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Wei Jiang

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29 papers

2 author rows

AAAI Conference 2026 Conference Paper

BokehFlow: Depth-Free Controllable Bokeh Rendering via Flow Matching

Yachuan Huang
Xianrui Luo
Qiwen Wang
Liao Shen
Jiaqi Li
Huiqiang Sun
Zihao Huang
Wei Jiang

Bokeh rendering simulates the shallow depth-of-field effect in photography, enhancing visual aesthetics and guiding viewer attention to regions of interest. Although recent approaches perform well, rendering controllable bokeh without additional depth inputs remains a significant challenge. Existing classical and neural controllable methods rely on accurate depth maps, while generative approaches often struggle with limited controllability and efficiency. In this paper, we propose BokehFlow, a depth-free framework for controllable bokeh rendering based on flow matching. BokehFlow directly synthesizes photorealistic bokeh effects from all-in-focus images, eliminating the need for depth inputs. It employs a cross-attention mechanism to enable semantic control over both focus regions and blur intensity via text prompts. To support training and evaluation, we collect and synthesize four datasets. Extensive experiments demonstrate that BokehFlow achieves visually compelling bokeh effects and offers precise control, outperforming existing depth-dependent and generative methods in both rendering quality and efficiency.

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EAAI Journal 2026 Journal Article

Bridging heterogeneous state space models and convolutional neural networks for efficient and robust pavement crack segmentation

Jinhuan Shan
Wei Jiang

Efficient and robust crack segmentation is essential for large-scale pavement inspection under resource-constrained deployment scenarios. However, existing lightweight deep learning models often suffer from limited representational capacity, resulting in degraded performance under complex environmental disturbances. To address this limitation, we propose a lightweight segmentation framework (ChampMamba) that integrates convolutional neural networks and visual state space modeling through an interleaved fusion strategy. The proposed architecture incorporates Efficient Champion Bottleneck modules for enhanced local–global feature interaction and a Cross-Layer Enhancement Head for efficient global decoding, achieving a favorable balance between accuracy and computational cost. To further improve deployment efficiency, we develop a pure convolutional student network (ChampMamba-S) trained via heterogeneous knowledge distillation from the visual state space–enhanced teacher model. By introducing Learnable Feature Prompts with spatial and channel attention mechanisms, the student network effectively inherits long-range contextual representations while preserving a fully convolutional inference pipeline. Comprehensive experiments on multiple pavement crack segmentation benchmarks under fifteen standardized image corruption types demonstrate that the proposed artificial intelligence models consistently outperform existing lightweight architectures in terms of segmentation accuracy and robustness. With only 0. 307 million parameters, ChampMamba-S achieves an excellent trade-off among model size, performance, and resilience to severe perturbations, making it well suited for real-time intelligent pavement inspection in complex and variable environments. The source code is available at: https: //github. com/SHAN-JH/ChampMamba.

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EAAI Journal 2026 Journal Article

Dimos: Diffusion model with unified sequential state space for session-based recommendation

Weiyue Li
Ming Gao
Bowei Chen
Jingmin An
Hao Dong
Wei Jiang
Jiafu Tang

Session-based recommendation aims to predict the next item based on the user–item interactions within the current session. Many existing methods adopt discriminative approaches to learn specific preference representations, while few methods introduce generative approaches to learn underlying preference distributions, failing to handle limited and noisy interactions effectively. Moreover, naive implementations of generative models face a trade-off between effectiveness and efficiency, limiting their practical utility. To address these challenges, we propose Dimos, a dual-branch framework comprising an exploring branch and an exploiting branch, which leverage diffusion models and attention networks to capture implicit and explicit preferences, respectively. At the core of Dimos is Bi-MaKAN, a novel backbone architecture featuring a pair of parameter-sharing bidirectional Mamba blocks and a Kolmogorov–Arnold network-based feature fusion layer, designed to enhance both performance and efficiency. To further improve generalization and reduce overfitting, we unify the sequential state spaces of both branches. Additionally, we introduce a linearly weighted fusion mechanism that integrates preference representations from both branches, enabling flexible adjustment of implicit and explicit preference contributions during training and inference. Extensive experiments on three real-world benchmark datasets demonstrate the superiority of Dimos, achieving up to 2. 79% improvement in Recall, 3. 09% in Mean Reciprocal Rank (MRR), and 3. 00% in Normalized Discounted Cumulative Gain (NDCG) over state-of-the-art baselines. Efficiency evaluations show substantial gains, with reductions of 94. 32% in Graphics Processing Unit (GPU) memory usage, 66. 81% in training time, and 98. 80% in inference time. In-depth analyses reveal a collaborative effect between the two branches during both training and inference, with dataset scale modulating their relative importance.

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AAAI Conference 2026 Conference Paper

History-Aware Reasoning for GUI Agents

Ziwei Wang
Leyang Yang
Xiaoxuan Tang
Sheng Zhou
Dajun Chen
Wei Jiang
Yong Li

Advances in Multimodal Large Language Models have significantly enhanced Graphical User Interface (GUI) automation. Equipping GUI agents with reliable episodic reasoning capabilities is essential for bridging the gap between users’ concise task descriptions and the complexities of real-world execution. Current methods integrate Reinforcement Learning (RL) with System-2 Chain-of-Thought, yielding notable gains in reasoning enhancement. For long-horizon GUI tasks, historical interactions connect each screen to the goal-oriented episode chain, and effectively leveraging these clues is crucial for the current decision. However, existing native GUI agents exhibit weak short-term memory in their explicit reasoning, interpreting the chained interactions as discrete screen understanding, i.e., unawareness of the historical interactions within the episode. This history-agnostic reasoning challenges their performance in GUI automation. To alleviate this weakness, we propose a History-Aware Reasoning (HAR) framework, which encourages an agent to reflect on its own errors and acquire episodic reasoning knowledge from them via tailored strategies that enhance short-term memory in long-horizon interaction. The framework mainly comprises constructing a reflective learning scenario, synthesizing tailored correction guidelines, and designing a hybrid RL reward function. Using the HAR framework, we develop a native end-to-end model, HAR-GUI-3B, which alters the inherent reasoning mode from history-agnostic to history-aware, equipping the GUI agent with stable short-term memory and reliable perception of screen details. Comprehensive evaluations across a range of GUI-related benchmarks demonstrate the effectiveness and generalization of our method.

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AAAI Conference 2026 Conference Paper

ProBench: Benchmarking GUI Agents with Accurate Process Information

Leyang Yang
Ziwei Wang
Xiaoxuan Tang
Sheng Zhou
Dajun Chen
Wei Jiang
Yong Li

With the deep integration of artificial intelligence and interactive technology, Graphical User Interface (GUI) Agent, as the carrier connecting goal-oriented natural language and real-world devices, has received widespread attention from the community. Contemporary benchmarks aim to evaluate the comprehensive capabilities of GUI agents in GUI operation tasks, generally determining task completion solely by inspecting the final screen state. However, GUI operation tasks consist of multiple chained steps while not all critical information is presented in the final few pages. Although a few research has begun to incorporate intermediate steps into evaluation, accurately and automatically capturing this process information still remains an open challenge. To address this weakness, we introduce ProBench, a comprehensive mobile benchmark with over 200 challenging GUI tasks covering widely-used scenarios. Remaining the traditional State-related Task evaluation, we extend our dataset to include Process-related Task and design a specialized evaluation method. A newly introduced Process Provider automatically supplies accurate process information, enabling presice assessment of agent's performance. Our evaluation of advanced GUI agents reveals significant limitations for real-world GUI scenarios. These shortcomings are prevalent across diverse models, including both large-scale generalist models and smaller, GUI-specific models. A detailed error analysis further exposes several universal problems, outlining concrete directions for future improvements.

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AAAI Conference 2026 Conference Paper

Semi-Supervised High Dynamic Range Image Reconstructing via Bi-Level Uncertain Area Masking

Wei Jiang
Jiahao Cui
Yizheng Wu
Zhan Peng
Zhiyu Pan
Zhiguo Cao

Reconstructing high dynamic range (HDR) images from low dynamic range (LDR) bursts plays an essential role in the computational photography. Impressive progress has been achieved by learning-based algorithms which require LDR-HDR image pairs. However, these pairs are hard to obtain, which motivates researchers to delve into the problem of annotation-efficient HDR image reconstructing: how to achieve comparable performance with limited HDR ground truths (GTs). This work attempts to address this problem from the view of semi-supervised learning where a teacher model generates pseudo HDR GTs for the LDR samples without GTs and a student model learns from pseudo GTs. Nevertheless, the confirmation bias, i.e., the student may learn from the artifacts in pseudo HDR GTs, presents an impediment. To remove this impediment, an uncertainty-based masking process is proposed to discard unreliable parts of pseudo GTs at both pixel and patch levels, then the student can learn from the trusted areas. With this novel masking process, our semi-supervised HDR reconstructing method not only outperforms previous annotation-efficient algorithms, but also achieves comparable performance with up-to-date fully-supervised methods by using only 6.7% HDR GTs.

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AAAI Conference 2026 Conference Paper

SSCL: Adversarially Guided Image Compression via Semantic and Spectral Consistency Learning

Wei Jiang
Yongqi Zhai
Jiayu Yang
Bohao Feng
Wenqiang Wang
Bo Huang
Lin Ding
Ronggang Wang

Perceptual image compression has recently gained increasing attention, as it aims to reconstruct visually realistic images using generative models. Most existing methods adopt patch-based generative adversarial networks (PatchGAN) for one-step image generation, where adversarial training helps the decoder learn the distribution of natural images. However, this strategy is often coarse-grained, as it focuses mainly on patch-level consistency and overlooks global structural and semantic details. To address this limitation, we propose a simple yet effective Semantic and Spectral Consistency Learning (SSCL) strategy, which complements existing patch-based approaches for more accurate distribution alignment. For semantic consistency, we leverage semantic vision models to extract semantic features. The semantic discriminator, aware of the specific semantics of each image, provides more adaptive and precise feedback. This guides the encoder to retain meaningful information and helps the decoder synthesize detailed textures, without requiring explicit semantic transmission or additional modules. For spectral consistency, we introduce a frequency discriminator that focuses on high-frequency components, helping to reduce artifacts based on spectral priors. Experiments show that SSCL outperforms existing perceptual codecs in terms of visual quality. Compared to MS-ILLM, SSCL achieves 45% to 60% bit-rate savings on CLIC2020 and Kodak datasets, measured by FID and DISTS.

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EAAI Journal 2025 Journal Article

Django-based framework database for leakage detection using machine learning for water distribution networks

Yiwei Xie
Mengze Gao
Fan Luo
Ao Zhou
Yunfeng Yang
Jian Hu
Wei Jiang
Yuanyao Ye

Leakage in water supply pipe networks is a critical issue, with traditional detection methods being inefficient and error-prone. Acoustic-based leak detection often lacks standardized databases, limiting its effectiveness. This study proposes an integrated system using MySQL, Python, and Django for managing and analyzing acoustic leakage data. The system incorporates Variable Modal Decomposition (VMD), Wavelet Threshold Noise Reduction, Feature Extraction, and Support Vector Machine (SVM) for accurate leak detection. Experimentation on 413 labeled acoustic samples achieved classification accuracies of 96. 1% (training set) and 97. 4% (test set). This approach enhances detection precision and offers a scalable solution for real-time monitoring, with significant practical implications for improving water distribution system management and decision-making.

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ICML Conference 2025 Conference Paper

Efficiently Serving Large Multimodal Models Using EPD Disaggregation

Gursimran Singh
Xinglu Wang
Yifan Hu
Timothy Tin Long Yu
Linzi Xing
Wei Jiang
Zhefeng Wang
Xiaolong Bai

Large Multimodal Models (LMMs) extend Large Language Models (LLMs) by handling diverse inputs such as images, audio, and video, but at the cost of adding a multimodal encoding stage that increases both computational and memory overhead. This step negatively affects key Service Level Objectives (SLOs), such as time to first token (TTFT) and time per output token (TPOT). We introduce Encode-Prefill-Decode (EPD) Disaggregation, a novel framework that separates the encoding, prefill, and decode stages onto dedicated resources. Unlike current systems, which bundle encoding and prefill together, our approach decouples these steps, unlocking new opportunities and optimizations. These include a mechanism to cache multimedia tokens for efficient transfer, a novel way to parallelize the encoding load within a request, a module for optimal resource allocation for disaggregated serving, and a novel role-switching method to handle changing workload characteristics. Experimental evaluations with popular LMMs show substantial gains in memory efficiency (up to 15$\times$ lower peak memory utilization), batch sizes (up to 22$\times$ larger), 10$\times$ more images per request, and 2. 2$\times$ larger KV caches. Furthermore, it leads to significant improvements in SLO attainment (up to 90–100% improvement) and TTFT (up to 71% reduction), compared to systems that do not disaggregate. The code is available at https: //github. com/vbdi/epdserve.

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IROS Conference 2025 Conference Paper

Hierarchical Trajectory Planning Method for Piano-Playing Robot

Zirui Wang
Jiayu Zhang
Wei Jiang
Tao Jiang
Jingdong Zhao
Liangliang Zhao
Baoshi Cao
Le Qi

Piano-playing tasks, which effectively demonstrate bimanual coordination capabilities in humanoid robots, are increasingly becoming a research focus. However, prior research has predominantly focused on Cartesian space trajectory planning without adequately addressing real-world obstacle avoidance constraints and manipulator acceleration limits. This paper proposes a hierarchical trajectory planning framework that systematically incorporates both obstacle avoidance and acceleration constraints. Firstly, discrete Cartesian path points are generated using a dynamic programming approach; secondly, joint space path points are derived considering obstacle avoidance and joint limit constraints through dynamic programming; thirdly, the joint space trajectory is interpolated using a Jacobian inverse-based method; finally, the trajectory is refined using Model Predictive Control (MPC). Experimental results demonstrate that the proposed method produces trajectories satisfying both obstacle avoidance and acceleration constraints, enabling fluent piano piece execution in real-world environments.

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TMLR Journal 2025 Journal Article

Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning

Zhihao Liu
Xianliang Yang
Zichuan Liu
Yifan Xia
Wei Jiang
Yuanyu Zhang
Lijuan Li
Guoliang Fan

Multi-agent reinforcement learning (MARL) is employed to develop autonomous agents that can learn to adopt cooperative or competitive strategies within complex environments. However, the linear increase in the number of agents leads to a combinatorial explosion of the action space, which always results in algorithmic instability, difficulty in convergence, or entrapment in local optima. While researchers have designed a variety of effective algorithms to compress the action space, these methods also introduce new challenges, such as the need for manually designed prior knowledge or reliance on the structure of the problem, which diminishes the applicability of these techniques. In this paper, we introduce \textbf{E}volutionary action \textbf{SPA}ce \textbf{R}eduction with \textbf{K}nowledge (eSpark), an exploration function generation framework driven by large language models (LLMs) to boost exploration and prune unnecessary actions in MARL. Using just a basic prompt that outlines the overall task and setting, eSpark is capable of generating exploration functions in a zero-shot manner, identifying and pruning redundant or irrelevant state-action pairs, and then achieving autonomous improvement from policy feedback. In reinforcement learning tasks involving inventory management and traffic light control encompassing a total of 15 scenarios, eSpark consistently outperforms the combined MARL algorithm in all scenarios, achieving an average performance gain of 34.4% and 9.9% in the two types of tasks respectively. Additionally, eSpark has proven to be capable of managing situations with a large number of agents, securing a 29.7% improvement in scalability challenges that featured over 500 agents. The code can be found in https://github.com/LiuZhihao2022/eSpark.

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IJCAI Conference 2025 Conference Paper

Multi-View Learning with Context-Guided Receptance for Image Denoising

Binghong Chen
Tingting Chai
Wei Jiang
Yuanrong Xu
Guanglu Zhou
Xiangqian Wu

Image denoising is essential in low-level vision applications such as photography and automated driving. Existing methods struggle with distinguishing complex noise patterns in real-world scenes and consume significant computational resources due to reliance on Transformer-based models. In this work, the Context-guided Receptance Weighted Key-Value (CRWKV) model is proposed, combining enhanced multi-view feature integration with efficient sequence modeling. The Context-guided Token Shift (CTS) mechanism is introduced to effectively capture local spatial dependencies and enhance the model's ability to model real-world noise distributions. Also, the Frequency Mix (FMix) module extracting frequency-domain features is designed to isolate noise in high-frequency spectra, and is integrated with spatial representations through a multi-view learning process. To improve computational efficiency, the Bidirectional WKV (BiWKV) mechanism is adopted, enabling full pixel-sequence interaction with linear complexity while overcoming the causal selection constraints. The model is validated on multiple real-world image denoising datasets, outperforming the state-of-the-art methods quantitatively and reducing inference time up to 40%. Qualitative results further demonstrate the ability of our model to restore fine details in various scenes. The code is publicly available at https: //github. com/Seeker98/CRWKV.

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IROS Conference 2025 Conference Paper

Offline Reinforcement Learning with Koopman Operators for Control of Soft Robots

Yue Jiang
Cong Li
Yihe Yang
Wenyu Cao
Xin Xu
Jinze Liu
Wei Jiang
Xinglong Zhang

Soft robots are promising to offer flexibility in environmental interaction tasks through compliant deformations. However, the infinite degrees of freedom and high nonlinearity of dynamics pose significant challenges in dynamic modeling and control in soft robots. While online reinforcement learning (RL) is promising for designing policies directly from data, the black-box policy learning process suffers from data inefficiency and sim-to-real gap, limiting its applications in soft robots. To address these challenges, we propose a novel offline RL with Koopman operators (KORL) framework to generate control policies for soft robots without using physical simulators or real-world interactions. In particular, we first utilize a deep neural network to map dynamics of soft robots to a lifted Koopman observable space, which is inherently linear. Then, an offline RL algorithm with a control-informed actor is designed to learn the robotic policy in the linear observable space. This is significantly different from the black-box policy design in existing offline RL paradigms. The designed Koopman observable enables efficient model-free policy learning with linear control theory, improving control performance while preserving interpretability in policy learning. The effectiveness of our KORL framework is validated in a real-world soft robotic system. Comparative experimental results demonstrate that our method outperforms state-of-the-art methods in target-reaching and trajectory-tracking tasks.

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IJCAI Conference 2025 Conference Paper

Smoothed Online Convex Optimization with Delayed Feedback

Sifan Yang
Wenhao Yang
Wei Jiang
Yuanyu Wan
Lijun Zhang

Smoothed online convex optimization (SOCO), in which the online player incurs both a hitting cost and a switching cost for changing its decisions, has garnered significant attention in recent years. While existing studies typically assume that the gradient information is revealed immediately, such an assumption may not hold in some real-world applications. To overcome this limitation, we investigate SOCO with delayed feedback, and develop two online algorithms that can minimize the dynamic regret with switching cost. Firstly, we extend Mild-OGD, an existing algorithm that adopts the meta-expert framework for online convex optimization with delayed feedback, to account for switching cost. Specifically, we analyze the switching cost in the expert-algorithm of Mild-OGD, and then modify its meta-algorithm to incorporate this cost when assigning the weight to each expert. We demonstrate that our proposed method, Smelt-DOGD can achieve an O(√(dT(P_T+1))) dynamic regret bound with switching cost, where d is the maximum delay and P_T is the path-length. Secondly, we develop an efficient variant to reduce the number of projections per round from O(log T) to 1, yet maintaining the same theoretical guarantee. The key idea is to construct a new surrogate loss defined over a simpler domain for expert-algorithms so that these experts do not need to perform the complex projection operations in each round. Finally, we conduct experiments to validate the effectiveness and efficiency of our algorithms.

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JBHI Journal 2025 Journal Article

Wearable PPG Based BP Estimation Methods: A Systematic Review and Meta-Analysis

Ziya Sastimoglu
Sophini Subramaniam
Abu Ilius Faisal
Wei Jiang
Andrew Ye
M. Jamal Deen

This meta-analysis and systematic review, conducted in accordance with PRISMA guidelines, explores the efficacy of cuff-less blood pressure (BP) monitoring methods, particularly focusing on photoplethysmogram-based technologies. This comprehensive analysis carefully searched prominent databases such as MEDLINE, PubMed, AMED, Embase, and IEEE-Xplore, encompassing 25 studies with a collective participant pool of 21 142 individuals. The study primarily investigates the accuracy and practicality of continuous BP estimation devices and algorithms, aiming to assess their suitability for daily or long-term, as well as their applicability and usability across a broad population. The mean disparities were 4. 14 mmHg for systolic blood pressure (SBP) and 2. 79 mmHg for diastolic blood pressure (DBP), highlighting a close congruence with established measurement techniques. An in-depth analysis into specific methodologies reveals that Pulse Waveform Analysis (PWA) demonstrates a more favorable performance compared to Pulse Wave Velocity (PWV) for both SBP and DBP, although these differences are not statistically significant. The findings indicate a promising future for wearable devices in short-term BP monitoring scenarios. Both PWA and PWV methods in wearable formats have shown considerable potential as effective tools for BP assessment. However, the study underscores the need for further research, particularly targeting hypertensive populations, to validate the long-term effectiveness and reliability of these wearables. Finally, this investigation is crucial for establishing the role of wearables in ongoing, reliable BP monitoring, especially when considered in conjunction with other health monitoring technologies.

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NeurIPS Conference 2024 Conference Paper

Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions

Wei Jiang
Sifan Yang
Yibo Wang
Lijun Zhang

This paper explores adaptive variance reduction methods for stochastic optimization based on the STORM technique. Existing adaptive extensions of STORM rely on strong assumptions like bounded gradients and bounded function values, or suffer an additional $\mathcal{O}(\log T)$ term in the convergence rate. To address these limitations, we introduce a novel adaptive STORM method that achieves an optimal convergence rate of $\mathcal{O}(T^{-1/3})$ for non-convex functions with our newly designed learning rate strategy. Compared with existing approaches, our method requires weaker assumptions and attains the optimal convergence rate without the additional $\mathcal{O}(\log T)$ term. We also extend the proposed technique to stochastic compositional optimization, obtaining the same optimal rate of $\mathcal{O}(T^{-1/3})$. Furthermore, we investigate the non-convex finite-sum problem and develop another innovative adaptive variance reduction method that achieves an optimal convergence rate of $\mathcal{O}(n^{1/4} T^{-1/2} )$, where $n$ represents the number of component functions. Numerical experiments across various tasks validate the effectiveness of our method.

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NeurIPS Conference 2024 Conference Paper

Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction

Wei Jiang
Sifan Yang
Wenhao Yang
Lijun Zhang

Sign stochastic gradient descent (signSGD) is a communication-efficient method that transmits only the sign of stochastic gradients for parameter updating. Existing literature has demonstrated that signSGD can achieve a convergence rate of $\mathcal{O}(d^{1/2}T^{-1/4})$, where $d$ represents the dimension and $T$ is the iteration number. In this paper, we improve this convergence rate to $\mathcal{O}(d^{1/2}T^{-1/3})$ by introducing the Sign-based Stochastic Variance Reduction (SSVR) method, which employs variance reduction estimators to track gradients and leverages their signs to update. For finite-sum problems, our method can be further enhanced to achieve a convergence rate of $\mathcal{O}(m^{1/4}d^{1/2}T^{-1/2})$, where $m$ denotes the number of component functions. Furthermore, we investigate the heterogeneous majority vote in distributed settings and introduce two novel algorithms that attain improved convergence rates of $\mathcal{O}(d^{1/2}T^{-1/2} + dn^{-1/2})$ and $\mathcal{O}(d^{1/4}T^{-1/4})$ respectively, outperforming the previous results of $\mathcal{O}(dT^{-1/4} + dn^{-1/2})$ and $\mathcal{O}(d^{3/8}T^{-1/8})$, where $n$ represents the number of nodes. Numerical experiments across different tasks validate the effectiveness of our proposed methods.

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AAAI Conference 2024 Conference Paper

Non-stationary Projection-Free Online Learning with Dynamic and Adaptive Regret Guarantees

Yibo Wang
Wenhao Yang
Wei Jiang
Shiyin Lu
Bing Wang
Haihong Tang
Yuanyu Wan
Lijun Zhang

Projection-free online learning has drawn increasing interest due to its efficiency in solving high-dimensional problems with complicated constraints. However, most existing projection-free online methods focus on minimizing the static regret, which unfortunately fails to capture the challenge of changing environments. In this paper, we investigate non-stationary projection-free online learning, and choose dynamic regret and adaptive regret to measure the performance. Specifically, we first provide a novel dynamic regret analysis for an existing projection-free method named BOGD_IP, and establish an O(T^¾ (1+P_T)) dynamic regret bound, where P_T denotes the path-length of the comparator sequence. Then, we improve the upper bound to O(T^¾ (1+P_T)^¼) by running multiple BOGD_IP algorithms with different step sizes in parallel, and tracking the best one on the fly. Our results are the first general-case dynamic regret bounds for projection-free online learning, and can recover the existing O(T^¾) static regret by setting P_T = 0. Furthermore, we propose a projection-free method to attain an O(?^¾) adaptive regret bound for any interval with length?, which nearly matches the static regret over that interval. The essential idea is to maintain a set of BOGD_IP algorithms dynamically, and combine them by a meta algorithm. Moreover, we demonstrate that it is also equipped with an O(T^¾ (1+P_T)^¼) dynamic regret bound. Finally, empirical studies verify our theoretical findings.

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NeurIPS Conference 2024 Conference Paper

Online Composite Optimization Between Stochastic and Adversarial Environments

Yibo Wang
Sijia Chen
Wei Jiang
Wenhao Yang
Yuanyu Wan
Lijun Zhang

We study online composite optimization under the Stochastically Extended Adversarial (SEA) model. Specifically, each loss function consists of two parts: a fixed non-smooth and convex regularizer, and a time-varying function which can be chosen either stochastically, adversarially, or in a manner that interpolates between the two extremes. In this setting, we show that for smooth and convex time-varying functions, optimistic composite mirror descent (OptCMD) can obtain an $\mathcal{O}(\sqrt{\sigma_{1: T}^2} + \sqrt{\Sigma_{1: T}^2})$ regret bound, where $\sigma_{1: T}^2$ and $\Sigma_{1: T}^2$ denote the cumulative stochastic variance and the cumulative adversarial variation of time-varying functions, respectively. For smooth and strongly convex time-varying functions, we establish an $\mathcal{O}((\sigma_{\max}^2 + \Sigma_{\max}^2)\log(\sigma_{1: T}^2 + \Sigma_{1: T}^2))$ regret bound, where $\sigma_{\max}^2$ and $\Sigma_{\max}^2$ denote the maximal stochastic variance and the maximal adversarial variation, respectively. For smooth and exp-concave time-varying functions, we achieve an $\mathcal{O}(d \log (\sigma_{1: T}^2 + \Sigma_{1: T}^2))$ bound where $d$ denotes the dimensionality. Moreover, to deal with the unknown function type in practical problems, we propose a multi-level \textit{universal} algorithm that is able to achieve the desirable bounds for three types of time-varying functions simultaneously. It should be noticed that all our findings match existing bounds for the SEA model without the regularizer, which implies that there is \textit{no price} in regret bounds for the benefits gained from the regularizer.

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AAAI Conference 2024 Conference Paper

Summarizing Stream Data for Memory-Constrained Online Continual Learning

Jianyang Gu
Kai Wang
Wei Jiang
Yang You

Replay-based methods have proved their effectiveness on online continual learning by rehearsing past samples from an auxiliary memory. With many efforts made on improving training schemes based on the memory, however, the information carried by each sample in the memory remains under-investigated. Under circumstances with restricted storage space, the informativeness of the memory becomes critical for effective replay. Although some works design specific strategies to select representative samples, by only employing a small number of original images, the storage space is still not well utilized. To this end, we propose to Summarize the knowledge from the Stream Data (SSD) into more informative samples by distilling the training characteristics of real images. Through maintaining the consistency of training gradients and relationship to the past tasks, the summarized samples are more representative for the stream data compared to the original images. Extensive experiments are conducted on multiple online continual learning benchmarks to support that the proposed SSD method significantly enhances the replay effects. We demonstrate that with limited extra computational overhead, SSD provides more than 3% accuracy boost for sequential CIFAR-100 under extremely restricted memory buffer. Code in https://github.com/vimar-gu/SSD.

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EAAI Journal 2023 Journal Article

Distributed deep reinforcement learning-based gas supply system coordination management method for solid oxide fuel cell

Jiawen Li
Haoyang Cui
Wei Jiang

In order to sustain solid oxide fuel cell (SOFC) net output power and prevent violation of oxygen excess ratio (OER) constraint and fuel utilization (FU) constraint, a data-driven gas supply system coordination management method is proposed. Accordingly, a population evolution-based multi-agent double delay deep deterministic policy gradient (PE-MA4DPG) algorithm is introduced. The artificial intelligence design of the algorithm is guided by the concepts of imitation learning and curriculum learning, whereby different agents of different combinations are trained in different environments, thus improving the robustness of the coordination strategy. In this algorithm, the hydrogen controller and the air controller are treated as two agents. The centralized training enables agents with different objectives to coordinate with each other. The effectiveness of the proposed algorithm is demonstrated in three experiments, wherein the proposed algorithm is compared with a group of existing algorithms.

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NeurIPS Conference 2022 Conference Paper

Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization

Wei Jiang
Gang Li
Yibo Wang
Lijun Zhang
Tianbao Yang

Variance reduction techniques such as SPIDER/SARAH/STORM have been extensively studied to improve the convergence rates of stochastic non-convex optimization, which usually maintain and update a sequence of estimators for a single function across iterations. What if we need to track multiple functional mappings across iterations but only with access to stochastic samples of $\mathcal{O}(1)$ functional mappings at each iteration? There is an important application in solving an emerging family of coupled compositional optimization problems in the form of $\sum_{i=1}^m f_i(g_i(\mathbf{w}))$, where $g_i$ is accessible through a stochastic oracle. The key issue is to track and estimate a sequence of $\mathbf g(\mathbf{w})=(g_1(\mathbf{w}), \ldots, g_m(\mathbf{w}))$ across iterations, where $\mathbf g(\mathbf{w})$ has $m$ blocks and it is only allowed to probe $\mathcal{O}(1)$ blocks to attain their stochastic values and Jacobians. To improve the complexity for solving these problems, we propose a novel stochastic method named Multi-block-Single-probe Variance Reduced (MSVR) estimator to track the sequence of $\mathbf g(\mathbf{w})$. It is inspired by STORM but introduces a customized error correction term to alleviate the noise not only in stochastic samples for the selected blocks but also in those blocks that are not sampled. With the help of the MSVR estimator, we develop several algorithms for solving the aforementioned compositional problems with improved complexities across a spectrum of settings with non-convex/convex/strongly convex/Polyak-Lojasiewicz (PL) objectives. Our results improve upon prior ones in several aspects, including the order of sample complexities and dependence on the strong convexity parameter. Empirical studies on multi-task deep AUC maximization demonstrate the better performance of using the new estimator.

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NeurIPS Conference 2022 Conference Paper

Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor

Lijun Zhang
Wei Jiang
Jinfeng Yi
Tianbao Yang

In this paper, we investigate an online prediction strategy named as Discounted-Normal-Predictor [Kapralov and Panigrahy, 2010] for smoothed online convex optimization (SOCO), in which the learner needs to minimize not only the hitting cost but also the switching cost. In the setting of learning with expert advice, Daniely and Mansour [2019] demonstrate that Discounted-Normal-Predictor can be utilized to yield nearly optimal regret bounds over any interval, even in the presence of switching costs. Inspired by their results, we develop a simple algorithm for SOCO: Combining online gradient descent (OGD) with different step sizes sequentially by Discounted-Normal-Predictor. Despite its simplicity, we prove that it is able to minimize the adaptive regret with switching cost, i. e. , attaining nearly optimal regret with switching cost on every interval. By exploiting the theoretical guarantee of OGD for dynamic regret, we further show that the proposed algorithm can minimize the dynamic regret with switching cost in every interval.

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NeurIPS Conference 2021 Conference Paper

Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions

Lijun Zhang
Guanghui Wang
Wei-Wei Tu
Wei Jiang
Zhi-Hua Zhou

To deal with changing environments, a new performance measure—adaptive regret, defined as the maximum static regret over any interval, was proposed in online learning. Under the setting of online convex optimization, several algorithms have been successfully developed to minimize the adaptive regret. However, existing algorithms lack universality in the sense that they can only handle one type of convex functions and need apriori knowledge of parameters. By contrast, there exist universal algorithms, such as MetaGrad, that attain optimal static regret for multiple types of convex functions simultaneously. Along this line of research, this paper presents the first universal algorithm for minimizing the adaptive regret of convex functions. Specifically, we borrow the idea of maintaining multiple learning rates in MetaGrad to handle the uncertainty of functions, and utilize the technique of sleeping experts to capture changing environments. In this way, our algorithm automatically adapts to the property of functions (convex, exponentially concave, or strongly convex), as well as the nature of environments (stationary or changing). As a by product, it also allows the type of functions to switch between rounds.

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NeurIPS Conference 2021 Conference Paper

Revisiting Smoothed Online Learning

Lijun Zhang
Wei Jiang
Shiyin Lu
Tianbao Yang

In this paper, we revisit the problem of smoothed online learning, in which the online learner suffers both a hitting cost and a switching cost, and target two performance metrics: competitive ratio and dynamic regret with switching cost. To bound the competitive ratio, we assume the hitting cost is known to the learner in each round, and investigate the simple idea of balancing the two costs by an optimization problem. Surprisingly, we find that minimizing the hitting cost alone is $\max(1, \frac{2}{\alpha})$-competitive for $\alpha$-polyhedral functions and $1 + \frac{4}{\lambda}$-competitive for $\lambda$-quadratic growth functions, both of which improve state-of-the-art results significantly. Moreover, when the hitting cost is both convex and $\lambda$-quadratic growth, we reduce the competitive ratio to $1 + \frac{2}{\sqrt{\lambda}}$ by minimizing the weighted sum of the hitting cost and the switching cost. To bound the dynamic regret with switching cost, we follow the standard setting of online convex optimization, in which the hitting cost is convex but hidden from the learner before making predictions. We modify Ader, an existing algorithm designed for dynamic regret, slightly to take into account the switching cost when measuring the performance. The proposed algorithm, named as Smoothed Ader, attains an optimal $O(\sqrt{T(1+P_T)})$ bound for dynamic regret with switching cost, where $P_T$ is the path-length of the comparator sequence. Furthermore, if the hitting cost is accessible in the beginning of each round, we obtain a similar guarantee without the bounded gradient condition, and establish an $\Omega(\sqrt{T(1+P_T)})$ lower bound to confirm the optimality.

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ICRA Conference 2019 Conference Paper

DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block

Wei Jiang
Yan Wu 0011
Linting Guan
Junqiao Zhao

For the domain of self-driving and automatic parking, perception is a basic and critical technique, moreover, the detection of lane markings and parking slots is an important part of visual perception. Compared with front sight images, panoramic images(PI) can capture more comprehensive pavement information. However, the imbalance of different classes in PI is even more serious. Additionally, the judgment of boundary information between areas is a hard problem in deep models. Therefore, we propose a new model named DFNet to solve these problems. The proposed model has two main contributions, one is dynamic loss weights, and the other is residual fusion block(RFB). DFNet use dynamic loss weights to overcome the negative effect of imbalance dataset, which are calculated according to the pixel number of each class in a batch. RFB is composed of several convolutional layers, a pooling layer, and a fusion layer to combine the feature maps by pixel multiplication, which can reduce boundary information loss. We evaluate our method on PSV dataset, and the achieved advanced results demonstrate the effectiveness of the proposed model.

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IS Journal 2015 Journal Article

System Informatics: From Methodology to Applications

Kang Zhao
Yao Xie
Kwok-Leung Tsui
Qingming Wei
Wenpo Huang
Wei Jiang
Yanting Li
Sugon Cho

This installment of Trends & Controversies provides an array of perspectives on the latest research in system informatics. Kang Zhao, Yao Xie, and Kwok-Leung Tsui introduce the work in "System Informatics: From Methodology to Applications. " On the methodology side, in "Projection-Based Process Monitoring and Empirical Divergence, " Wenpo Huang, Wei Jiang, Qingming Wei, and Yanting Li propose a framework of projection-based methods, and in "One-Class Classification Methods for Process Monitoring and Diagnosis, " Sugon Cho and Seoung Bum Kim discuss how a data analytics algorithm can be used as a control chart. On the application side, "IoT-Enabled System Informatics for Service Decision Making, " by Kaibo Liu and Jianjun Shi, reviews current trends and future opportunities for IoT, with a special focus on issues related to the big data collected by multiple sensors. "Quantifying the Risk Level of Functional Chips in DRAM Wafers, " by Young-Seon Jeong, Byunghoon Kimb, Seung Hoon Tong, In-Kap Chang, and Myong K. Jeong, not only identifies research challenges and opportunities for decision making with massive data in the process of semiconductor manufacturing but also quantifies the risk level of functional chips in DRAM wafers. Finally, "Flight Operations Monitoring through Cluster Analysis: A Case Study, " by Florent Charruaud and Lishuai Li, describes a new method called cluster-based anomaly detection to help airline safety experts monitor daily flights and detect anomalies.

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TIST Journal 2014 Journal Article

Traffic Information Publication with Privacy Preservation

Sashi Gurung
Dan Lin
Wei Jiang
Ali Hurson
Rui Zhang

We are experiencing the expanding use of location-based services such as AT&T’s TeleNav GPS Navigator and Intel’s Thing Finder. Existing location-based services have collected a large amount of location data, which has great potential for statistical usage in applications like traffic flow analysis, infrastructure planning, and advertisement dissemination. The key challenge is how to wisely use the data without violating each user’s location privacy concerns. In this article, we first identify a new privacy problem, namely, the inference-route problem, and then present our anonymization algorithms for privacy-preserving trajectory publishing. The experimental results have demonstrated that our approach outperforms the latest related work in terms of both efficiency and effectiveness.

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TCS Journal 2013 Journal Article

Feedback vertex sets on restricted bipartite graphs

Wei Jiang
Tian Liu
Chaoyi Wang
Ke Xu

A feedback vertex set (FVS) in a graph is a subset of vertices whose complement induces a forest. Finding a minimum FVS is NP -complete on bipartite graphs, but tractable on convex bipartite graphs and on chordal bipartite graphs. A bipartite graph is called tree convex, if a tree is defined on one part of the vertices, such that for every vertex in the other part, its neighborhood induces a subtree. When the tree is a path, a triad or a star, the bipartite graph is called convex bipartite, triad convex bipartite or star convex bipartite, respectively. We show that: (1) FVS is tractable on triad convex bipartite graphs; (2) FVS is NP -complete on star convex bipartite graphs and on tree convex bipartite graphs where the maximum degree of vertices on the tree is at most three.

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