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Han Xiao

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21 papers

2 author rows

TMLR Journal 2026 Journal Article

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

Huan-ang Gao
Jiayi Geng
Wenyue Hua
Mengkang Hu
Xinzhe Juan
Hongzhang Liu
Shilong Liu
Jiahao Qiu

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks but remain fundamentally static, unable to adapt their internal parameters to novel tasks, evolving knowledge domains, or dynamic interaction contexts. As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck, necessitating agents that can adaptively reason, act, and evolve in real time. This paradigm shift ---from scaling static models to developing self-evolving agents --- has sparked growing interest in architectures and methods enabling continual learning and adaptation from data, interactions, and experiences. This survey provides the first systematic and comprehensive review of self-evolving agents, organizing the field around three foundational dimensions --- what to evolve, when to evolve, and how to evolve. We examine evolutionary mechanisms across agent components (e.g., models, memory, tools, architecture), categorize adaptation methods by stages (e.g., intra-test-time, inter-test-time), and analyze the algorithmic and architectural designs that guide evolutionary adaptation (e.g., scalar rewards, textual feedback, single-agent and multi-agent systems). Additionally, we analyze evaluation metrics and benchmarks tailored for self-evolving agents, highlight applications in domains such as coding, education, and healthcare, and identify critical challenges and research directions in safety, scalability, and co-evolutionary dynamics. By providing a structured framework for understanding and designing self-evolving agents, this survey establishes a roadmap for advancing more adaptive, capable, robust, and versatile agentic systems in both research and real-world deployments, and ultimately sheds light on the realization of Artificial Super Intelligence (ASI) where agents evolve autonomously and perform beyond human-level intelligence across a wide array of tasks.

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AAAI Conference 2026 Conference Paper

HMformer: Unleashing Transformer’s Potential for Time Series Forecasting via Hierarchical Multi-Scale Modeling

Renjun Huang
Han Xiao
Bingqing Li
Baili Zhang
Jianhua Lyu

Time series forecasting plays a critical role across a wide range of domains. Recently, an increasing number of Transformer-based forecasting models have emerged, achieving remarkably competitive performance. However, real-world time series data often exhibit complex multi-scale periodicities, which are not well-suited for modeling by the original Transformer architecture originally developed for NLP tasks. To address this limitation, we propose the Hierarchical Multi-scale Time Series Transformer (HMformer), employing a novel and sophisticated framework specifically designed for multi-scale time series forecasting. Specifically, HMformer incorporates a hierarchical cross-scale mixing mechanism that progressively aggregates temporal information from fine to coarse granularities, a scale-adaptive feature expansion design enhancing the extraction of high-level temporal semantics, and a multi-branch complementary prediction strategy for effectively integrating diverse temporal patterns. Collectively, these components enable HMformer to capture intricate, multi-scale temporal dynamics while retaining the Transformer’s inherent strength in modeling long-range dependencies. Extensive experiments conducted on multiple real-world benchmark datasets—encompassing both long-term and short-term forecasting tasks—demonstrate that HMformer achieves state-of-the-art performance.

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AAAI Conference 2026 Conference Paper

UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning

Zhengxi Lu
Yuxiang Chai
Yaxuan Guo
Xi Yin
Liang Liu
Hao Wang
Han Xiao
Shuai Ren

The recent DeepSeek-R1 has showcased the emergence of reasoning capabilities in large language models (LLMs) through reinforcement learning (RL) with rule-based rewards. Despite its success in language tasks, its application in multimodal domains, particularly in graphic user interface (GUI) agent tasks, remains under-explored. To address this gap, we propose UI-R1, the first framework to investigate how rule-based RL can enhance the reasoning capabilities of multimodal large language models (MLLMs) for GUI action prediction tasks. UI-R1 introduces a novel rule-based action reward scheme, enabling model optimization via policy-based algorithms such as Group Relative Policy Optimization (GRPO). To further improve efficiency at inference time, we present UI-R1-Efficient, a two-stage training paradigm that reduces reasoning length while boosting overall performance. In addition, we construct a compact yet high-quality dataset containing 2K challenging tasks across five prevalent mobile device action types. Experiments show that our proposed models (e.g., UI-R1-3B) achieve substantial improvements over the base model (Qwen2.5-VL-3B) on both in-domain (ID) and out-of-domain (OOD) tasks, with average accuracy gains of 18.3% on ScreenSpot, 6.0% on ScreenSpot-Pro, and 10.9% on ANDROIDCONTROL. Moreover, our efficient versions deliver competitive performance compared to considerably larger state-of-the-art models, underscoring the potential of reinforcement learning to advance GUI control and paving the way for future research in Human-Computer Interaction (HCI).

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JBHI Journal 2025 Journal Article

Interpretable End to End Epileptic Seizure Detection via Linear and Nonlinear Filtering Networks

Jie Wang
Xianlei Zeng
Yingchao Wang
Jie Xu
Defu Zhai
Han Xiao
Weiwei Nie
Qi Yuan

Epilepsy is a prevalent neurological disorder marked recurrent, unpredictable seizures. Electroencephalogram (EEG)-based seizure detection has become a key focus in clinical research due to its potential for identifying abnormal brain activity patterns. However, most current approaches rely on single-modal feature analysis and struggle to disentangle the complex linear and nonlinear dynamics of EEG signals, limiting their clinical utility. To address this limitation, we propose a novel contrastive learning framework with linear and nonlinear filtering networks (CL LNFNet) for interpretable seizure detection. CL-LNFNet enhances explainability by tracing the full decision-making pathway from raw EEG signals to diagnostic outcomes. Through comparative analysis of feature evolution across six seizure types and non-seizure states, the model bridges the gap between the “black-box” nature of deep learning and the transparency required in clinical diagnostics. The framework first employs a recursive residual decomposition scheme to extract linear and nonlinear components using dual-branch decoupling networks. These features are then refined via two adaptive filtering networks equipped with feature selection gating mechanisms. A multi-scale convolutional module within a three-layer convolutional architecture hierarchically integrates the dual-stream outputs to improve classification performance. Furthermore, we introduce a hybrid learning strategy that combines supervised and self-supervised contrastive learning to enhance feature representation through the joint optimization of both loss functions. Experimental evaluations on both scalp and intracranial EEG datasets demonstrate that CL-LNFNet achieves over 95% accuracy in both cross-patient and specific patient scenarios, outperforming existing state-of-the-art methods. The code is available at https://github.com/JW Image/CL-LNFNet.

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TMLR Journal 2025 Journal Article

LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects

Guangyi Liu
Pengxiang Zhao
Yaozhen Liang
Liang Liu
Yaxuan Guo
Han Xiao
Weifeng Lin
Yuxiang Chai

With the rapid rise of large language models (LLMs), phone automation has undergone transformative changes. This paper systematically reviews LLM-driven phone GUI agents, highlighting their evolution from script-based automation to intelligent, adaptive systems. We first contextualize key challenges, (i) limited generality, (ii) high maintenance overhead, and (iii) weak intent comprehension, and show how LLMs address these issues through advanced language understanding, multimodal perception, and robust decision-making. We then propose a taxonomy covering fundamental agent frameworks (single-agent, multi-agent, plan-then-act), modeling approaches (prompt engineering, training-based), and essential datasets and benchmarks. Furthermore, we detail task-specific architectures, supervised fine-tuning, and reinforcement learning strategies that bridge user intent and GUI operations. Finally, we discuss open challenges such as dataset diversity, on-device deployment efficiency, user-centric adaptation, and security concerns, offering forward-looking insights into this rapidly evolving field. By providing a structured overview and identifying pressing research gaps, this paper serves as a definitive reference for researchers and practitioners seeking to harness LLMs in designing scalable, user-friendly phone GUI agents. The collection of papers reviewed in this survey will be hosted and regularly updated on the GitHub repository: \url{https://github.com/PhoneLLM/Awesome-LLM-Powered-Phone-GUI-Agents}

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IJCAI Conference 2025 Conference Paper

NuMDS: An Efficient Local Search Algorithm for Minimum Dominating Set Problem

Rui Sun
Zhaohui Liu
Yiyuan Wang
Han Xiao
Jiangnan Li
Jiejiang Chen

The minimum dominating set (MDS) problem is a crucial NP-hard combinatorial optimization problem with wide applications in real-world scenarios. In this paper, we propose an efficient local search algorithm namely NuMDS to solve the MDS, which comprises three key ideas. First, we introduce a dominate propagation-based reduction method that fixes a portion of vertices in a given graph. Second, we develop a novel two-phase initialization method based on the decomposition method. Third, we propose a multi-stage local search procedure, which adopts three different search manners according to the current stage of the search. We conduct extensive experiments to demonstrate the outstanding effectiveness of NuMDS, and the results clearly indicate that NuMDS outperforms previous state-of-the-art algorithms on almost all instances.

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NeurIPS Conference 2025 Conference Paper

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents

Han Xiao
Guozhi Wang
Yuxiang Chai
Zimu Lu
Weifeng Lin
Hao He
Lue Fan
Liuyang Bian

In this paper, we introduce UI-Genie, a self-improving framework addressing two key challenges in GUI agents: verification of trajectory outcome is challenging and high-quality training data are not scalable. These challenges are addressed by a reward model and a self-improving pipeline, respectively. The reward model, UI-Genie-RM, features an image-text interleaved architecture that efficiently processes historical context and unifies action-level and task-level rewards. To support the training of UI-Genie-RM, we develop deliberately-designed data generation strategies including rule-based verification, controlled trajectory corruption, and hard negative mining. To address the second challenge, a self-improvement pipeline progressively expands solvable complex GUI tasks by enhancing both the agent and reward models through reward-guided exploration and outcome verification in dynamic environments. For training the model, we generate UI-Genie-RM-517k and UI-Genie-Agent-16k, establishing the first reward-specific dataset for GUI agents while demonstrating high-quality synthetic trajectory generation without manual annotation. Experimental results show that UI-Genie achieves state-of-the-art performance across multiple GUI agent benchmarks with three generations of data-model self-improvement. We open-source our complete framework implementation and generated datasets to facilitate further research in https: //github. com/Euphoria16/UI-Genie.

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NeurIPS Conference 2025 Conference Paper

WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch

Zimu Lu
Yunqiao Yang
Houxing Ren
Haotian Hou
Han Xiao
Ke Wang
Weikang Shi
Aojun Zhou

LLM‑based agents have demonstrated great potential in generating and managing code within complex codebases. In this paper, we introduce WebGen-Bench, a novel benchmark designed to measure an LLM-based agent's ability to create multi-file website codebases from scratch. It contains diverse instructions for website generation, created through the combined efforts of human annotators and GPT-4o. These instructions span three major categories and thirteen minor categories, encompassing nearly all important types of web applications. To assess the quality of the generated websites, we generate test cases targeting each functionality described in the instructions. These test cases are then manually filtered, refined, and organized to ensure accuracy, resulting in a total of 647 test cases. Each test case specifies an operation to be performed on the website and the expected outcome of the operation. To automate testing and improve reproducibility, we employ a powerful web-navigation agent to execute test cases on the generated websites and determine whether the observed responses align with the expected results. We evaluate three high-performance code-agent frameworks—Bolt. diy, OpenHands, and Aider—using multiple proprietary and open-source LLMs as engines. The best-performing combination, Bolt. diy powered by DeepSeek-R1, achieves only 27. 8\% accuracy on the test cases, highlighting the challenging nature of our benchmark. Additionally, we construct WebGen-Instruct, a training set consisting of 6, 667 website-generation instructions. Training Qwen2. 5-Coder-32B-Instruct on Bolt. diy trajectories generated from a subset of the training set achieves an accuracy of 38. 2\%, surpassing the performance of the best proprietary model. We release our data-generation, training, and testing code, along with both the datasets and model weights at https: //github. com/mnluzimu/WebGen-Bench.

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TCS Journal 2024 Journal Article

Approximate core allocations for edge cover games

Tianhang Lu
Han Xiao
Qizhi Fang

Edge cover games are cooperative cost games arising from edge cover problems. In an edge cover game, each player controls a vertex and the cost of a coalition is the minimum weight of edge covers in the subgraph induced by the coalition. The approximate core is a relaxation of the core which is one of the most important concepts in cooperative game theory. A vector belongs to the α-core ( 0 ≤ α ≤ 1 ) if it recovers at least α-fraction of the total cost of the game when no deviating coalition is better off. In this paper, we show that the 3 4 -core of edge cover games is always non-empty and a vector in the 3 4 -core can be computed efficiently. We also show that 3 4 is the best constant ratio for the approximate core of edge cover games, as it is the reciprocal of the integrality gap for edge cover problems.

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IJCAI Conference 2024 Conference Paper

Dual Enhancement in ODI Super-Resolution: Adapting Convolution and Upsampling to Projection Distortion

Xiang Ji
Changqiao Xu
Lujie Zhong
Shujie Yang
Han Xiao
Gabriel-Miro Muntean

Omnidirectional images (ODIs) demand considerably higher resolution to ensure high quality across all viewports. Traditional convolutional neural networks (CNN)-based single-image super-resolution (SISR) networks, however, are not effective for spherical ODIs. This is due to the uneven pixel density distribution and varying texture complexity in different regions that arise when projecting from a sphere to a plane. Additionally, the computational and memory costs associated with large-sized ODIs present a challenge for real-world application. To address these issues, we propose an efficient distortion-adaptive super-resolution network (ODA-SRN). Specifically, ODA-SRN employs a series of specially designed Distortion Attention Block Groups (DABG) as its backbone. Our Distortion Attention Blocks (DABs) utilize multi-segment parameterized convolution to generate dynamic filters, which compensate for distortion and texture fading during feature extraction. Moreover, we introduce an upsampling scheme that accounts for the dependence of pixel position and distortion degree to achieve pixel-level distortion offset. A comprehensive set of results demonstrates that our ODA-SRN significantly improves the super-resolution performance for ODIs, both quantitatively and qualitatively, when compared to other state-of-the-art methods.

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AAAI Conference 2024 Conference Paper

G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks

Anchun Gui
Jinqiang Ye
Han Xiao

It has become a popular paradigm to transfer the knowledge of large-scale pre-trained models to various downstream tasks via fine-tuning the entire model parameters. However, with the growth of model scale and the rising number of downstream tasks, this paradigm inevitably meets the challenges in terms of computation consumption and memory footprint issues. Recently, Parameter-Efficient Fine-Tuning (PEFT) (e.g., Adapter, LoRA, BitFit) shows a promising paradigm to alleviate these concerns by updating only a portion of parameters. Despite these PEFTs having demonstrated satisfactory performance in natural language processing, it remains under-explored for the question: whether these techniques could be transferred to graph-based tasks with Graph Transformer Networks (GTNs)? Therefore, in this paper, we fill this gap by providing extensive benchmarks with traditional PEFTs on a range of graph-based downstream tasks. Our empirical study shows that it is sub-optimal to directly transfer existing PEFTs to graph-based tasks due to the issue of feature distribution shift. To address this issue, we propose a novel structure-aware PEFT approach, named G-Adapter, which leverages graph convolution operation to introduce graph structure information (e.g., graph adjacency matrix) as an inductive bias to guide the updating process. Further, we propose Bregman proximal point optimization to alleviate feature distribution shift by preventing the model from aggressive update. Extensive experiments demonstrate that G-Adapter obtains state-of-the-art performance compared to counterparts on nine graph benchmark datasets based on diverse pre-trained GTNs, and delivers tremendous memory footprint efficiency compared to the conventional paradigm.

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NeurIPS Conference 2024 Conference Paper

Lumina-Next : Making Lumina-T2X Stronger and Faster with Next-DiT

Le Zhuo
Ruoyi Du
Han Xiao
Yangguang Li
Dongyang Liu
Rongjie Huang
Wenze Liu
Xiangyang Zhu

Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers (Flag-DiT) that establishes a unified framework for transforming noise into various modalities, such as images and videos, conditioned on text instructions. Despite its promising capabilities, Lumina-T2X still encounters challenges including training instability, slow inference, and extrapolation artifacts. In this paper, we present Lumina-Next, an improved version of Lumina-T2X, showcasing stronger generation performance with increased training and inference efficiency. We begin with a comprehensive analysis of the Flag-DiT architecture and identify several suboptimal components, which we address by introducing the Next-DiT architecture with 3D RoPE and sandwich normalizations. To enable better resolution extrapolation, we thoroughly compare different context extrapolation methods applied to text-to-image generation with 3D RoPE, and propose Frequency- and Time-Aware Scaled RoPE tailored for diffusion transformers. Additionally, we introduce a sigmoid time discretization schedule for diffusion sampling, which achieves high-quality generation in 5-10 steps combined with higher-order ODE solvers. Thanks to these improvements, Lumina-Next not only improves the basic text-to-image generation but also demonstrates superior resolution extrapolation capabilities as well as multilingual generation using decoder-based LLMs as the text encoder, all in a zero-shot manner. To further validate Lumina-Next as a versatile generative framework, we instantiate it on diverse tasks including visual recognition, multi-views, audio, music, and point cloud generation, showcasing strong performance across these domains. By releasing all codes and model weights at https: //github. com/Alpha-VLLM/Lumina-T2X, we aim to advance the development of next-generation generative AI capable of universal modeling.

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NeurIPS Conference 2024 Conference Paper

Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning

Hao Shao
Shengju Qian
Han Xiao
Guanglu Song
Zhuofan Zong
Letian Wang
Yu Liu
Hongsheng Li

Multi-Modal Large Language Models (MLLMs) have demonstrated impressive performance in various VQA tasks. However, they often lack interpretability and struggle with complex visual inputs, especially when the resolution of the input image is high or when the interested region that could provide key information for answering the question is small. To address these challenges, we collect and introduce the large-scale Visual CoT dataset comprising 438k question-answer pairs, annotated with intermediate bounding boxes highlighting key regions essential for answering the questions. Additionally, about 98k pairs of them are annotated with detailed reasoning steps. Importantly, we propose a multi-turn processing pipeline that dynamically focuses on visual inputs and provides interpretable thoughts. We also introduce the related benchmark to evaluate the MLLMs in scenarios requiring specific local region identification. Extensive experiments demonstrate the effectiveness of our framework and shed light on better inference strategies. The Visual CoT dataset, benchmark, and pre-trained models are available on this website to support further research in this area.

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JMLR Journal 2022 Journal Article

KoPA: Automated Kronecker Product Approximation

Chencheng Cai
Rong Chen
Han Xiao

We consider the problem of matrix approximation and denoising induced by the Kronecker product decomposition. Specifically, we propose to approximate a given matrix by the sum of a few Kronecker products of matrices, which we refer to as the Kronecker product approximation (KoPA). Because the Kronecker product is an extensions of the outer product from vectors to matrices, KoPA extends the low rank matrix approximation, and includes it as a special case. Comparing with the latter, KoPA also offers a greater flexibility, since it allows the user to choose the configuration, which are the dimensions of the two smaller matrices forming the Kronecker product. On the other hand, the configuration to be used is usually unknown, and needs to be determined from the data in order to achieve the optimal balance between accuracy and parsimony. We propose to use extended information criteria to select the configuration. Under the paradigm of high dimensional analysis, we show that the proposed procedure is able to select the true configuration with probability tending to one, under suitable conditions on the signal-to-noise ratio. We demonstrate the superiority of KoPA over the low rank approximations through numerical studies, and several benchmark image examples. [abs] [ pdf ][ bib ] &copy JMLR 2022. ( edit, beta )

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TCS Journal 2020 Journal Article

Population monotonic allocation schemes for vertex cover games

Han Xiao
Qizhi Fang
Ding-Zhu Du

For vertex cover games (introduced by Deng et al. (1999) [2]), we investigate population monotonic allocation schemes (introduced by Sprumont (1990) [11]). We show that the existence of a population monotonic allocation scheme (PMAS for short) for vertex cover games can be determined efficiently and that a PMAS, if exists, can be constructed accordingly. We also show that integral PMAS-es for vertex cover games can be characterized with stable matchings and be enumerated by employing Gale-Shapley algorithm (introduced by Gale and Shapley (1962) [4]).

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AAAI Conference 2017 Conference Paper

SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions

Han Xiao
Minlie Huang
Lian Meng
Xiaoyan Zhu

Knowledge graph embedding represents entities and relations in knowledge graph as low-dimensional, continuous vectors, and thus enables knowledge graph compatible with machine learning models. Though there have been a variety of models for knowledge graph embedding, most methods merely concentrate on the fact triples, while supplementary textual descriptions of entities and relations have not been fully employed. To this end, this paper proposes the semantic space projection (SSP) model which jointly learns from the symbolic triples and textual descriptions. Our model builds interaction between the two information sources, and employs textual descriptions to discover semantic relevance and offer precise semantic embedding. Extensive experiments show that our method achieves substantial improvements against baselines on the tasks of knowledge graph completion and entity classiﬁcation. Papers, Posters, Slides, Datasets and Codes: http: //www. ibookman. net/conference. html

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IJCAI Conference 2016 Conference Paper

From One Point to a Manifold: Knowledge Graph Embedding for Precise Link Prediction

Han Xiao
Minlie Huang
Xiaoyan Zhu

Knowledge graph embedding aims at offering a numerical knowledge representation paradigm by transforming the entities and relations into continuous vector space. However, existing methods could not characterize the knowledge graph in a fine degree to make a precise link prediction. There are two reasons: being an ill-posed algebraic system and adopting an overstrict geometric form. As precise link prediction is critical, we propose a manifold-based embedding principle (ManifoldE) which could be treated as a well-posed algebraic system that expands the position of golden triples from one point in current models to a manifold in ours. Extensive experiments show that the proposed models achieve substantial improvements against the state-of-the-art baselines especially for the precise prediction task, and yet maintain high efficiency.

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IROS Conference 2016 Conference Paper

Incremental scene understanding on dense SLAM

Chi Li
Han Xiao
Keisuke Tateno
Federico Tombari
Nassir Navab
Gregory D. Hager

We present an architecture for online, incremental scene modeling which combines a SLAM-based scene understanding framework with semantic segmentation and object pose estimation. The core of this approach comprises a probabilistic inference scheme that predicts semantic labels for object hypotheses at each new frame. From these hypotheses, recognized scene structures are incrementally constructed and tracked. Semantic labels are inferred using a multi-domain convolutional architecture which operates on the image time series and which enables efficient propagation of features as well as robust model registration. To evaluate this architecture, we introduce a large-scale RGB-D dataset JHUSEQ-25 as a new benchmark for the sequence-based scene understanding in complex and densely cluttered scenes. This dataset contains 25 RGB-D video sequences with 100, 000 labeled frames in total. We validate our method on this dataset and demonstrate improved performance of semantic segmentation and 6-DoF object pose estimation compared with methods based on the single view.

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EAAI Journal 2013 Journal Article

Hydraulic turbine governing system identification using T–S fuzzy model optimized by chaotic gravitational search algorithm

Chaoshun Li
Jianzhong Zhou
Jian Xiao
Han Xiao

Hydraulic turbine governing system (HTGS) is a complicated nonlinear system that controls the frequency and power output of hydroelectric generating unit (HGU). The modeling of HTGS is an important and difficult task, because some components, like hydraulic turbine and governor actuator, are with strong nonlinearity. In this paper, a novel Takagi–Sugeno (T–S) fuzzy model identification method based on chaotic gravitational search algorithm (CGSA) is proposed and applied in the modeling of HTGS. In the proposed method, fuzzy c-regression model clustering algorithm is used to partition the input space and identify the coarse antecedent membership function (MF) parameters at first. And then, a novel CGSA is proposed to search better MF parameters around the coarse results, in which chaotic search has been embedded in the iteration of basic GSA to search and replace the current best solution of GSA. The performance of the proposed fuzzy model identification method is validated by benchmark problems, and the results show that the accuracies of identified models have been improved significantly compared with the other existing models. Finally, the proposed approach has been applied to approximate the dynamic behaviors of HTGS of a HGU in a hydropower station of Jiangxi Province of China. The experimental results show that our approach can identify the HTGS satisfactorily with acceptable accuracy.

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AAAI Conference 2013 Conference Paper

Lazy Gaussian Process Committee for Real-Time Online Regression

Han Xiao
Claudia Eckert

A significant problem of Gaussian process (GP) is its unfavorable scaling with a large amount of data. To overcome this issue, we present a novel GP approximation scheme for online regression. Our model is based on a combination of multiple GPs with random hyperparameters. The model is trained by incrementally allocating new examples to a selected subset of GPs. The selection is carried out efficiently by optimizing a submodular function. Experiments on real-world data sets showed that our method outperforms existing online GP regression methods in both accuracy and efficiency. The applicability of the proposed method is demonstrated by the mouse-trajectory prediction in an Internet banking scenario.

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EAAI Journal 2011 Journal Article

Parameters identification of nonlinear state space model of synchronous generator

Pangao Kou
Jianzhong Zhou
Changqing Wang
Han Xiao
Huifeng Zhang
Chaoshun Li

Synchronous generator (SG) modeling plays an important role in system planning, operation and post-disturbance analysis. This paper presents an improved algorithm named Particle Swarm Optimization with Quantum Operation (PSO–QO) to solve both offline and online parameters estimation problem for SG. First, the hybrid algorithm is proposed to increase the convergence speed and identification accuracy of the basic Particle Swarm Optimization (PSO). An illustrative example for parameters identification of SG is provided to confirm the validity, as compared with Linearly Decreasing Inertia Weight PSO (LDW-PSO), and the Quantum Particle Swarm Optimization (QPSO) in terms of parameter estimation accuracy and convergence speed. Second, PSO–QO is also improved to detect and determine parameters variation. In this case, a sentry particle is introduced to detect any changes in system parameters. Simulation results confirm that the proposed algorithm is a viable alternative for online parameters detection and parameters identification of SG.

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