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Jiming Chen

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21 papers

2 author rows

IROS Conference 2025 Conference Paper

A Hybrid Mapping Method: Balancing Efficiency and Intuitiveness in Lateral Teleoperation

Yuwei Xie
Ruize Wang
Jiming Chen
Gaofeng Li

Mobile manipulators integrate the locomotion flexibility of quadruped robots with the operational capabilities of robotic manipulators. This integrated system is particularly effective for teleoperating explosive ordnance disposal (EOD) tasks in hazardous environments, enabling the safe handling of explosive devices. However, when the quadruped operates in narrow corridors or cluttered spaces, its ability to reposition is limited. This limitation, combined with targets located laterally relative to the robot, poses critical challenges for achieving rapid and intuitive teleoperation of the manipulator. Existing manipulator mapping methods either fail to support lateral teleoperation or lack proper coordinate transformations, leading to mismatches between the intended and actual movement directions of the leader and follower devices. This reduces operational intuitiveness and increases the cognitive load on human operators. To overcome these issues, we propose a hybrid mapping method that combines joint-space velocity control with Cartesian-space control. This method leverages joint-space velocity commands for rapid manipulator reorientation, while employing Cartesian-space commands to achieve precise end-effector teleoperation. Furthermore, we introduce a virtual base coordinate frame that adaptively adjusts in response to the manipulator’s reorientation. This adaptive compensation ensures that the visual feedback from the camera mounted on the end-effector remains consistent and intuitive. The proposed method was validated through experiments on a quadruped robot equipped with a manipulator in an EOD scenario. Results demonstrated significant improvements, including 100% success rate, 43. 9% task duration reduction, and 31. 7% NASA-TLX score decrease, indicating decreased cognitive load and enhanced task efficiency compared to baseline methods.

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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.

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NeurIPS Conference 2025 Conference Paper

FairDD: Fair Dataset Distillation

Qihang Zhou
ShenHao Fang
Shibo He
Wenchao Meng
Jiming Chen

Condensing large datasets into smaller synthetic counterparts has demonstrated its promise for image classification. However, previous research has overlooked a crucial concern in image recognition: ensuring that models trained on condensed datasets are unbiased towards protected attributes (PA), such as gender and race. Our investigation reveals that dataset distillation fails to alleviate the unfairness towards minority groups within original datasets. Moreover, this bias typically worsens in the condensed datasets due to their smaller size. To bridge the research gap, we propose a novel fair dataset distillation (FDD) framework, namely FairDD, which can be seamlessly applied to diverse matching-based DD approaches (DDs), requiring no modifications to their original architectures. The key innovation of FairDD lies in synchronously matching synthetic datasets to PA-wise groups of original datasets, rather than indiscriminate alignment to the whole distributions in vanilla DDs, dominated by majority groups. This synchronized matching allows synthetic datasets to avoid collapsing into majority groups and bootstrap their balanced generation to all PA groups. Consequently, FairDD could effectively regularize vanilla DDs to favor biased generation toward minority groups while maintaining the accuracy of target attributes. Theoretical analyses and extensive experimental evaluations demonstrate that FairDD significantly improves fairness compared to vanilla DDs, with a promising trade-off between fairness and accuracy. Its consistent superiority across diverse DDs, spanning Distribution and Gradient Matching, establishes it as a versatile FDD approach.

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AAAI Conference 2025 Conference Paper

Fed-DFA: Federated Distillation for Heterogeneous Model Fusion Through the Adversarial Lens

Zichen Wang
Feng Yan
Tianyi Wang
Cong Wang
Yuanchao Shu
Peng Cheng
Jiming Chen

Most of the federated learning techniques are limited to homogeneous model fusion. With the rapid growth of smart applications on resource-constrained edge devices, it becomes a barrier to accommodate their heterogeneous computing power and memory in the real world. Federated Distillation is a promising alternative to enable aggregation from heterogeneous models. However, the effectiveness of knowledge transfer still remains elusive under the shadow of distinct representation power from heterogeneous models. In this paper, we approach from an adversarial perspective to characterize the decision boundaries during distillation. By leveraging K-step PGD attacks, we successfully model the dynamics of the closest boundary points and establish a quantitative connection between the predictive uncertainty and boundary margin. Based on these findings, we further propose a new loss function to make the distillation attend to samples close to the decision boundaries, thus learning from more informed logit distributions. The extensive experiments over CIFAR-10/100 and Tiny-ImageNet demonstrate about 0.5-3.5% improvement of accuracy under different IID and non-IID settings, with only a small increment of computational overhead.

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ICRA Conference 2025 Conference Paper

Gate-Aware Online Planning for Two-Player Autonomous Drone Racing

Fangguo Zhao
Jiahao Mei
Jin Zhou
Yuanyi Chen
Jiming Chen
Shuo Li

The flying speed of autonomous quadrotors has increased significantly in the field of autonomous drone racing. However, most research primarily focuses on the aggressive flight of a single quadrotor, simplifying the racing gate traversal problem to a waypoint passing problem that neglects the orientations of the racing gates or implicitly considers the waypoint direction during path planning. In this paper, we propose a systematic method called Pairwise Model Predictive Control (PMPC) that can guide two quadrotors online to navigate racing gates with minimal time and without collisions. The flight task is initially simplified as a point-mass model waypoint passing problem to provide time optimal reference through an efficient two-step velocity search method. Subsequently, we utilize the spatial configuration of the racing track to compute the optimal heading at each gate, maximizing the visibility of subsequent gates for the quadrotors. To address varying gate orientations, we introduce a novel Magnetic Induction Line-based spatial curve to guide the quadrotors through racing gates of different orientations. Furthermore, we formulate a nonlinear optimization problem that uses the point-mass trajectory as initial values and references to enhance solving efficiency. The feasibility of the proposed method is validated through both simulation and real-world experiments. In real-world tests, the two quadrotors achieved a top speed of $6. 1m/s$ on a 7-waypoint racing track within a compact flying arena of $5m\times 4m\times 2m$.

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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.

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NeurIPS Conference 2025 Conference Paper

RFMPose: Generative Category-level Object Pose Estimation via Riemannian Flow Matching

Wenzhe Ouyang
Qi Ye
Jinghua Wang
Zenglin Xu
Jiming Chen

We introduce RFMPose, a novel generative framework for category-level 6D object pose estimation that learns deterministic pose trajectories through Riemannian Flow Matching (RFM). Existing discriminative approaches struggle with multi-hypothesis predictions (e. g. , symmetry ambiguities) and often require specialized network architectures. RFMPose advances this paradigm through three key innovations: (1) Ensuring geometric consistency via geodesic interpolation on Riemannian manifolds combined with bi-invariant metric constraints; (2) Alleviating symmetry-induced ambiguities through Riemannian Optimal Transport for probability mass redistribution without ad-hoc design; (3) Enabling end-to-end likelihood estimation through Hutchinson trace approximation, thereby eliminating auxiliary model dependencies. Extensive experiments on the Omni6DPose demonstrate state-of-the-art performance of the proposed method, with significant improvements of $\textbf{+4. 1}$ in $\mathrm{\textbf{IoU}_{25}}$ and $\textbf{+2. 4}$ in $\textbf{5°2cm}$ metrics compared to prior generative approaches. Furthermore, the proposed RFM framework exhibits robust sim-to-real transfer capabilities and facilitates pose tracking extensions with minimal architectural adaptation.

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ICRA Conference 2025 Conference Paper

Safety-Critical Online Quadrotor Trajectory Planner for Agile Flights in Unknown Environments

Jiazhe Yuan
Dongcheng Cao
Jiahao Mei
Jiming Chen
Shuo Li

Autonomous high-speed flight in unknown, clut-tered environments is essential for a variety of quadrotor applications, such as inspection, search, and rescue. In this study, we propose a novel trajectory planner designed to achieve efficient, high-speed, collision-free flights in such environments. The proposed approach begins by generating a safe flight corridor based on the path found by Lazy Theta*, representing the safe regions with polytopic sets. These sets are then used to define discrete-time control barrier function (DCBF), ensuring the quadrotor stays within safe bounds during flight. By selecting a single waypoint ahead of the quadrotor on the path as the next waypoint, the trajectory is optimized by considering both the total flight time and safety constraints. Extensive simulations and real-world experiments have confirmed our method's feasibility, demonstrating its capability for high-speed performance and reliable obstacle avoidance. [video 4 4 https://www.youtube.com/playlist?list=PLJFduoH7QICOhcIX3JFsZwB4IgS4_-sPt]

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NeurIPS Conference 2024 Conference Paper

A Consistency-Aware Spot-Guided Transformer for Versatile and Hierarchical Point Cloud Registration

Renlang Huang
Yufan Tang
Jiming Chen
Liang Li

Deep learning-based feature matching has shown great superiority for point cloud registration in the absence of pose priors. Although coarse-to-fine matching approaches are prevalent, the coarse matching of existing methods is typically sparse and loose without consideration of geometric consistency, which makes the subsequent fine matching rely on ineffective optimal transport and hypothesis-and-selection methods for consistency. Therefore, these methods are neither efficient nor scalable for real-time applications such as odometry in robotics. To address these issues, we design a consistency-aware spot-guided Transformer (CAST), which incorporates a spot-guided cross-attention module to avoid interfering with irrelevant areas, and a consistency-aware self-attention module to enhance matching capabilities with geometrically consistent correspondences. Furthermore, a lightweight fine matching module for both sparse keypoints and dense features can estimate the transformation accurately. Extensive experiments on both outdoor LiDAR point cloud datasets and indoor RGBD point cloud datasets demonstrate that our method achieves state-of-the-art accuracy, efficiency, and robustness.

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ICRA Conference 2024 Conference Paper

iBoW3D: Place Recognition Based on Incremental and General Bag of Words in 3D Scans

Yuxiaotong Lin
Jiming Chen
Liang Li

Existing methods for place recognition in 3D point clouds either ignore partial structure information by converting 3D scans to 2D images or construct constrained bag-of-words (BoW) representations reliant on specific feature extraction algorithms. In this paper, we propose a novel method based on incremental and general bag of words. Incorporating an adaptable keypoint and 3D local feature extraction method, we employ an incremental BoW model that is updated regularly. This enables a coarse-to-fine candidate selection from the database. And a revisit can be identified following geometric verification. In addition, we propose a new supplementary metric that addresses the leaving-out issue of the conventional metric, enhancing the identification of true loops. Employing a state-of-the-art (SOTA) keypoint and feature extraction algorithm, we evaluate our method as well as SOTA place recognition methods using diverse datasets with varying qualities. Experimental results demonstrate that our method outperforms the baselines across all three datasets, showcasing robust performance and notable generalization capabilities.

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AAAI Conference 2024 Conference Paper

In-Hand 3D Object Reconstruction from a Monocular RGB Video

Shijian Jiang
Qi Ye
Rengan Xie
Yuchi Huo
Xiang Li
Yang Zhou
Jiming Chen

Our work aims to reconstruct a 3D object that is held and rotated by a hand in front of a static RGB camera. Previous methods that use implicit neural representations to recover the geometry of a generic hand-held object from multi-view images achieved compelling results in the visible part of the object. However, these methods falter in accurately capturing the shape within the hand-object contact region due to occlusion. In this paper, we propose a novel method that deals with surface reconstruction under occlusion by incorporating priors of 2D occlusion elucidation and physical contact constraints. For the former, we introduce an object amodal completion network to infer the 2D complete mask of objects under occlusion. To ensure the accuracy and view consistency of the predicted 2D amodal masks, we devise a joint optimization method for both amodal mask refinement and 3D reconstruction. For the latter, we impose penetration and attraction constraints on the local geometry in contact regions. We evaluate our approach on HO3D and HOD datasets and demonstrate that it outperforms the state-of-the-art methods in terms of reconstruction surface quality, with an improvement of 52% on HO3D and 20% on HOD. Project webpage: https://east-j.github.io/ihor.

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ICRA Conference 2024 Conference Paper

KDD-LOAM: Jointly Learned Keypoint Detector and Descriptors Assisted LiDAR Odometry and Mapping

Renlang Huang
Minglei Zhao
Jiming Chen
Liang Li

Sparse keypoint matching based on distinct 3D feature representations can improve the efficiency and robustness of point cloud registration. Existing learning-based 3D descriptors and keypoint detectors are either independent or loosely coupled, so they cannot fully adapt to each other. In this work, we propose a tightly coupled keypoint detector and descriptor (TCKDD) based on a multi-task fully convolutional network with a probabilistic detection loss. In particular, this self-supervised detection loss fully adapts the keypoint detector to any jointly learned descriptors and benefits the self-supervised learning of descriptors. Extensive experiments on both indoor and outdoor datasets show that our TCKDD achieves state-of- the-art performance in point cloud registration. Furthermore, we design a keypoint detector and descriptors-assisted LiDAR odometry and mapping framework (KDD-LOAM), whose real-time odometry relies on keypoint descriptor matching-based RANSAC. The sparse keypoints are further used for efficient scan-to-map registration and mapping. Experiments on KITTI dataset demonstrate that KDD-LOAM significantly surpasses LOAM and shows competitive performance in odometry.

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ICRA Conference 2024 Conference Paper

LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization

Mingrui Liu
Xinyang Tang
Yeqiang Qian
Jiming Chen
Liang Li

Precise and long-term stable localization is essential in parking lots for tasks like autonomous driving or autonomous valet parking, etc. Existing methods rely on a fixed and memory-inefficient map, which lacks robust data association approaches. And it is not suitable for precise localization or long-term map maintenance. In this paper, we propose a novel mapping, localization, and map update system based on ground semantic features, utilizing low-cost cameras. We present a precise and lightweight parameterization method to establish improved data association and achieve accurate localization at centimeter-level. Furthermore, we propose a novel map update approach by implementing high-quality data association for parameterized semantic features, allowing continuous map update and refinement during re-localization, while maintaining centimeter-level accuracy. We validate the performance of the proposed method in real-world experiments and compare it against state-of-the-art algorithms. The proposed method achieves an average accuracy improvement of 5cm during the registration process. The generated maps consume only a compact size of 450 KB/km and remain adaptable to evolving environments through continuous update.

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NeurIPS Conference 2024 Conference Paper

PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection

Qihang Zhou
Jiangtao Yan
Shibo He
Wenchao Meng
Jiming Chen

Zero-shot (ZS) 3D anomaly detection is a crucial yet unexplored field that addresses scenarios where target 3D training samples are unavailable due to practical concerns like privacy protection. This paper introduces PointAD, a novel approach that transfers the strong generalization capabilities of CLIP for recognizing 3D anomalies on unseen objects. PointAD provides a unified framework to comprehend 3D anomalies from both points and pixels. In this framework, PointAD renders 3D anomalies into multiple 2D renderings and projects them back into 3D space. To capture the generic anomaly semantics into PointAD, we propose hybrid representation learning that optimizes the learnable text prompts from 3D and 2D through auxiliary point clouds. The collaboration optimization between point and pixel representations jointly facilitates our model to grasp underlying 3D anomaly patterns, contributing to detecting and segmenting anomalies of unseen diverse 3D objects. Through the alignment of 3D and 2D space, our model can directly integrate RGB information, further enhancing the understanding of 3D anomalies in a plug-and-play manner. Extensive experiments show the superiority of PointAD in ZS 3D anomaly detection across diverse unseen objects.

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EWRL Workshop 2024 Workshop Paper

Private Online Learning in Adversarial MDPs: Full-Information and Bandit

Shaojie Bai
Lanting Zeng
Chengcheng Zhao
Xiaoming Duan
Mohammad Sadegh Talebi
Peng Cheng
Jiming Chen

We study learning adversarial Markov decision process (MDP) in the episodic setting under the constraint of differential privacy (DP). This is motivated by the widespread applications of reinforcement learning (RL) in non-stationary and even adversarial scenarios, where protecting users' sensitive information is vital. We first propose two efficient frameworks for adversarial MDPs, spanning full-information and bandit settings. Within each framework, we consider both Joint DP (JDP), where a central agent is trusted to protect the sensitive data, and Local DP (LDP), where the information is protected directly on the user side. Then, we design novel privacy mechanisms to privatize the stochastic transition and adversarial losses. By instantiating such privacy mechanisms to satisfy JDP and LDP requirements, we obtain near-optimal regret guarantees for both frameworks. To our knowledge, these are the first algorithms to tackle the challenge of private learning in adversarial MDPs.

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ICRA Conference 2024 Conference Paper

SAGE-ICP: Semantic Information-Assisted ICP

Jiaming Cui
Jiming Chen
Liang Li

Robust and accurate pose estimation in unknown environments is an essential part of robotic applications. We focus on LiDAR-based point-to-point ICP combined with effective semantic information. This paper proposes a novel semantic information-assisted ICP method named SAGE-ICP, which leverages semantics in odometry. The semantic information for the whole scan is timely and efficiently extracted by a 3D convolution network, and these point-wise labels are deeply involved in every part of the registration, including semantic voxel downsampling, data association, adaptive local map, and dynamic vehicle removal. Unlike previous semantic-aided approaches, the proposed method can improve localization accuracy in large-scale scenes even if the semantic information has certain errors. Experimental evaluations on KITTI and KITTI-360 show that our method outperforms the baseline methods, and improves accuracy while maintaining real-time performance, i. e. , runs faster than the sensor frame rate.

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AAMAS Conference 2024 Conference Paper

Stability of Weighted Majority Voting under Estimated Weights

Shaojie Bai
Dongxia Wang
Tim Muller
Peng Cheng
Jiming Chen

Weighted Majority Voting (WMV) is a well-known decision making rule. The weights of sources are determined by the probabilities that sources provide accurate information (trustworthiness). However, in reality, the trustworthiness is usually not a known quantity to the decision maker – they have to rely on an estimate called trust. An algorithm that computes trust is called unbiased when it has the property that it does not systematically overestimate or underestimate the trustworthiness. To formally analyze the uncertainty to the decision process brought by such unbiased trust values, we introduce and analyze two important properties of WMV: Stability of Correctness and Stability of Optimality. Stability of Correctness measures the difference between the decision accuracy that the decision maker believes he can achieve and the accuracy he actually achieves. We prove Stability of Correctness absolutely holds for WMV – the difference is 0. Stability of Optimality measures the difference between the actual accuracy of decisions made using trust values, and those made using trustworthiness values. We find a relatively tight upper bound on the Stability of Optimality, meaning that, although using (unbiased) trust values is suboptimal compared to using the true trustworthiness values, the difference is small. Meanwhile, a counter-intuitive observation is that while distributions of trustworthiness influence the Stability of Optimality, the number of sources barely influences it. We also provide an overview of how sensitive decision accuracy is to the changes in trust and trustworthiness.

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ICRA Conference 2024 Conference Paper

The Joint-Space Reconstruction of Human Fingers by using a Highly Under-Actuated Exoskeleton

Yuan Su
Gaofeng Li
Yongsheng Deng
Ioannis Sarakoglou
Nikos G. Tsagarakis
Jiming Chen

Hand motion tracking is essential in many fields, e. g. , immersive virtual reality, teleoperation of robotic hand, and hand rehabilitation of stroke patient, as human hand plays a crucial role in our daily life. The highly under-actuated hand exoskeleton, which can track the 6-DoF motions of each fingertip via a highly under-actuated kinematic chain, exhibits many benefits in wearability and portability over other solutions. However, due to the non-anthropomorphic linkage, this hand exoskeleton also encounters difficulties in measuring human-finger’s joint angles. While the joint-space is important in many scenarios, such as teleoperating a robotic hand with anthropomorphic kinematics but with different size to human. Here we proposed a new method to reconstruct the human finger joints by using a highly under-actuated hand exoskeleton. Our key contribution is the arc-fitting algorithm, which is able to calibrate the misalignment between the exoskeleton’s and the human-finger’s base frames and estimate the length of human’s phalanxes, by using the fingertip’s circular motions. With knowing the aforementioned informations, the joint angles can be reconstructed in high precision based on the inverse kinematics models of human fingers. Furthermore, our proposed method is compared with a baseline method, in which the joint angles obtained by a motion capture system are served as ground-truth. The results demonstrate that our proposed method exhibits excellent performance in reconstructing finger’s joint configurations.

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IJCAI Conference 2023 Conference Paper

Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint

Haoming Li
Xinzhuo Lin
Yang Zhou
Xiang Li
Yuchi Huo
Jiming Chen
Qi Ye

3D grasp synthesis generates grasping poses given an input object. Existing works tackle the problem by learning a direct mapping from objects to the distributions of grasping poses. However, because the physical contact is sensitive to small changes in pose, the high-nonlinear mapping between 3D object representation to valid poses is considerably non-smooth, leading to poor generation efficiency and restricted generality. To tackle the challenge, we introduce an intermediate variable for grasp contact areas to constrain the grasp generation; in other words, we factorize the mapping into two sequential stages by assuming that grasping poses are fully constrained given contact maps: 1) we first learn contact map distributions to generate the potential contact maps for grasps; 2) then learn a mapping from the contact maps to the grasping poses. Further, we propose a penetration-aware optimization with the generated contacts as a consistency constraint for grasp refinement. Extensive validations on two public datasets show that our method outperforms state-of-the-art methods regarding grasp generation on various metrics.

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AAAI Conference 2023 Conference Paper

Detecting Multivariate Time Series Anomalies with Zero Known Label

Qihang Zhou
Jiming Chen
Haoyu Liu
Shibo He
Wenchao Meng

Multivariate time series anomaly detection has been extensively studied under the one-class classification setting, where a training dataset with all normal instances is required. However, preparing such a dataset is very laborious since each single data instance should be fully guaranteed to be normal. It is, therefore, desired to explore multivariate time series anomaly detection methods based on the dataset without any label knowledge. In this paper, we propose MTGFlow, an unsupervised anomaly detection approach forMultivariate Time series anomaly detection via dynamic Graph and entityaware normalizing Flow, leaning only on a widely accepted hypothesis that abnormal instances exhibit sparse densities than the normal. However, the complex interdependencies among entities and the diverse inherent characteristics of each entity pose significant challenges to density estimation, let alone to detect anomalies based on the estimated possibility distribution. To tackle these problems, we propose to learn the mutual and dynamic relations among entities via a graph structure learning model, which helps to model the accurate distribution of multivariate time series. Moreover, taking account of distinct characteristics of the individual entities, an entity-aware normalizing flow is developed to describe each entity into a parameterized normal distribution, thereby producing fine-grained density estimation. Incorporating these two strategies, MTGFlow achieves superior anomaly detection performance. Experiments on five public datasets with seven baselines are conducted, MTGFlow outperforms the SOTA methods by up to 5.0 AUROC%.

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AAMAS Conference 2023 Conference Paper

Stability of Weighted Majority Voting under Estimated Weights

Shaojie Bai
Dongxia Wang
Tim Muller
Peng Cheng
Jiming Chen

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