Yang Xu

JAIR Journal 2026 Journal Article

A Review of Causal Decision Making

• Lin Ge
• Hengrui Cai
• Runzhe Wan
• Yang Xu
• Rui Song

To make effective decisions, it is important to have a thorough understanding of the causal relationships among actions, environments, and outcomes. This review aims to surface three crucial aspects of decision making through a causal lens: 1) the discovery of causal relationships through causal structure learning, 2) understanding the impacts of these relationships through causal effect learning, and 3) applying the knowledge gained from the first two aspects to support decision making via causal policy learning. Moreover, we identify challenges that hinder the broader utilization of causal decision making and discuss recent advances in overcoming these challenges. Finally, we provide future research directions to address these challenges and further enhance the implementation of causal decision making in practice, with real-world applications illustrated through the proposed causal decision-making workflow. To facilitate broader adoption, we additionally integrate relevant methods into a unified Python-based collection, offering a methodological and practical framework for the community (available at https://causaldm.github.io/Causal-Decision-Making).

AAAI Conference 2026 Conference Paper

GeoPTH: A Lightweight Approach to Category-Based Trajectory Retrieval via Geometric Prototype Trajectory Hashing

• Yang Xu
• Zuliang Yang
• Kai Ming Ting

Trajectory similarity retrieval is an important part of spatiotemporal data mining, however, existing methods have the following limitations: traditional metrics are computationally expensive, while learning-based methods suffer from substantial training costs and potential instability. This paper addresses these problems by proposing Geometric Prototype Trajectory Hashing (GeoPTH), a novel, lightweight, and non-learning framework for efficient category-based trajectory retrieval. GeoPTH constructs data-dependent hash functions by using representative trajectory prototypes, i.e., small point sets preserving geometric characteristics, as anchors. The hashing process is efficient, which involves mapping a new trajectory to its closest prototype via a robust, Hausdorff metric. Extensive experiments show that GeoPTH’s retrieval accuracy is highly competitive with both traditional metrics and state-of-the-art learning methods, and it significantly outperforms binary codes generated through simple binarization of the learned embeddings. Critically, GeoPTH consistently outperforms all competitors in terms of efficiency. Our work demonstrates that a lightweight, prototype-centric approach offers a practical and powerful alternative, achieving an exceptional retrieval performance and computational efficiency.

AAAI Conference 2026 Conference Paper

IDK-S: Incremental Distributional Kernel for Streaming Anomaly Detection

• Yang Xu
• Yixiao Ma
• Kaifeng Zhang
• Zuliang Yang
• Kai Ming Ting

Anomaly detection on data streams presents significant challenges, requiring methods to maintain high detection accuracy among evolving distributions while ensuring real-time efficiency. Here we introduce IDK-S, a novel Incremental Distributional Kernel for Streaming anomaly detection that effectively addresses these challenges by creating a new dynamic representation in the kernel mean embedding framework. The superiority of IDK-S is attributed to two key innovations. First, it inherits the strengths of the Isolation Distributional Kernel, an offline detector that has demonstrated significant performance advantages over foundational methods like Isolation Forest and Local Outlier Factor due to the use of a data-dependent kernel. Second, it adopts a lightweight incremental update mechanism that significantly reduces computational overhead compared to the naive baseline strategy of performing a full model retraining. This is achieved without compromising detection accuracy, a claim supported by its statistical equivalence to the full retrained model. Our extensive experiments on thirteen benchmarks demonstrate that IDK-S achieves superior detection accuracy while operating substantially faster, in many cases by an order of magnitude, than existing state-of-the-art methods.

AAAI Conference 2026 Conference Paper

Judge Q: Trainable Queries for Optimized Information Retention in KV Cache Eviction

• Yijun Liu
• Yixuan Wang
• Yuzhuang Xu
• Shiyu Ji
• Yang Xu
• Qingfu Zhu
• Wanxiang Che

Large language models (LLMs) utilize key-value (KV) cache to store historical information during sequence processing. The size of KV cache grows linearly as the length of the sequence extends, which seriously affects memory usage and decoding efficiency. Current methods for KV cache eviction typically utilize the last window from the pre-filling phase as queries to compute the KV importance scores for eviction. Although this scheme is simple to implement, it tends to overly focus on local information, potentially leading to the neglect or omission of crucial global information. To mitigate this issue, we propose **Judge Q**, a novel training method which incorporates a soft token list. This method only tunes the model’s embedding layer at a low training cost. By concatenating the soft token list at the end of the input sequence, we train these tokens' attention map to the original input sequence to align with that of the actual decoded tokens. In this way, the queries corresponding to the soft tokens can effectively capture global information and better evaluate the importance of the keys and values within the KV cache, thus maintaining decoding quality when KV cache is evicted. Under the same eviction budget, our method exhibits less performance degradation compared to existing eviction approaches. We validate our approach through experiments conducted on models such as Llama-3.1-8B-Instruct and Mistral-7B-Instruct-v0.3, using benchmarks including LongBench, RULER, and Needle-in-a-Haystack. Results indicate an improvement of approximately 1 point on the LongBench and over 3 points on RULER. This proposed methodology can be seamlessly integrated into existing open-source models with minimal training overhead, thereby enhancing performance in KV cache eviction scenarios.

AAAI Conference 2026 Conference Paper

MSCFL: Model Structure-Aware Clustered Federated Learning for System Heterogeneity and Data Drift

• Yang Xu
• Xiaowei Wu
• Zifeng Xu
• Cheng Zhang
• Ju Ren
• Yaoxue Zhang

Federated Learning (FL) faces significant challenges arising from both data and system heterogeneity. While Clustered Federated Learning (CFL) mitigates data heterogeneity by grouping clients with similar data distributions, it remains vulnerable to system heterogeneity, which can slow convergence due to performance disparities among clients. Moreover, data drift may degrade clustering accuracy and training efficiency over time. In this work, we propose a Model Structure-aware Clustered Federated Learning (MSCFL) framework that simultaneously addresses the issues of data heterogeneity, system heterogeneity, and data drift. MSCFL incorporates model pruning (MP) into the CFL framework to enhance training efficiency under system heterogeneity. To enable this integration, we address the key challenge of performing effective clustering based on heterogeneous, pruned local models with varying structures. To this end, we design a model structure-based similarity computation algorithm to integrate CFL with MP. To effectively address data drift, we propose a dynamic cluster migration strategy that efficiently monitors model structures via Hamming Distance and triggers re-clustering only when necessary. Extensive experimental results show that MSCFL improves the accuracy and convergence speed of cluster models, outperforming traditional CFL in various settings.

EAAI Journal 2026 Journal Article

Multilayer inverse dynamic deduction algorithm of standard contradiction separation rule based on parallel mechanism

• Guoyan Zeng
• Guanfeng Wu
• Shuwei Chen
• Peiyao Liu
• Jun Liu
• Yang Xu
• Jian Zhong

The standard contradiction separation (S-CS) rule is a new inference rule recently proposed in the field of automated reasoning, which is characterized by dynamism, robustness, and collaborative deduction of multiple clauses. According to the above characteristics, to further utilize the inference ability of the S-CS rule, we propose an inverse and parallel algorithms to extend and enhance S-CS rule. Specifically, a multi-layer inverse and parallel deduction algorithm (in short MIP) is built. This algorithm transforms the first-order logic clause set into multiple clause sets, which are then recursively and iteratively deduced in parallel such that whenever a clause set is unsatisfiable, the original clause set is unsatisfiable. The main advantages of this algorithm are inverse deduction, parallel deduction, and depth (multi-layer) deduction. In order to improve the performance of automated theorem prover, we embed this algorithm into the current top automated theorem provers Vampire and E to form the new provers MIP_V and MIP_E. Then we test MIP_V with the problems from the international competition (CASC) for automated theorem provers, and test MIP_E and MIP_V with the hardest problem of rating = 1 from the benchmark library TPTP. The experimental results show that MIP_V (MIP_E) has a better performance than Vampire (E), and MIP_V and MIP_E can solve 66 problems with rating = 1.

AAAI Conference 2026 Conference Paper

SCoNE: Spherical Consistent Neighborhoods Ensemble for Effective and Efficient Multi-View Anomaly Detection

• Yang Xu
• Hang Zhang
• Yixiao Ma
• Ye Zhu
• Kai Ming Ting

The core problem in multi-view anomaly detection is to represent local neighborhoods of normal instances consistently across all views. Recent approaches consider a representation of local neighborhood in each view independently, and then capture the consistent neighbors across all views via a learning process. They suffer from two key issues. First, there is no guarantee that they can capture consistent neighbors well, especially when the same neighbors are in regions of varied densities in different views, resulting in inferior detection accuracy. Second, the learning process has a high computational cost of O(N^2), rendering them inapplicable for large datasets. To address these issues, we propose a novel method termed Spherical Consistent Neighborhoods Ensemble (SCoNE). It has two unique features: (a) the consistent neighborhoods are represented with multi-view instances directly, requiring no intermediate representations as used in existing approaches; and (b) the neighborhoods have data-dependent properties, which lead to large neighborhoods in sparse regions and small neighborhoods in dense regions. The data-dependent properties enable local neighborhoods in different views to be represented well as consistent neighborhoods, without learning. This leads to O(N) time complexity. Empirical evaluations show that SCoNE has superior detection accuracy and runs orders-of-magnitude faster in large datasets than existing approaches.

JBHI Journal 2026 Journal Article

Simultaneous Decoding of Wrist Angles and Grasp Forces Based on Channel-Wise Cumulative Spike Trains

• Yang Yu
• Yang Xu
• Jiamin Zhao
• Dongxuan Li
• Weichao Guo
• Xinjun Sheng
• Xiangyang Zhu

Understanding the underlying mechanism of neuromuscular system on motion/force generation is essential for human-machine interfacing. However, simultaneous decoding of wrist angles and grasp forces from neural signals remains an open challenge in the field of neural interfacing. In this study, we proposed a scheme leveraging channel-wise cumulative spike trains (cw-CSTs) of motor units to simultaneously decode wrist angles and grasp forces. Specifically, a spatial spike detection method was utilized to detect cw-CST from surface electromyography, observing as much as possible of motor unit activities. Accordingly, we extracted three neural features to drive the decoders, including a twitch force model-based (cw-MUdrive) and a discharge rate-based (DR-cwCST) neural features derived from cw-CSTs, and DR of motor units (DR-MUST) decomposed by a conventional blind source separation algorithm. Wrist- and hand-specific decoders were built to estimate wrist angles and grasp forces via Gaussian process regression. Experiments were conducted with ten subjects, in which they activated wrist motions and grasp forces concurrently. We evaluated the performance with both accuracy and output stability. Results demonstrated that the cwCST-based neural features outperformed the conventional DR-MUST features with both higher accuracy and stability metrics. Additionally, cw-MUdrive performed better than DR-cwCST in grasp force estimation and comparable to DR-cwCST in wrist angle estimation. The outcome provides an effective solution for simultaneously decoding wrist movements and hand grasp forces, promoting the development of natural control in neural interface.

AAAI Conference 2026 Conference Paper

Towards Better Correctness and Efficiency in Code Generation

• Yunlong Feng
• Yang Xu
• Xiao Xu
• Binyuan Hui
• Junyang Lin

While code large language models have demonstrated remarkable progress in code generation, the generated code often exhibits poor runtime efficiency, limiting its practical application in performance-sensitive scenarios. To address this limitation, we propose an efficiency-oriented reinforcement learning framework guided by a novel performance reward. Based on this framework, we take a deeper dive into the code efficiency problem, identifying then proposing methods to overcome key bottlenecks: (1) Dynamic exploration overcomes the static data constraints of offline fine-tuning, enabling the discovery of more efficient code implementations. (2) The error-insensitive reinforcement learning method and high-contrast efficiency signals are crucial for mitigating systematic errors and achieving effective optimization. (3) Online exploration is most effective when starting from a high-correctness baseline, as this allows for efficiency improvements without sacrificing accuracy. With these discoveries, we finally propose a two-stage tuning method, which achieves high and balanced performance across correctness and efficiency. The results of experiments show the effectiveness of the method, which improves code correctness by 10.18% and runtime efficiency by 7.75% on a 7B model, achieving performance comparable to much larger model.

EAAI Journal 2025 Journal Article

Calculating of stomatal index for tomato and lettuce based on You Only Look Once version 8 and improved High-Resolution Network

• Yang Xu
• Li Du
• Qingrui Zhu
• Can Wang
• Liyuan Zhang
• Yaxiao Niu
• Qi Li
• Danyan Chen

The stomatal index (SI), the ratio of stomata to the total of stomata and pavement cells, is a crucial indicator of plant growth status. However, automatic counting of SI in lettuce and tomato presents significant challenges due to the difficulties in accurately segmenting and counting pavement cells with complex morphology. A novel artificial intelligence (AI)-driven architecture for calculating the SI is proposed. The improved High-Resolution Network (Imp_HRNet) integrates a Multi-level Data-dependent Feature Aggregation (MDFA) module to enhance cell segmentation, and a connected domain algorithm was used to count the segmented pavement cells. Compared to the HRNet, the Imp_HRNet demonstrates significant improvements: (1) a 0. 18% increase in pavement cell segmentation accuracy; (2) enhanced R 2 values, achieving 0. 9991 for lettuce and 0. 9985 for tomato; and (3) reduced mean absolute percentage errors (MAPE) by 9. 45% for lettuce and 4. 74% for tomato. The You Only Look Once version 8 (YOLOv8) was employed for stomata counting, achieving R 2 values of 0. 9964 for lettuce and 0. 9916 for tomato, with corresponding M A P E of 0. 83% and 2. 53%, respectively. The SI was calculated from stomata and pavement cells counts and achieved R 2 values of 0. 9653 for lettuce and 0. 9685 for tomato, with M A P E of 2. 22% and 3. 41%, respectively. These results showed that the proposed AI-driven method enables efficient and precise SI estimation, even for complex pavement cell shapes.

IROS Conference 2025 Conference Paper

DHC-ME: A Decentralized Hybrid Cooperative Approach for Multi-Robot Autonomous Exploration

• Wenhao Jia
• Yang Xu
• Chenglong Qian
• Xiufang Shi
• Jiming Chen
• Liang Li

Multi-robot exploration in unknown environments is a fundamental task for multi-robot systems, which requires the coordination of the robots to avoid collisions and conflicts while performing task allocation. Existing exploration strategies improve the efficiency of multi-robot exploration by modeling the multi-robot task allocation problem as a variant of the multiple traveling salesman problem. However, this is computationally intensive and difficult to deploy on physical platforms. Hence, this paper develops a hybrid strategy for range-sensing multi-robot exploration with effective team coordination, enabling a larger team dispersion degree and higher exploration efficiency. In addition, we present a novel multi-robot exploration point detection method suitable for narrow and dynamic environments, effectively reducing exploration failure and incompleteness. The Gazebo simulations demonstrate better exploration efficiency and the least time cost of our exploration framework compared with state-of-the-art methods, and real-world experiments also validate the effectiveness. The code is released at https://github.com/NeSC-IV/DHC_ME.

TMLR Journal 2025 Journal Article

Doubly Robust Uncertainty Quantification for Quantile Treatment Effects in Sequential Decision Making

• Yang Xu
• Chengchun Shi
• Shikai Luo
• Lan Wang
• Rui Song

We consider multi-stage sequential decision making, where the treatment at any stage may depend on the subject’s entire treatment and covariate history. We introduce a general framework for doubly robust uncertainty quantification for the quantiles of cumulative outcomes under a sequential treatment rule. While previous studies focused on mean effects, quantile effects offer unique insights into the distributional properties and are more robust for heavy-tailed outcomes. It is known that, doubly robust inference is significantly more challenging and largely unexplored for quantile treatment effects. More importantly, for mean effects, doubly robust estimation does not ensure doubly robust inference. Our approach first provides a doubly robust estimator for any quantile of interest based on pre-collected data, achieving semi-parametric efficiency. We then propose a novel doubly robust estimator for the asymptotic variance, enabling the construction of a doubly robust confidence interval. To overcome the challenges in parameter-dependent nuisance functions, we leverage deep conditional generative learning techniques. We demonstrate advantages of our approach via both simulation and real data from a short video platform. Additionally, we observe that our proposed approach leads to another mean effect estimator that outperforms existing estimators with heavy-tailed outcomes.

EAAI Journal 2025 Journal Article

Energy-derivative attention enhanced deep learning for multi-phase segmentation of mesoscale heterogeneous material using X-ray computed tomography images

• Xin Jing
• Yu Wang
• Yixuan Huan
• Kaiyu Guo
• Jiaqi Dong
• Zhanxiong Ma
• Yang Xu
• Qiangqiang Zhang

The precise and autonomous segmentation of mesoscale phases in heterogeneous materials remains challenging due to the similarity in characterization between holes and fractures. To address this issue, a neural network for computed tomography of multi-phase composites (MCCTNet) is established with an innovative architecture of adaptable configurations to recognize pixel-level mesoscale phases using X-ray Computed Tomography (X-CT) images of composites. Based on the original U-Net-like encoder-decoder structure, M to N layers are integrated with a novel attention module, termed the energy-derivative attention module (EDAM), which is designed to learn explicit feature representations for regional energy and boundary geometry. A pixel-level labeled dataset with 600 X-CT images covering diverse phases was established. The effectiveness of the proposed method and its superiority over existing methods were validated through comparative studies and ablation tests. EDAM significantly improves the recognition of small region-of-interests (RoIs), achieving in an improvement of 2. 29 %, 1. 16 %, 0. 92 %, and 0. 66 % for fracture, hole, cement paste, and aggregate, respectively. In addition, the proposed MCCTNet-1-4 embedded with EDAM demonstrated robust and consistent segmentation accuracy under Gaussian, salt-and-pepper, and speckle noise. Finally, practical applications including two-dimensional analysis, uniaxial compression simulations, and three-dimensional reconstruction based on the multi-phase segmentation results were conducted to verify the proposed method.

NeurIPS Conference 2025 Conference Paper

Federated Multi-armed Bandits with Efficient Bit-Level Communications

• Haoran Zhang
• Yang Xu
• Xuchuang Wang
• Hao-Xu Chen
• Hao Qiu
• Lin Yang
• Yang Gao

In this work, we study the federated multi-armed bandit (FMAB) problem, where a set of distributed agents collaboratively aim to minimize cumulative regret while interacting with a shared set of arms. Unlike traditional centralized bandit models, agents in FMAB settings are connected via a communication graph and cannot share data freely due to bandwidth limitations or privacy constraints. This raises a fundamental challenge: how to achieve optimal learning performance under stringent communication budgets. We propose a novel communication-efficient algorithm that decouples the learning process into two phases: one for eliminating suboptimal arms through early and frequent communication of key decisions, and another for refining global estimates using buffered, quantized, and differentially transmitted statistics. By carefully balancing the communication frequency and precision of shared information, our algorithm achieves the optimal individual regret bound $O(N^{-1}\log T)$ while significantly reducing the total number of communication rounds and transmitted bits. Theoretically, we derive tight upper bounds on both individual cumulative regret and group regret, and prove that our method asymptotically matches the lower bound of regret in federated settings. Experimental results on synthetic data validate the effectiveness of the proposed approach in various graph topologies and under heterogeneous feedback.

NeurIPS Conference 2025 Conference Paper

Finite-Sample Analysis of Policy Evaluation for Robust Average Reward Reinforcement Learning

• Yang Xu
• Washim Mondal
• Vaneet Aggarwal

We present the first finite-sample analysis of policy evaluation in robust average-reward Markov Decision Processes (MDPs). Prior work in this setting have established only asymptotic convergence guarantees, leaving open the question of sample complexity. In this work, we address this gap by showing that the robust Bellman operator is a contraction under a carefully constructed semi-norm, and developing a stochastic approximation framework with controlled bias. Our approach builds upon Multi-Level Monte Carlo (MLMC) techniques to estimate the robust Bellman operator efficiently. To overcome the infinite expected sample complexity inherent in standard MLMC, we introduce a truncation mechanism based on a geometric distribution, ensuring a finite expected sample complexity while maintaining a small bias that decays exponentially with the truncation level. Our method achieves the order-optimal sample complexity of $\tilde{\mathcal{O}}(\epsilon^{-2})$ for robust policy evaluation and robust average reward estimation, marking a significant advancement in robust reinforcement learning theory.

EAAI Journal 2025 Journal Article

Formal verification for multi-agent path execution in stochastic environments

• Xia Wang
• Jun Liu
• Chris D. Nugent
• Shaobing Xu
• Yang Xu

Multi-agent pathfinding aims to determine conflict-free paths for multiple agents in a shared environment. However, real-world uncertainties can disrupt preplanned paths, leading to delays and new conflicts. Addressing these challenges requires robust strategies for path execution and adjustment. While many multi-agent pathfinding algorithms have been proposed, this work does not introduce a new algorithm. Instead, it presents an adjustment solution based on a set of constraint rules and a priority strategy to avoid conflicts and deadlocks. Additionally, a Markov decision process model is developed, derived from the preplanned paths, and integrated with the adjustment solution to account for stochastic environmental uncertainties. A novel integrated framework is proposed for formally analyze and verify the reliability of multi-agent path execution and the robustness of the adjustment solution in stochastic environments, with formal verification achieved through a logic-based probabilistic model checker. The performance of the proposed framework is validated through various scenarios on the Flatland platform. Results demonstrate that the adjustment solution, based on the constraint rules, effectively mitigates conflicts and deadlocks, improving robustness. Furthermore, formal verification proves effective in assessing the reliability and robustness of multi-agent path execution under uncertainty.

NeurIPS Conference 2025 Conference Paper

Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm

• Yang Xu
• Swetha Ganesh
• Washim Mondal
• Qinbo Bai
• Vaneet Aggarwal

This paper investigates infinite-horizon average reward Constrained Markov Decision Processes (CMDPs) under general parametrized policies with smooth and bounded policy gradients. We propose a Primal-Dual Natural Actor-Critic algorithm that adeptly manages constraints while ensuring a high convergence rate. In particular, our algorithm achieves global convergence and constraint violation rates of $\tilde{\mathcal{O}}(1/\sqrt{T})$ over a horizon of length $T$ when the mixing time, $\tau_{\mathrm{mix}}$, is known to the learner. In absence of knowledge of $\tau_{\mathrm{mix}}$, the achievable rates change to $\tilde{\mathcal{O}}(1/T^{0. 5-\epsilon})$ provided that $T \geq \tilde{\mathcal{O}}\left(\tau_{\mathrm{mix}}^{2/\epsilon}\right)$. Our results match the theoretical lower bound for Markov Decision Processes and establish a new benchmark in the theoretical exploration of average reward CMDPs.

ECAI Conference 2025 Conference Paper

Masked Spectrum ViT with Cross-Scale Fusion for Universal Deepfake Detection

• Kaiwen Xu
• Xiyuan Hu
• Chen Chen 0036
• Yichao Zhou
• Yang Xu

With the rapid evolution of deepfake technologies, existing universal detection methods often fail to generalize when faced with new forgery patterns, as they tend to overfit specific artifacts found in the training data. To address this challenge, we propose a detection framework that integrates adaptive spectrum masking and cross-scale feature fusion, termed Masked Spectrum Vision Transformer with Cross-scale Fusion (MSViT-CF). Our approach introduces two key innovations: (1) Multi-band Artifact Enhancement Module (MAEM) reconstructs input images through wavelet decomposition and strategically perturbs high-frequency subbands via adaptive masking, amplifying subtle forgery traces while forcing the model to learn generalized artifact representations; (2) Dynamic Scale Fusion Transformer (DSFT) integrates multi-resolution frequency features through parallel convolutional-transformer pathways, dynamically weighting local spectrum anomalies and global structural inconsistencies. MAEM enhances artifact sensitivity through frequency-space discrepancy learning, while DSFT establishes cross-scale relationships between pixel-level irregularities and semantic-level inconsistencies via learnable attention gates. Experimental results demonstrate that MSViT-CF significantly outperforms existing state-of-the-art methods in detecting deepfake images generated by various GANs and diffusion models, exhibiting superior universality and robustness.

IROS Conference 2025 Conference Paper

PB-MOT: Pose-aware Association Boosted Online 3D Multi-Object Tracking

• Bo Pang
• Yang Xu
• Jiming Chen
• Liang Li

Robotic and autonomous driving platforms necessitate efficient 3D Multi-Object Tracking (MOT) that harmonizes geometric precision, motion robustness, and computational efficiency. Traditional 3D MOT approaches face critical challenges: geometric similarity metrics (e. g. , IoU-based) degrade at long ranges with high computational costs, while distance-based methods fail to capture object orientation and shape; the effects of occlusion and the intricate relative ego-object motion degrade tracking performance in dynamic scenes. To this end, we propose PB-MOT, an online framework integrating two key innovations: ego-motion-compensated state estimation that decouples dynamic interactions; and a rotated ellipse association algorithm unifying pose and shape-aware matching with adaptive distance constraints. Evaluations on the KITTI benchmark show that our PB-MOT achieves state-of-the-art performance with a HOTA score of 81. 94%, while running at an impressive 2, 402. 76 FPS on CPU. This enables real-time, high-fidelity perception and tracking for resource-constrained robotic systems.

IJCAI Conference 2025 Conference Paper

Rethinking Removal Attack and Fingerprinting Defense for Model Intellectual Property Protection: A Frequency Perspective

• Cheng Zhang
• Yang Xu
• Tingqiao Huang
• Zixing Zhang

Training deep neural networks is resource-intensive, making it crucial to protect their intellectual property from infringement. However, current model ownership resolution (MOR) methods predominantly address general removal attacks that involve weight modifications, with limited research considering alternative attack perspectives. In this work, we propose a frequency-based model ownership removal attack, grounded in a key observation: modifying a model's high-frequency coefficients does not significantly impact its performance but does alter its weights and decision boundary. This change invalidates the existing MOR methods. We further propose a frequency-based fingerprinting technique as a defense mechanism. By extracting frequency-domain characteristics instead of decision boundary or model weights, our fingerprinting defense effectively against the proposed frequency-based removal attack and demonstrates robustness against existing general removal attacks. The experimental results show that the frequency-based removal attack can easily defeat state-of-the-art white-box watermarking and fingerprinting schemes while preserving model performance, and the proposed defense method is also effective. Our code is released at: https: //github. com/huangtingqiao/RRA-IJCAI25.

AAAI Conference 2025 System Paper

RLLTE: Long-Term Evolution Project of Reinforcement Learning

• Mingqi Yuan
• Zequn Zhang
• Yang Xu
• Shihao Luo
• Bo Li
• Xin Jin
• Wenjun Zeng

We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application. Beyond delivering top-notch algorithm implementations, RLLTE also serves as a toolkit for developing algorithms. More specifically, RLLTE decouples the RL algorithms completely from the exploitation-exploration perspective, providing a large number of components to accelerate algorithm development and evolution. In particular, RLLTE is the first RL framework to build a comprehensive ecosystem, which includes model training, evaluation, deployment, benchmark hub, and large language model (LLM)-empowered copilot. RLLTE is expected to set standards for RL engineering practice and be highly stimulative for industry and academia. Our documentation, examples, and source code are available at https://github.com/RLE-Foundation/rllte.

NeurIPS Conference 2024 Conference Paper

Assembly Fuzzy Representation on Hypergraph for Open-Set 3D Object Retrieval

• Yang Xu
• Yifan Feng
• Jun Zhang
• Jun-Hai Yong
• Yue Gao

The lack of object-level labels presents a significant challenge for 3D object retrieval in the open-set environment. However, part-level shapes of objects often share commonalities across categories but remain underexploited in existing retrieval methods. In this paper, we introduce the Hypergraph-Based Assembly Fuzzy Representation (HARF) framework, which navigates the intricacies of open-set 3D object retrieval through a bottom-up lens of Part Assembly. To tackle the challenge of assembly isomorphism and unification, we propose the Hypergraph Isomorphism Convolution (HIConv) for smoothing and adopt the Isomorphic Assembly Embedding (IAE) module to generate assembly embeddings with geometric-semantic consistency. To address the challenge of open-set category generalization, our method employs high-order correlations and fuzzy representation to mitigate distribution skew through the Structure Fuzzy Reconstruction (SFR) module, by constructing a leveraged hypergraph based on local certainty and global uncertainty correlations. We construct three open-set retrieval datasets for 3D objects with part-level annotations: OP-SHNP, OP-INTRA, and OP-COSEG. Extensive experiments and ablation studies on these three benchmarks show our method outperforms current state-of-the-art methods.

AAAI Conference 2024 Conference Paper

Cautiously-Optimistic Knowledge Sharing for Cooperative Multi-Agent Reinforcement Learning

• Yanwen Ba
• Xuan Liu
• Xinning Chen
• Hao Wang
• Yang Xu
• Kenli Li
• Shigeng Zhang

While decentralized training is attractive in multi-agent reinforcement learning (MARL) for its excellent scalability and robustness, its inherent coordination challenges in collaborative tasks result in numerous interactions for agents to learn good policies. To alleviate this problem, action advising methods make experienced agents share their knowledge about what to do, while less experienced agents strictly follow the received advice. However, this method of sharing and utilizing knowledge may hinder the team's exploration of better states, as agents can be unduly influenced by suboptimal or even adverse advice, especially in the early stages of learning. Inspired by the fact that humans can learn not only from the success but also from the failure of others, this paper proposes a novel knowledge sharing framework called Cautiously-Optimistic kNowledge Sharing (CONS). CONS enables each agent to share both positive and negative knowledge and cautiously assimilate knowledge from others, thereby enhancing the efficiency of early-stage exploration and the agents' robustness to adverse advice. Moreover, considering the continuous improvement of policies, agents value negative knowledge more in the early stages of learning and shift their focus to positive knowledge in the later stages. Our framework can be easily integrated into existing Q-learning based methods without introducing additional training costs. We evaluate CONS in several challenging multi-agent tasks and find it excels in environments where optimal behavioral patterns are difficult to discover, surpassing the baselines in terms of convergence rate and final performance.

TMLR Journal 2024 Journal Article

Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions

• Jiayu Chen
• Bhargav Ganguly
• Yang Xu
• Yongsheng Mei
• Tian Lan
• Vaneet Aggarwal

Deep generative models (DGMs) have demonstrated great success across various domains, particularly in generating texts and images using models trained from offline data. Similarly, data-driven decision-making also necessitates learning a generator function from the offline data to serve as the policy. Applying DGMs in offline policy learning exhibits great potential, and numerous studies have explored in this direction. However, this field still lacks a comprehensive review and so developments of different branches are relatively independent. In this paper, we provide the first systematic review on the applications of DGMs for offline policy learning. We cover five mainstream DGMs, including Variational Auto-Encoders, Generative Adversarial Networks, Normalizing Flows, Transformers, and Diffusion Models, and their applications in both offline reinforcement learning (offline RL) and imitation learning (IL). Offline RL and IL are two main branches of offline policy learning and are widely-adopted techniques for sequential decision-making. Notably, for each type of DGM-based offline policy learning, we distill its fundamental scheme, categorize related works based on the usage of the DGM, and sort out the development process of algorithms in that field. In addition, we provide in-depth discussions on DGMs and offline policy learning as a summary, based on which we present our perspectives on future research directions. This work offers a hands-on reference for the research progress in DGMs for offline policy learning, and aims to inspire improved DGM-based offline RL or IL algorithms. For convenience, we maintain a paper list on https://github.com/LucasCJYSDL/DGMs-for-Offline-Policy-Learning.

AIJ Journal 2024 Journal Article

Is it possible to find the single nearest neighbor of a query in high dimensions?

• Kai Ming Ting
• Takashi Washio
• Ye Zhu
• Yang Xu
• Kaifeng Zhang

We investigate an open question in the study of the curse of dimensionality: Is it possible to find the single nearest neighbor of a query in high dimensions? Using the notion of (in)distinguishability to examine whether the feature map of a kernel is able to distinguish two distinct points in high dimensions, we analyze this ability of a metric-based Lipschitz continuous kernel as well as that of the recently introduced Isolation Kernel. Between the two kernels, we show that only Isolation Kernel has distinguishability and it performs consistently well in four tasks: indexed search for exact nearest neighbor search, anomaly detection using kernel density estimation, t-SNE visualization and SVM classification in both low and high dimensions, compared with distance, Gaussian and three other existing kernels.

ICML Conference 2024 Conference Paper

Layerwise Change of Knowledge in Neural Networks

• Xu Cheng 0005
• Lei Cheng 0006
• Zhaoran Peng
• Yang Xu
• Tian Han 0001
• Quanshi Zhang

This paper aims to explain how a deep neural network (DNN) gradually extracts new knowledge and forgets noisy features through layers in forward propagation. Up to now, although how to define knowledge encoded by the DNN has not reached a consensus so far, previous studies have derived a series of mathematical evidences to take interactions as symbolic primitive inference patterns encoded by a DNN. We extend the definition of interactions and, for the first time, extract interactions encoded by intermediate layers. We quantify and track the newly emerged interactions and the forgotten interactions in each layer during the forward propagation, which shed new light on the learning behavior of DNNs. The layer-wise change of interactions also reveals the change of the generalization capacity and instability of feature representations of a DNN.

AAAI Conference 2024 Conference Paper

Learning Multi-Modal Cross-Scale Deformable Transformer Network for Unregistered Hyperspectral Image Super-resolution

• Wenqian Dong
• Yang Xu
• Jiahui Qu
• Shaoxiong Hou

Hyperspectral image super-resolution (HSI-SR) is a technology to improve the spatial resolution of HSI. Existing fusion-based SR methods have shown great performance, but still have some problems as follows: 1) existing methods assume that the auxiliary image providing spatial information is strictly registered with the HSI, but images are difficult to be registered finely due to the shooting platforms, shooting viewpoints and the influence of atmospheric turbulence; 2) most of the methods are based on convolutional neural networks (CNNs), which is effective for local features but cannot utilize the global features. To this end, we propose a multi-modal cross-scale deformable transformer network (M2DTN) to achieve unregistered HSI-SR. Specifically, we formulate a spectrum-preserving based spatial-guided registration-SR unified model (SSRU) from the view of the realistic degradation scenarios. According to SSRU, we propose multi-modal registration deformable module (MMRD) to align features between different modalities by deformation field. In order to efficiently utilize the unique information between different modals, we design multi-scale feature transformer (MSFT) to emphasize the spatial-spectral features at different scales. In addition, we propose the cross-scale feature aggregation module (CSFA) to accurately reconstruct the HSI by aggregating feature information at different scales. Experiments show that M2DTN outperforms the-state-of-the-art HSI-SR methods. Code is obtainable at https://github.com/Jiahuiqu/M2DTN.

IJCAI Conference 2024 Conference Paper

Negative Prompt Driven Complementary Parallel Representation for Open-World 3D Object Retrieval

• Yang Xu
• Yifan Feng
• Yue Gao

The limited availability of supervised labels (positive information) poses a notable challenge for open-world retrieval. However, negative information is more easily obtained but remains underexploited in current methods. In this paper, we introduce the Negative Prompt Driven Complementary Parallel Representation (NPCP) framework, which navigates the complexities of open-world retrieval through the lens of Negative Prompts. Specifically, we employ the Parallel Exclusive Embedding (PEE) to effectively utilize the prompt information, bilaterally capturing both explicit negative and implicit positive signals. To address the challenges of embedding unification and generalization, our method leverages high-order correlations among objects through the Complementary Structure Tuning (CST), by constructing a complementary hypergraph based on bi-directional and cross-category correlations. We have developed four multimodal datasets for open-world 3D object retrieval with negative prompts: NPMN, NPAB, NPNT, and NPES. Extensive experiments and ablation studies on these four benchmarks demonstrate the superiority of our method over current state-of-the-art approaches.

NeurIPS Conference 2024 Conference Paper

Semi-Open 3D Object Retrieval via Hierarchical Equilibrium on Hypergraph

• Yang Xu
• Yifan Feng
• Jun Zhang
• Jun-Hai Yong
• Yue Gao

Existing open-set learning methods consider only the single-layer labels of objects and strictly assume no overlap between the training and testing sets, leading to contradictory optimization for superposed categories. In this paper, we introduce a more practical Semi-Open Environment setting for open-set 3D object retrieval with hierarchical labels, in which the training and testing set share a partial label space for coarse categories but are completely disjoint from fine categories. We propose the Hypergraph-Based Hierarchical Equilibrium Representation (HERT) framework for this task. Specifically, we propose the Hierarchical Retrace Embedding (HRE) module to overcome the global disequilibrium of unseen categories by fully leveraging the multi-level category information. Besides, tackling the feature overlap and class confusion problem, we perform the Structured Equilibrium Tuning (SET) module to utilize more equilibrial correlations among objects and generalize to unseen categories, by constructing a superposed hypergraph based on the local coherent and global entangled correlations. Furthermore, we generate four semi-open 3DOR datasets with multi-level labels for benchmarking. Results demonstrate that the proposed method can effectively generate the hierarchical embeddings of 3D objects and generalize them towards semi-open environments.

NeurIPS Conference 2024 Conference Paper

The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing

• Yang Xu
• Yihong Gu
• Cong Fang

Models are expected to engage in invariance learning, which involves distinguishing the core relations that remain consistent across varying environments to ensure the predictions are safe, robust and fair. While existing works consider specific algorithms to realize invariance learning, we show that model has the potential to learn invariance through standard training procedures. In other words, this paper studies the implicit bias of Stochastic Gradient Descent (SGD) over heterogeneous data and shows that the implicit bias drives the model learning towards an invariant solution. We call the phenomenon the implicit invariance learning. Specifically, we theoretically investigate the multi-environment low-rank matrix sensing problem where in each environment, the signal comprises (i) a lower-rank invariant part shared across all environments; and (ii) a significantly varying environment-dependent spurious component. The key insight is, through simply employing the large step size large-batch SGD sequentially in each environment without any explicit regularization, the oscillation caused by heterogeneity can provably prevent model learning spurious signals. The model reaches the invariant solution after certain iterations. In contrast, model learned using pooled SGD over all data would simultaneously learn both the invariant and spurious signals. Overall, we unveil another implicit bias that is a result of the symbiosis between the heterogeneity of data and modern algorithms, which is, to the best of our knowledge, first in the literature.

NeurIPS Conference 2024 Conference Paper

Towards the Dynamics of a DNN Learning Symbolic Interactions

• Qihan Ren
• Junpeng Zhang
• Yang Xu
• Yue Xin
• Dongrui Liu
• Quanshi Zhang

This study proves the two-phase dynamics of a deep neural network (DNN) learning interactions. Despite the long disappointing view of the faithfulness of post-hoc explanation of a DNN, a series of theorems have been proven [27] in recent years to show that for a given input sample, a small set of interactions between input variables can be considered as primitive inference patterns that faithfully represent a DNN's detailed inference logic on that sample. Particularly, Zhang et al. [41] have observed that various DNNs all learn interactions of different complexities in two distinct phases, and this two-phase dynamics well explains how a DNN changes from under-fitting to over-fitting. Therefore, in this study, we mathematically prove the two-phase dynamics of interactions, providing a theoretical mechanism for how the generalization power of a DNN changes during the training process. Experiments show that our theory well predicts the real dynamics of interactions on different DNNs trained for various tasks.

JBHI Journal 2023 Journal Article

A Novel and Efficient Surface Electromyography Decomposition Algorithm Using Local Spatial Information

• Yang Xu
• Yang Yu
• Miaojuan Xia
• Xinjun Sheng
• Xiangyang Zhu

Motor unit spike trains (MUSTs) decomposed from surface electromyography (sEMG) have been an emerging solution for neural interfacing, especially for the control of upper limb prosthetics. Accurate and efficient decomposition techniques are essential and desirable. However, most decomposition methods are designed for motor units (MUs) with global maximum of single or large muscle, while in general forearm muscles are usually small and slender with low global energy. Thus, we propose a novel approach using local spatial information towards more accurate and efficient sEMG decomposition of forearm muscles. A fast spatial spike detection method is proposed to replace the time-consuming iteration process of blind source separation (BSS) methods. Here, spatial distribution characteristics of motor unit action potential are leveraged to pre-classify the candidate MUs, and further to create initial MU templates, aiming to avoid repeating convergence to high-energy MUs. The results of both simulated and experimental sEMG signals show that low-energy MUs from small muscles are more easily found compared with conventional BSS algorithm. Specifically, the proposed method can identify more 40% reliable MUs while only 30% consuming time are needed. The outcomes provide a novel solution for more efficient sEMG decomposition, potentially paving the way of MUST-based non-invasive neural interface.

JBHI Journal 2023 Journal Article

Cumulative Spike Train Estimation for Muscle Excitation Assessment From Surface EMG Using Spatial Spike Detection

• Yang Xu
• Yang Yu
• Zeming Zhao
• Chen Chen
• Xinjun Sheng

Estimating cumulative spike train (CST) of motor units (MUs) from surface electromyography (sEMG) is essential for the effective control of neural interfaces. However, the limited accuracy of existing estimation methods greatly hinders the further development of neural interface. This paper proposes a simple but effective approach for identifying CST based on spatial spike detection from high-density sEMG. Specifically, we use a spatial sliding window to detect spikes according to the spatial propagation characteristics of the motor unit action potential, focusing on the spikes of activated MUs in a local area rather than those of a specific MU. We validated the effectiveness of our proposed method through an experiment involving wrist flexion/extension and pronation/supination, comparing it with a recognized CST estimation method and an MU decomposition based method. The results demonstrated that the proposed method obtained higher accuracy on multi-DoF wrist torque estimation leveraging the estimated CST compared to the other three methods. On average, the correlation coefficient (R) and the normalized root mean square error (nRMSE) between the estimation results and recorded force were 0. 96 $\pm$ 0. 03 and 10. 1% $\pm$ 3. 7%, respectively. Moreover, there was an extremely high interpretive extent between the CSTs of proposed method and the MU decomposition method. The outcomes reveal the superiority of the proposed method in identifying CSTs and can provide promising driven signals for neural interface.

NeurIPS Conference 2023 Conference Paper

FACE: Evaluating Natural Language Generation with Fourier Analysis of Cross-Entropy

• Zuhao Yang
• Yingfang Yuan
• Yang Xu
• SHUO ZHAN
• Huajun Bai
• Kefan Chen

Measuring the distance between machine-produced and human language is a critical open problem. Inspired by empirical findings from psycholinguistics on the periodicity of entropy in language, we propose FACE, a set of metrics based on Fourier Analysis of the estimated Cross-Entropy of language, for measuring the similarity between model-generated and human-written languages. Based on an open-ended generation task and the experimental data from previous studies, we find that FACE can effectively identify the human-model gap, scales with model size, reflects the outcomes of different sampling methods for decoding, correlates well with other evaluation metrics and with human judgment scores.

AAMAS Conference 2021 Conference Paper

Fast Adaptation to External Agents via Meta Imitation Counterfactual Regret Advantage

• Mingyue Zhang
• Zhi Jin
• Yang Xu
• Zehan Shen
• Kun Liu
• Keyu Pan

This paper focuses on the multi-agent credit assignment problem. We propose a novel multi-agent reinforcement learning algorithm called meta imitation counterfactual regret advantage (MICRA) and a three-phase framework for training, adaptation, and execution of MICRA. The key features are: (1) a counterfactual regret advantage is proposed to optimize the target agents’ policy; (2) a meta-imitator is designed to infer the external agents’ policies. Results show that MICRA outperforms state-of-the-art algorithms.

AAAI Conference 2020 Conference Paper

Multi-Feature Discrete Collaborative Filtering for Fast Cold-Start Recommendation

• Yang Xu
• Lei Zhu
• Zhiyong Cheng
• Jingjing Li
• Jiande Sun

Hashing is an effective technique to address the largescale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items. However, existing hashing-based recommendation methods still suffer from two important problems: 1) Their recommendation process mainly relies on the user-item interactions and single specific content feature. When the interaction history or the content feature is unavailable (the cold-start problem), their performance will be seriously deteriorated. 2) Existing methods learn the hash codes with relaxed optimization or adopt discrete coordinate descent to directly solve binary hash codes, which results in significant quantization loss or consumes considerable computation time. In this paper, we propose a fast cold-start recommendation method, called Multi-Feature Discrete Collaborative Filtering (MFDCF), to solve these problems. Specifically, a lowrank self-weighted multi-feature fusion module is designed to adaptively project the multiple content features into binary yet informative hash codes by fully exploiting their complementarity. Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations. Experiments on two public recommendation datasets demonstrate that MFDCF outperforms the stateof-the-arts on various aspects.

AAAI Conference 2019 Conference Paper

Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

• Wenjing Fu
• Zhaohui Peng
• Senzhang Wang
• Yang Xu
• Jin Li

As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, crossdomain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or implicit feedback from the auxiliary domain to the target domain. Although the side information of review texts and item contents has been proven to be useful in recommendation, most existing works only use one kind of side information and cannot deeply fuse this side information with ratings. In this paper, we propose a Review and Content based Deep Fusion Model named RC-DFM for crossdomain recommendation. We first extend Stacked Denoising Autoencoders (SDAE) to effectively fuse review texts and item contents with the rating matrix in both auxiliary and target domains. Through this way, the learned latent factors of users and items in both domains preserve more semantic information for recommendation. Then we utilize a multi-layer perceptron to transfer user latent factors between the two domains to address the data sparsity and cold start issues. Experimental results on real datasets demonstrate the superior performance of RC-DFM compared with state-of-the-art recommendation methods.Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

YNIMG Journal 2018 Journal Article

Optimal referencing for stereo-electroencephalographic (SEEG) recordings

• Guangye Li
• Shize Jiang
• Sivylla E. Paraskevopoulou
• Meng Wang
• Yang Xu
• Zehan Wu
• Liang Chen
• Dingguo Zhang

Stereo-electroencephalography (SEEG) is an intracranial recording technique in which depth electrodes are inserted in the brain as part of presurgical assessments for invasive brain surgery. SEEG recordings can tap into neural signals across the entire brain and thereby sample both cortical and subcortical sites. However, even though signal referencing is important for proper assessment of SEEG signals, no previous study has comprehensively evaluated the optimal referencing method for SEEG. In our study, we recorded SEEG data from 15 human subjects during a motor task, referencing them against the average of two white matter contacts (monopolar reference). We then subjected these signals to 5 different re-referencing approaches: common average reference (CAR), gray-white matter reference (GWR), electrode shaft reference (ESR), bipolar reference, and Laplacian reference. The results from three different signal quality metrics suggest the use of the Laplacian re-reference for study of local population-level activity and low-frequency oscillatory activity.

AAMAS Conference 2012 Conference Paper

An Information Sharing Algorithm For Large Dynamic Mobile Multi-agent Teams

• Linglong Zhu
• Yang Xu
• Paul Scerri
• Han Liang

In large-scale multi-agent systems, communicating effectively is necessary for agents to cooperatively achieve joint goals. Despite significant progress on the multi-agent information sharing problem, existing research has not adequately dealt with the case of very large teams coordinating using a wireless network with changing team structure and density, where messages are broadcast to multiple members of the team. In this paper, we developed a compact and effective information sharing approach for teams with a dynamically changing, broadcast communication medium. By using a matrix representation of information status, the network structure and information needs, the model allows efficient reasoning about communication in a single computation. Empirical simulation results show that the approach performs well in large team, and effectively balances sharing key information with minimizing communication costs.

EAAI Journal 2011 Journal Article

resolution method for a lattice-valued first-order logic

• Xingxing He
• Yang Xu
• Jun Liu
• Da Ruan

This paper focuses on resolution-based automated reasoning approaches in a lattice-valued first-order logic LF(X) with truth-values defined in a logical algebraic structure—lattice implication algebra (LIA), which aims at providing the logic foundation to represent and handle both imprecision and incomparability. In order to improve the efficiency of α - resolution approach proposed for LF(X), firstly the concepts of α - lock resolution principle and deduction are introduced for lattice-valued propositional logic LP(X) based on LIA, along with its soundness and weak completeness theorems. Then all the results are extended into LF(X) by using Lifting Lemma. Finally an α - lock resolution automated reasoning algorithm in LF(X) is proposed for the implementation purpose. This work provides a theoretical foundation for more efficient resolution-based automated reasoning algorithm in lattice-valued logic LF(X).

NeurIPS Conference 2010 Conference Paper

Inference and communication in the game of Password

• Yang Xu
• Charles Kemp

Communication between a speaker and hearer will be most efficient when both parties make accurate inferences about the other. We study inference and communication in a television game called Password, where speakers must convey secret words to hearers by providing one-word clues. Our working hypothesis is that human communication is relatively efficient, and we use game show data to examine three predictions. First, we predict that speakers and hearers are both considerate, and that both take the other’s perspective into account. Second, we predict that speakers and hearers are calibrated, and that both make accurate assumptions about the strategy used by the other. Finally, we predict that speakers and hearers are collaborative, and that they tend to share the cognitive burden of communication equally. We find evidence in support of all three predictions, and demonstrate in addition that efficient communication tends to break down when speakers and hearers are placed under time pressure.

AAMAS Conference 2006 Conference Paper

Comparing Market and Token-Based Coordination

• Yang Xu
• Paul Scerri
• Katia Sycara
• Michael Lewis

AAMAS Conference 2006 Conference Paper

Scalable and Reliable Data Delivery in Mobile Ad Hoc Sensor Networks

• Bin Yu
• Paul Scerri
• Katia Sycara
• Yang Xu
• Michael Lewis

AAMAS Conference 2004 Conference Paper

Scaling Teamwork to Very Large Teams

• Scerri Paul
• Yang Xu
• Elizabeth Liao
• Guoming Lai
• Katia Sycara