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Chen Luo

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10 papers

2 author rows

TMLR Journal 2026 Journal Article

DiffKGW: Stealthy and Robust Diffusion Model Watermarking

Tianxin Wei
Ruizhong Qiu
Yifan Chen
Yunzhe Qi
Jiacheng Lin
Wenxuan Bao
Wenju Xu
Sreyashi Nag

Diffusion models are known for their supreme capability to generate realistic images. However, ethical concerns, such as copyright protection and the generation of inappropriate content, pose significant challenges for the practical deployment of diffusion models. Recent work has proposed a flurry of watermarking techniques that inject artificial patterns into initial latent representations of diffusion models, offering a promising solution to these issues. However, enforcing a specific pattern on selected elements can disrupt the Gaussian distribution of the initial latent representation. Inspired by watermarks for large language models (LLMs), we generalize the LLM KGW watermark to image diffusion models and propose a stealthy probability adjustment approach DiffKGW that preserves the Gaussian distribution of initial latent representation. In addition, we dissect the design principles of state-of-the-art watermarking techniques and introduce a unified framework. We identify a set of dimensions that explain the manipulation enforced by watermarking methods, including the distribution of individual elements, the specification of watermark shapes within each channel, and the choice of channels for watermark embedding. Through the empirical studies on regular text-to-image applications and the first systematic attempt at watermarking image-to-image diffusion models, we thoroughly verify the effectiveness of our proposed framework through comprehensive evaluations. On all the diffusion models, including Stable Diffusion, our approach induced from the proposed framework not only preserves image quality but also outperforms existing methods in robustness against a wide range of attacks.

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NeurIPS Conference 2025 Conference Paper

AgentTTS: Large Language Model Agent for Test-time Compute-optimal Scaling Strategy in Complex Tasks

Fali Wang
Hui Liu
Zhenwei Dai
Jingying Zeng
Zhiwei Zhang
Zongyu Wu
Chen Luo
Zhen Li

Test-time scaling (TTS) enhances the performance of large language models (LLMs) by allocating additional compute resources during inference. However, existing research primarily investigates TTS in single-stage tasks; while many real-world problems are multi-stage complex tasks, composed of a sequence of heterogeneous subtasks with each subtask requires LLM of specific capability. Therefore, we study a novel problem: the test-time compute-optimal scaling in multi-stage complex tasks, aiming to select suitable models and allocate budgets per subtask to maximize overall performance. TTS in multi-stage tasks introduces two fundamental challenges: (i) The combinatorial search space of model and budget allocations, combined with the high cost of inference, makes brute-force search impractical. (ii) The optimal model and budget allocations across subtasks are interdependent, increasing the complexity of the compute-optimal search. To address this gap, we conduct extensive pilot experiments on four tasks across six datasets, deriving three empirical insights characterizing the behavior of LLMs in multi-stage complex tasks. Informed by these insights, we propose AgentTTS, an LLM-agent-based framework that autonomously searches for compute-optimal allocations through iterative feedback-driven interactions with the execution environment. Experimental results demonstrate that AgentTTS significantly outperforms traditional and other LLM-based baselines in search efficiency, and shows improved robustness to varying training set sizes and enhanced interpretability.

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ECAI Conference 2025 Conference Paper

DetTrack: Realizing Strong Identity Preservation in Multi-Object Tracking via exploration of Detection Information

Yi Zhang
Yi Su
Chen Luo

Multiple Object Tracking (MOT) aims to detect all objects in the scene and associate them across frames with unique ID. Within tracking-by-detection (TBD) paradigm, the confidence based two-stage matching scheme has become popular in MOT. However, when two detections are matched to the same trajectory, the one with higher confidence score usually takes precedence over the lower one, even if the lower one is the ground-truth, causing ID switches (IDS). Considering this, we propose a tailored filtering mechanism to handle the low-confident detections in a more reasonable way. Besides, we introduce a novel fusion scheme for appearance and motion information based on appearance clarity and localization accuracy of the detection boxes. Finally, an adaptive management of unmatched detections scheme is proposed to reduce the occurrence of IDS and duplicate trajectories. Extensive experiments have been conducted on MOT17 and MOT20, in which our tracker exhibits stronger identity preservation capabilities against other competitors.

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EAAI Journal 2025 Journal Article

Photovoltaic system modeling and forecasting techniques: A survey

Chen Luo
Naji Al-Messabi
Zhaoqi Kuang
Changjiang Ma
Ibrahim El-Amin
Hui Deng
Yun Li

Modeling provides engineering science a vital technique in the third paradigm of science. This paper reviews a series of modeling techniques for forecasting solar energy yields of photovoltaic (PV) systems, with comparisons among various aspects of solar photovoltaic forecasting, including forecasting techniques, irradiance models, and PV forecasting software and services. Efficient integration of renewable energy sources, in particular PV systems, to the power gird complement fossil fueled generation in reducing emissions. A pivotal step in this integration is to predict the PV outputs, so as to account for their dynamic energy contribution in generation planning and unit commitment programs. Existing research has mostly been on forecasting solar irradiance figures from a very short term (minutes/hours) to a short term (days/weeks) horizon. The paper highlights the use of advanced intelligent modeling techniques. The results serve as a practical guide to renewable energy researchers and engineers for best available approaches in handling photovoltaic forecasting and renewable power generation.

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NeurIPS Conference 2023 Conference Paper

Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation

Wei Jin
Haitao Mao
Zheng Li
Haoming Jiang
Chen Luo
Hongzhi Wen
Haoyu Han
Hanqing Lu

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences is essential for providing personalized recommendations. Session-based recommendation, which utilizes customer session data to predict their next interaction, has become increasingly popular. However, existing session datasets have limitations in terms of item attributes, user diversity, and dataset scale. As a result, they cannot comprehensively capture the spectrum of user behaviors and preferences. To bridge this gap, we present the Amazon Multilingual Multi-locale Shopping Session Dataset, namely Amazon-M2. It is the first multilingual dataset consisting of millions of user sessions from six different locales, where the major languages of products are English, German, Japanese, French, Italian, and Spanish. Remarkably, the dataset can help us enhance personalization and understanding of user preferences, which can benefit various existing tasks as well as enable new tasks. To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation. With the above tasks, we benchmark a range of algorithms on our proposed dataset, drawing new insights for further research and practice. In addition, based on the proposed dataset and tasks, we hosted a competition in the KDD CUP 2023 https: //www. aicrowd. com/challenges/amazon-kdd-cup-23-multilingual-recommendation-challenge and have attracted thousands of users and submissions. The winning solutions and the associated workshop can be accessed at our website~https: //kddcup23. github. io/.

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NeurIPS Conference 2023 Conference Paper

Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints

Jiaxin Bai
Xin Liu
Weiqi Wang
Chen Luo
Yangqiu Song

Querying knowledge graphs (KGs) using deep learning approaches can naturally leverage the reasoning and generalization ability to learn to infer better answers. Traditional neural complex query answering (CQA) approaches mostly work on entity-centric KGs. However, in the real world, we also need to make logical inferences about events, states, and activities (i. e. , eventualities or situations) to push learning systems from System I to System II, as proposed by Yoshua Bengio. Querying logically from an EVentuality-centric KG (EVKG) can naturally provide references to such kind of intuitive and logical inference. Thus, in this paper, we propose a new framework to leverage neural methods to answer complex logical queries based on an EVKG, which can satisfy not only traditional first-order logic constraints but also implicit logical constraints over eventualities concerning their occurrences and orders. For instance, if we know that Food is bad happens before PersonX adds soy sauce, then PersonX adds soy sauce is unlikely to be the cause of Food is bad due to implicit temporal constraint. To facilitate consistent reasoning on EVKGs, we propose Complex Eventuality Query Answering (CEQA), a more rigorous definition of CQA that considers the implicit logical constraints governing the temporal order and occurrence of eventualities. In this manner, we propose to leverage theorem provers for constructing benchmark datasets to ensure the answers satisfy implicit logical constraints. We also propose a Memory-Enhanced Query Encoding (MEQE) approach to significantly improve the performance of state-of-the-art neural query encoders on the CEQA task.

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ECAI Conference 2023 Conference Paper

Multi-Agent Cooperative Games Using Belief Map Assisted Training

Chen Luo
Qinwei Huang
Alex B. Wu
Simon Khan
Hai Li 0001
Qinru Qiu

In a multi-agent system, agents share their local observations to gain global situational awareness for decision making and collaboration using a message passing system. When to send a message, how to encode a message, and how to leverage the received messages directly affect the effectiveness of the collaboration among agents. When training a multi-agent cooperative game using reinforcement learning (RL), the message passing system needs to be optimized together with the agent policies. This consequently increases the model’s complexity and poses significant challenges to the convergence and performance of learning. To address this issue, we propose the Belief-map Assisted Multi-agent System (BAMS), which leverages a neuro-symbolic belief map to enhance training. The belief map decodes the agent’s hidden state to provide a symbolic representation of the agent’s understanding of the environment and other agents’ status. The simplicity of symbolic representation allows the gathering and comparison of the ground truth information with the belief, which provides an additional channel of feedback for the learning. Compared to the sporadic and delayed feedback coming from the reward in RL, the feedback from the belief map is more consistent and reliable. Agents using BAMS can learn a more effective message passing network to better understand each other, resulting in better performance in the game. We evaluate BAMS’s performance in a cooperative predator and prey game with varying levels of map complexity and compare it to previous multi-agent message passing models. The simulation results showed that BAMS reduced training epochs by 66%, and agents who apply the BAMS model completed the game with 34. 62% fewer steps on average.

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AAAI Conference 2019 Conference Paper

Scaling-Up Split-Merge MCMC with Locality Sensitive Sampling (LSS)

Chen Luo
Anshumali Shrivastava

Split-Merge MCMC (Monte Carlo Markov Chain) is one of the essential and popular variants of MCMC for problems when an MCMC state consists of an unknown number of components. It is well known that state-of-the-art methods for split-merge MCMC do not scale well. Strategies for rapid mixing requires smart and informative proposals to reduce the rejection rate. However, all known smart proposals involve expensive operations to suggest informative transitions. As a result, the cost of each iteration is prohibitive for massive scale datasets. It is further known that uninformative but computationally efficient proposals, such as random split-merge, leads to extremely slow convergence. This tradeoff between mixing time and per update cost seems hard to get around. We leverage some unique properties of weighted MinHash, which is a popular LSH, to design a novel class of split-merge proposals which are significantly more informative than random sampling but at the same time efficient to compute. Overall, we obtain a superior tradeoff between convergence and per update cost. As a direct consequence, our proposals are around 6X faster than the state-of-the-art sampling methods on two large real datasets KDDCUP and PubMed with several millions of entities and thousands of clusters.

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IJCAI Conference 2017 Conference Paper

Variational Mixtures of Gaussian Processes for Classification

Chen Luo
Shiliang Sun

Gaussian Processes (GPs) are powerful tools for machine learning which have been applied to both classification and regression. The mixture models of GPs were later proposed to further improve GPs for data modeling. However, these models are formulated for regression problems. In this work, we propose a new Mixture of Gaussian Processes for Classification (MGPC). Instead of the Gaussian likelihood for regression, MGPC employs the logistic function as likelihood to obtain the class probabilities, which is suitable for classification problems. The posterior distribution of latent variables is approximated through variational inference. The hyperparameters are optimized through the variational EM method and a greedy algorithm. Experiments are performed on multiple real-world datasets which show improvements over five widely used methods on predictive performance. The results also indicate that for classification MGPC is significantly better than the regression model with mixtures of GPs, different from the existing consensus that their single model counterparts are comparable.

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TIST Journal 2016 Journal Article

Telco User Activity Level Prediction with Massive Mobile Broadband Data

Chen Luo
Jia Zeng
Mingxuan Yuan
Wenyuan Dai
Qiang Yang

Telecommunication (telco) operators aim to provide users with optimized services and bandwidth in a timely manner. The goal is to increase user experience while retaining profit. To do this, knowing the changing behavior patterns of users through their activity levels in advance can be a great help for operators to adjust their management strategies and reduce operational risk. To achieve this goal, the operators can make use of knowledge discovered from telco’s historical mobile broadband (MBB) records to predict mobile access activity level at an early stage. In this article, we report our research in a real-world telco setting involving more than one million telco users. Our novel contribution includes representing users as documents containing a collection of changing spatiotemporal “words” that express user behavior. By extracting users’ space-time access records in MBB data, we use latent Dirichlet allocation (LDA) to learn user-specific compact topic features for user activity level prediction. We propose a scalable online expectation-maximization (OEM) algorithm that can scale LDA to massive MBB data, which is significantly faster than several state-of-the-art online LDA algorithms. Using these real-world MBB data, we confirm high performance in user activity level prediction. In addition, we show that the inferred topics indicate that future activity level anomalies correlate highly with early skewed bandwidth supply and demand relations. Thus, our prediction system can also guide the telco operators to balance the telecommunication network in terms of supply-demand relations, saving deployment costs and energy of cell towers in the future.

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