the model understand the edits and generalize (poor generalization). Therefore, we propose WISE to bridge the gap between memories. In WISE, we design a dual parametric memory scheme, which consists of the main memory for the pretrained knowledge and a side memory for the edited knowledge. We only edit the knowledge in the side memory and train a router to decide which memory to go through when given a query. For continual editing, we devise a knowledge-sharding mechanism where different sets of edits reside in distinct subspaces of parameters, and are subsequently merged into a shared memory without conflicts. Extensive experiments show that WISE can outperform previous model editing methods and overcome the impossible triangle under lifelong model editing of question answering, hallucination, and out-of-distribution settings across trending LLM architectures, e. g. , GPT, LLaMA, and Mistral.

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AAAI Conference 2023 Conference Paper

Covariate-Shift Generalization via Random Sample Weighting

  • Yue He
  • Xinwei Shen
  • Renzhe Xu
  • Tong Zhang
  • Yong Jiang
  • Wenchao Zou
  • Peng Cui

Shifts in the marginal distribution of covariates from training to the test phase, named covariate-shifts, often lead to unstable prediction performance across agnostic testing data, especially under model misspecification. Recent literature on invariant learning attempts to learn an invariant predictor from heterogeneous environments. However, the performance of the learned predictor depends heavily on the availability and quality of provided environments. In this paper, we propose a simple and effective non-parametric method for generating heterogeneous environments via Random Sample Weighting (RSW). Given the training dataset from a single source environment, we randomly generate a set of covariate-determining sample weights and use each weighted training distribution to simulate an environment. We theoretically show that under appropriate conditions, such random sample weighting can produce sufficient heterogeneity to be exploited by common invariance constraints to find the invariant variables for stable prediction under covariate shifts. Extensive experiments on both simulated and real-world datasets clearly validate the effectiveness of our method.

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NeurIPS Conference 2023 Conference Paper

Interpreting Unsupervised Anomaly Detection in Security via Rule Extraction

  • Ruoyu Li
  • Qing Li
  • Yu Zhang
  • Dan Zhao
  • Yong Jiang
  • Yong Yang

Many security applications require unsupervised anomaly detection, as malicious data are extremely rare and often only unlabeled normal data are available for training (i. e. , zero-positive). However, security operators are concerned about the high stakes of trusting black-box models due to their lack of interpretability. In this paper, we propose a post-hoc method to globally explain a black-box unsupervised anomaly detection model via rule extraction. First, we propose the concept of distribution decomposition rules that decompose the complex distribution of normal data into multiple compositional distributions. To find such rules, we design an unsupervised Interior Clustering Tree that incorporates the model prediction into the splitting criteria. Then, we propose the Compositional Boundary Exploration (CBE) algorithm to obtain the boundary inference rules that estimate the decision boundary of the original model on each compositional distribution. By merging these two types of rules into a rule set, we can present the inferential process of the unsupervised black-box model in a human-understandable way, and build a surrogate rule-based model for online deployment at the same time. We conduct comprehensive experiments on the explanation of four distinct unsupervised anomaly detection models on various real-world datasets. The evaluation shows that our method outperforms existing methods in terms of diverse metrics including fidelity, correctness and robustness.

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NeurIPS Conference 2023 Conference Paper

Metis: Understanding and Enhancing In-Network Regular Expressions

  • Zhengxin Zhang
  • Yucheng Huang
  • Guanglin Duan
  • Qing Li
  • Dan Zhao
  • Yong Jiang
  • Lianbo Ma
  • Xi Xiao

Regular expressions (REs) offer one-shot solutions for many networking tasks, e. g. , network intrusion detection. However, REs purely rely on expert knowledge and cannot utilize labeled data for better accuracy. Today, neural networks (NNs) have shown superior accuracy and flexibility, thanks to their ability to learn from rich labeled data. Nevertheless, NNs are often incompetent in cold-start scenarios and too complex for deployment on network devices. In this paper, we propose Metis, a general framework that converts REs to network device affordable models for superior accuracy and throughput by taking advantage of REs' expert knowledge and NNs' learning ability. In Metis, we convert REs to byte-level recurrent neural networks (BRNNs) without training. The BRNNs preserve expert knowledge from REs and offer adequate accuracy in cold-start scenarios. When rich labeled data is available, the performance of BRNNs can be improved by training. Furthermore, we design a semi-supervised knowledge distillation to transform the BRNNs into pooling soft random forests (PSRFs) that can be deployed on network devices. To the best of our knowledge, this is the first method to employ model inference as an alternative to RE matching in network scenarios. We collect network traffic data on our campus for three weeks and evaluate Metis on them. Experimental results show that Metis is more accurate than original REs and other baselines, achieving superior throughput when deployed on network devices.

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IJCAI Conference 2023 Conference Paper

One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER

  • Xiang Chen
  • Lei Li
  • Shuofei Qiao
  • Ningyu Zhang
  • Chuanqi Tan
  • Yong Jiang
  • Fei Huang
  • Huajun Chen

Cross-domain NER is a challenging task to address the low-resource problem in practical scenarios. Previous typical solutions mainly obtain a NER model by pre-trained language models (PLMs) with data from a rich-resource domain and adapt it to the target domain. Owing to the mismatch issue among entity types in different domains, previous approaches normally tune all parameters of PLMs, ending up with an entirely new NER model for each domain. Moreover, current models only focus on leveraging knowledge in one general source domain while failing to successfully transfer knowledge from multiple sources to the target. To address these issues, we introduce Collaborative Domain-Prefix Tuning for cross-domain NER (CP-NER) based on text-to-text generative PLMs. Specifically, we present text-to-text generation grounding domain-related instructors to transfer knowledge to new domain NER tasks without structural modifications. We utilize frozen PLMs and conduct collaborative domain-prefix tuning to stimulate the potential of PLMs to handle NER tasks across various domains. Experimental results on the Cross-NER benchmark show that the proposed approach has flexible transfer ability and performs better on both one-source and multiple-source cross-domain NER tasks.

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AAMAS Conference 2023 Conference Paper

User Device Interaction Prediction via Relational Gated Graph Attention Network and Intent-aware Encoder

  • Jingyu Xiao
  • Qingsong Zou
  • Qing Li
  • Dan Zhao
  • Kang Li
  • Wenxin Tang
  • Runjie Zhou
  • Yong Jiang

With the booming of smart home market, intelligent Internet of Things (IoT) devices have been increasingly more involved in home life. To improve the user experience of smart home, some prior works have explored how to use time series analysis technology for predicting the interaction between users and devices. However, existing solutions have inferior User Device Interaction (UDI) prediction accuracy, as they fail to consider the complex heterogeneous device transitions, multiple intents of a user and multi-level periodicity of user behaviors. In this paper, we present DeepUDI, a novel approach for accurate UDI prediction. First, we propose Relational Gated Graph Attention Network (RGGAT) to learn embedding of device and device control while considering complex heterogeneous temporal transitions. Second, we propose Intent-aware Encoder (IAE) to encode multiple intents of users via capsule networks. Third, we design a Historical Attention Mechanism (HAM) to capture the multi-level periodicity by aggregating the current sequence and the historical sequence representations through the attention mechanism. Comprehensive experiments on four realworld datasets show that DeepUDI consistently outperforms stateof-the-art baselines and also offers highly interpretable results.

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AAAI Conference 2022 Conference Paper

Defending against Model Stealing via Verifying Embedded External Features

  • Yiming Li
  • Linghui Zhu
  • Xiaojun Jia
  • Yong Jiang
  • Shu-Tao Xia
  • Xiaochun Cao

Obtaining a well-trained model involves expensive data collection and training procedures, therefore the model is a valuable intellectual property. Recent studies revealed that adversaries can ‘steal’ deployed models even when they have no training samples and can not get access to the model parameters or structures. Currently, there were some defense methods to alleviate this threat, mostly by increasing the cost of model stealing. In this paper, we explore the defense from another angle by verifying whether a suspicious model contains the knowledge of defender-specified external features. Specifically, we embed the external features by tempering a few training samples with style transfer. We then train a meta-classifier to determine whether a model is stolen from the victim. This approach is inspired by the understanding that the stolen models should contain the knowledge of features learned by the victim model. We examine our method on both CIFAR-10 and ImageNet datasets. Experimental results demonstrate that our method is effective in detecting different types of model stealing simultaneously, even if the stolen model is obtained via a multi-stage stealing process. The codes for reproducing main results are available at Github (https: //github. com/zlh-thu/StealingVerification).

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NeurIPS Conference 2022 Conference Paper

Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection

  • Yiming Li
  • Yang Bai
  • Yong Jiang
  • Yong Yang
  • Shu-Tao Xia
  • Bo Li

Deep neural networks (DNNs) have demonstrated their superiority in practice. Arguably, the rapid development of DNNs is largely benefited from high-quality (open-sourced) datasets, based on which researchers and developers can easily evaluate and improve their learning methods. Since the data collection is usually time-consuming or even expensive, how to protect their copyrights is of great significance and worth further exploration. In this paper, we revisit dataset ownership verification. We find that existing verification methods introduced new security risks in DNNs trained on the protected dataset, due to the targeted nature of poison-only backdoor watermarks. To alleviate this problem, in this work, we explore the untargeted backdoor watermarking scheme, where the abnormal model behaviors are not deterministic. Specifically, we introduce two dispersibilities and prove their correlation, based on which we design the untargeted backdoor watermark under both poisoned-label and clean-label settings. We also discuss how to use the proposed untargeted backdoor watermark for dataset ownership verification. Experiments on benchmark datasets verify the effectiveness of our methods and their resistance to existing backdoor defenses.

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NeurIPS Conference 2021 Conference Paper

Clustering Effect of Adversarial Robust Models

  • Yang Bai
  • Xin Yan
  • Yong Jiang
  • Shu-Tao Xia
  • Yisen Wang

Adversarial robustness has received increasing attention along with the study of adversarial examples. So far, existing works show that robust models not only obtain robustness against various adversarial attacks but also boost the performance in some downstream tasks. However, the underlying mechanism of adversarial robustness is still not clear. In this paper, we interpret adversarial robustness from the perspective of linear components, and find that there exist some statistical properties for comprehensively robust models. Specifically, robust models show obvious hierarchical clustering effect on their linearized sub-networks, when removing or replacing all non-linear components (e. g. , batch normalization, maximum pooling, or activation layers). Based on these observations, we propose a novel understanding of adversarial robustness and apply it on more tasks including domain adaption and robustness boosting. Experimental evaluations demonstrate the rationality and superiority of our proposed clustering strategy. Our code is available at https: //github. com/bymavis/Adv Weight NeurIPS2021.

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TCS Journal 2021 Journal Article

Constrained inverse minimum flow problems under the weighted Hamming distance

  • Yong Jiang
  • Weifeng Lin
  • Longcheng Liu
  • Anzhen Peng

In an inverse combinatorial optimization problem, a feasible solution is given and is not optimal under the current parameters. The aim is to make the feasible solution optimal by modifying the current parameters as little as possible. The modification cost can be measured by different norms, such as weighted l 1 norm, weighted l 2 norm, weighted l ∞ norm, weighted Hamming distance and so on. In this paper, we focus on the constrained inverse minimum flow problems under the weighted Hamming distance. Three different models are considered: the general problem under the weighted bottleneck-type Hamming distance and two cases under the mixed of sum-type and bottleneck type. Strongly polynomial algorithms are presented for the models we studied.

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ICLR Conference 2021 Conference Paper

Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search

  • Peidong Liu
  • Gengwei Zhang
  • Bochao Wang
  • Hang Xu 0004
  • Xiaodan Liang
  • Yong Jiang
  • Zhenguo Li

Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models. For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges (e.g. class imbalance, hard negative samples, and scale variances). Inspired by the recent progress in network architecture search, it is interesting to explore the possibility of discovering new loss function formulations via directly searching the primitive operation combinations. So that the learned losses not only fit for diverse object detection challenges to alleviate huge human efforts, but also have better alignment with evaluation metric and good mathematical convergence property. Beyond the previous auto-loss works on face recognition and image classification, our work makes the first attempt to discover new loss functions for the challenging object detection from primitive operation levels and finds the searched losses are insightful. We propose an effective convergence-simulation driven evolutionary search algorithm, called CSE-Autoloss, for speeding up the search progress by regularizing the mathematical rationality of loss candidates via two progressive convergence simulation modules: convergence property verification and model optimization simulation. CSE-Autoloss involves the search space (i.e. 21 mathematical operators, 3 constant-type inputs, and 3 variable-type inputs) that cover a wide range of the possible variants of existing losses and discovers best-searched loss function combination within a short time (around 1.5 wall-clock days with 20x speedup in comparison to the vanilla evolutionary algorithm). We conduct extensive evaluations of loss function search on popular detectors and validate the good generalization capability of searched losses across diverse architectures and various datasets. Our experiments show that the best-discovered loss function combinations outperform default combinations (Cross-entropy/Focal loss for classification and L1 loss for regression) by 1.1% and 0.8% in terms of mAP for two-stage and one-stage detectors on COCO respectively. Our searched losses are available at https://github.com/PerdonLiu/CSE-Autoloss.

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NeurIPS Conference 2020 Conference Paper

Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities

  • Chaobing Song
  • Zhengyuan Zhou
  • Yichao Zhou
  • Yong Jiang
  • Yi Ma

The optimization problems associated with training generative adversarial neural networks can be largely reduced to certain {\em non-monotone} variational inequality problems (VIPs), whereas existing convergence results are mostly based on monotone or strongly monotone assumptions. In this paper, we propose {\em optimistic dual extrapolation (OptDE)}, a method that only performs {\em one} gradient evaluation per iteration. We show that OptDE is provably convergent to {\em a strong solution} under different coherent non-monotone assumptions. In particular, when a {\em weak solution} exists, the convergence rate of our method is $O(1/{\epsilon^{2}})$, which matches the best existing result of the methods with two gradient evaluations. Further, when a {\em $\sigma$-weak solution} exists, the convergence guarantee is improved to the linear rate $O(\log\frac{1}{\epsilon})$. Along the way--as a byproduct of our inquiries into non-monotone variational inequalities--we provide the near-optimal $O\big(\frac{1}{\epsilon}\log \frac{1}{\epsilon}\big)$ convergence guarantee in terms of restricted strong merit function for monotone variational inequalities. We also show how our results can be naturally generalized to the stochastic setting, and obtain corresponding new convergence results. Taken together, our results contribute to the broad landscape of variational inequality--both non-monotone and monotone alike--by providing a novel and more practical algorithm with the state-of-the-art convergence guarantees.

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NeurIPS Conference 2020 Conference Paper

Stochastic Deep Gaussian Processes over Graphs

  • Naiqi Li
  • Wenjie Li
  • Jifeng Sun
  • Yinghua Gao
  • Yong Jiang
  • Shu-Tao Xia

In this paper we propose Stochastic Deep Gaussian Processes over Graphs (DGPG), which are deep structure models that learn the mappings between input and output signals in graph domains. The approximate posterior distributions of the latent variables are derived with variational inference, and the evidence lower bound is evaluated and optimized by the proposed recursive sampling scheme. The Bayesian non-parametric natural of our model allows it to resist overfitting, while the expressive deep structure grants it the potential to learn complex relations. Extensive experiments demonstrate that our method achieves superior performances in both small size ( 35, 000) datasets. We show that DGPG outperforms another Gaussian-based approach, and is competitive to a state-of-the-art method in the challenging task of traffic flow prediction. Our model is also capable of capturing uncertainties in a mathematical principled way and automatically discovering which vertices and features are relevant to the prediction.

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NeurIPS Conference 2020 Conference Paper

Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization

  • Chaobing Song
  • Yong Jiang
  • Yi Ma

In this paper, we introduce a simplified and unified method for finite-sum convex optimization, named \emph{Variance Reduction via Accelerated Dual Averaging (VRADA)}. In the general convex and smooth setting, VRADA can attain an $O\big(\frac{1}{n}\big)$-accurate solution in $O(n\log\log n)$ number of stochastic gradient evaluations, where $n$ is the number of samples; meanwhile, VRADA matches the lower bound of this setting up to a $\log\log n$ factor. In the strongly convex and smooth setting, VRADA matches the lower bound in the regime $n \le \Theta(\kappa)$, while it improves the rate in the regime $n\gg \kappa$ to $O\big(n +\frac{n\log(1/\epsilon)}{\log(n/\kappa)}\big)$, where $\kappa$ is the condition number. Besides improving the best known complexity results, VRADA has more unified and simplified algorithmic implementation and convergence analysis for both the general convex and strongly convex settings. Through experiments on real datasets, we show the good performance of VRADA over existing methods for large-scale machine learning problems.

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IJCAI Conference 2019 Conference Paper

Automatic Grassland Degradation Estimation Using Deep Learning

  • Xiyu Yan
  • Yong Jiang
  • Shuai Chen
  • Zihao He
  • Chunmei Li
  • Shu-Tao Xia
  • Tao Dai
  • Shuo Dong

Grassland degradation estimation is essential to prevent global land desertification and sandstorms. Typically, the key to such estimation is to measure the coverage of indicator plants. However, traditional methods of estimation rely heavily on human eyes and manual labor, thus inevitably leading to subjective results and high labor costs. In contrast, deep learning-based image segmentation algorithms are potentially capable of automatic assessment of the coverage of indicator plants. Nevertheless, a suitable image dataset comprising grassland images is not publicly available. To this end, we build an original Automatic Grassland Degradation Estimation Dataset (AGDE-Dataset), with a large number of grassland images captured from the wild. Based on AGDE-Dataset, we are able to propose a brand new scheme to automatically estimate grassland degradation, which mainly consists of two components. 1) Semantic segmentation: we design a deep neural network with an improved encoder-decoder structure to implement semantic segmentation of grassland images. In addition, we propose a novel Focal-Hinge Loss to alleviate the class imbalance of semantics in the training stage. 2) Degradation estimation: we provide the estimation of grassland degradation based on the results of semantic segmentation. Experimental results show that the proposed method achieves satisfactory accuracy in grassland degradation estimation.

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AAAI Conference 2019 Conference Paper

Bidirectional Transition-Based Dependency Parsing

  • Yunzhe Yuan
  • Yong Jiang
  • Kewei Tu

Transition-based dependency parsing is a fast and effective approach for dependency parsing. Traditionally, a transitionbased dependency parser processes an input sentence and predicts a sequence of parsing actions in a left-to-right manner. During this process, an early prediction error may negatively impact the prediction of subsequent actions. In this paper, we propose a simple framework for bidirectional transitionbased parsing. During training, we learn a left-to-right parser and a right-to-left parser separately. To parse a sentence, we perform joint decoding with the two parsers. We propose three joint decoding algorithms that are based on joint scoring, dual decomposition, and dynamic oracle respectively. Empirical results show that our methods lead to competitive parsing accuracy and our method based on dynamic oracle consistently achieves the best performance.

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AAAI Conference 2018 Conference Paper

Maximum A Posteriori Inference in Sum-Product Networks

  • Jun Mei
  • Yong Jiang
  • Kewei Tu

Sum-product networks (SPNs) are a class of probabilistic graphical models that allow tractable marginal inference. However, the maximum a posteriori (MAP) inference in SPNs is NP-hard. We investigate MAP inference in SPNs from both theoretical and algorithmic perspectives. For the theoretical part, we reduce general MAP inference to its special case without evidence and hidden variables; we also show that it is NP-hard to approximate the MAP problem to 2n for fixed 0 ≤ < 1, where n is the input size. For the algorithmic part, we first present an exact MAP solver that runs reasonably fast and could handle SPNs with up to 1k variables and 150k arcs in our experiments. We then present a new approximate MAP solver with a good balance between speed and accuracy, and our comprehensive experiments on real-world datasets show that it has better overall performance than existing approximate solvers.

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NeurIPS Conference 2017 Conference Paper

Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex

  • Chaobing Song
  • Shaobo Cui
  • Yong Jiang
  • Shu-Tao Xia

In this paper we study the well-known greedy coordinate descent (GCD) algorithm to solve $\ell_1$-regularized problems and improve GCD by the two popular strategies: Nesterov's acceleration and stochastic optimization. Firstly, we propose a new rule for greedy selection based on an $\ell_1$-norm square approximation which is nontrivial to solve but convex; then an efficient algorithm called ``SOft ThreshOlding PrOjection (SOTOPO)'' is proposed to exactly solve the $\ell_1$-regularized $\ell_1$-norm square approximation problem, which is induced by the new rule. Based on the new rule and the SOTOPO algorithm, the Nesterov's acceleration and stochastic optimization strategies are then successfully applied to the GCD algorithm. The resulted algorithm called accelerated stochastic greedy coordinate descent (ASGCD) has the optimal convergence rate $O(\sqrt{1/\epsilon})$; meanwhile, it reduces the iteration complexity of greedy selection up to a factor of sample size. Both theoretically and empirically, we show that ASGCD has better performance for high-dimensional and dense problems with sparse solution.

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AAAI Conference 2017 Conference Paper

Latent Dependency Forest Models

  • Shanbo Chu
  • Yong Jiang
  • Kewei Tu

Probabilistic modeling is one of the foundations of modern machine learning and artificial intelligence. In this paper, we propose a novel type of probabilistic models named latent dependency forest models (LDFMs). A LDFM models the dependencies between random variables with a forest structure that can change dynamically based on the variable values. It is therefore capable of modeling context-specific independence. We parameterize a LDFM using a first-order non-projective dependency grammar. Learning LDFMs from data can be formulated purely as a parameter learning problem, and hence the difficult problem of model structure learning is circumvented. Our experimental results show that LDFMs are competitive with existing probabilistic models.

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IROS Conference 2013 Conference Paper

Development of a wall-climbing robot with biped-wheel hybrid locomotion mechanism

  • Weiguang Dong
  • Hongguang Wang
  • Zhenhui Li
  • Yong Jiang
  • Jizhong Xiao

This paper presents a wall-climbing robot for reconnaissance in anti-hijacking application. A novel biped-wheel hybrid locomotion mechanism is proposed, which is composed of a planetary gear train, a vacuum adhesion module and a negative pressure adhesion module. The bipedal, wheeled and hybrid locomotion modes are analyzed respectively. A prototype of the wall-climbing robot with compact size and low power consumption has been developed and a lot of performance tests have been conducted. The experimental results demonstrate that the wall-climbing robot has such characteristics as fast moving speed, excellent surface adaptability and obstacle negotiation capability.

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AIIM Journal 2010 Journal Article

A knowledge-driven approach to biomedical document conceptualization

  • Hai-Tao Zheng
  • Charles Borchert
  • Yong Jiang

Objective Biomedical document conceptualization is the process of clustering biomedical documents based on ontology-represented domain knowledge. The result of this process is the representation of the biomedical documents by a set of key concepts and their relationships. Most of clustering methods cluster documents based on invariant domain knowledge. The objective of this work is to develop an effective method to cluster biomedical documents based on various user-specified ontologies, so that users can exploit the concept structures of documents more effectively. Methods We develop a flexible framework to allow users to specify the knowledge bases, in the form of ontologies. Based on the user-specified ontologies, we develop a key concept induction algorithm, which uses latent semantic analysis to identify key concepts and cluster documents. A corpus-related ontology generation algorithm is developed to generate the concept structures of documents. Results Based on two biomedical datasets, we evaluate the proposed method and five other clustering algorithms. The clustering results of the proposed method outperform the five other algorithms, in terms of key concept identification. With respect to the first biomedical dataset, our method has the F-measure values 0. 7294 and 0. 5294 based on the MeSH ontology and gene ontology (GO), respectively. With respect to the second biomedical dataset, our method has the F-measure values 0. 6751 and 0. 6746 based on the MeSH ontology and GO, respectively. Both results outperforms the five other algorithms in terms of F-measure. Based on the MeSH ontology and GO, the generated corpus-related ontologies show informative conceptual structures. Conclusions The proposed method enables users to specify the domain knowledge to exploit the conceptual structures of biomedical document collections. In addition, the proposed method is able to extract the key concepts and cluster the documents with a relatively high precision.

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IROS Conference 2010 Conference Paper

Development of an inspection robot for 500 kV EHV power transmission lines

  • Hongguang Wang
  • Fei Zhang 0002
  • Yong Jiang
  • Guangjun Liu
  • Xiaojie Peng

The developments in mobile robotics have increasingly played an important role in the inspection and maintenance work of Power Transmission Lines (PTLs). This paper presents the research and development of the inspection robots for 500kV PTLs in Shenyang Institute of Automation, Chinese Academy of Sciences (SIACAS). An overview of the research work and the development of an inspection robot AApe-B, are introduced, respectively. The AApe-B can run on the Overhead Ground Wires (OGWs) and navigate different types of obstacles in remote and locally autonomous control mode. A novel dual-wheel-arm hybrid mechanism with the excellent locomotion and obstacle-navigation performance is designed, and obstacle navigation process is analyzed. An embedded control system which can withstand the strong electromagnetic interference has been developed. A lot of field experiments have been carried out, and the experimental results have shown that the inspection robot can reliably work during the 500kV Extra High Voltage (EHV) electromagnetic environment and possess the primary ability to implement the inspection task of the 500kV PTLs.

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IROS Conference 2006 Conference Paper

A Novel Approach to Fault Detection and Identification in Suction Foot Control of a Climbing Robot

  • Yong Jiang
  • Hongguang Wang
  • Lijin Fang
  • Mingyang Zhao

This paper presents a multiple-model and Boolean logic reasoning (MMBLR) approach to detect and identify faults in the suction foot control of a climbing robot. For this control system, some fault models are easily given by kinematics equations. Moreover, the logic relations of the system states have been known in advance. Based on the combination of the multiple-model adaptive estimation (MMAE) algorithm and the Boolean logic reasoning, the MMBLR approach is properly fit for the fault detection and identification (FDI) application to the climbing robot. In the MMBLR architecture, the MMAE algorithm is used to reliably detect and identify the model-known faults. Then based on the robot's states and the results of the MMAE, other faults are detected and identified using the Boolean logic reasoning. Experimental results validated that the faults of the sensors and actuators in the suction foot control of the robot can be readily detected and identified by the MMBLR approach

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