It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, which may not correctly characterize the real relationships among labels. Besides, label correlations are normally used to regularize the hypothesis space, while the final predictions are not explicitly correlated. In this paper, we suggest that for each individual label, the final prediction involves the collaboration between its own prediction and the predictions of other labels. Based on this assumption, we first propose a novel method to learn the label correlations via sparse reconstruction in the label space. Then, by seamlessly integrating the learned label correlations into model training, we propose a novel multi-label learning approach that aims to explicitly account for the correlated predictions of labels while training the desired model simultaneously. Extensive experimental results show that our approach outperforms the state-of-the-art counterparts.
College of Comp. & Inf. Sci., Southwest University（计算机与信息科学学院，西南大学）；College of Elec. & Inf. Eng., Beijing Univ. of Civil Eng. and Arch.（电子与信息工程学院，北京建筑大学）
Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels, and only some of the labels are relevant. The PML problem is practical in real-world scenarios, as it is difficult and even impossible to obtain precisely labeled samples. Several PML solutions have been proposed to combat with the prone misled by the irrelevant labels concealed in the candidate labels, but they generally focus on the smoothness assumption in feature space or low-rank assumption in label space, while ignore the negative information between features a…
Center for Vision Technologies, SRI International（SRI国际视觉技术中心）；Center for Education Research and Innovation, SRI International（SRI国际教育研究与创新中心）
K-12 classrooms consistently integrate collaboration as part of their learning experiences. However, owing to large classroom sizes, teachers do not have the time to properly assess each student and give them feedback. In this paper we propose using simple deep-learning-based machine learning models to automatically determine the overall collaboration quality of a group based on annotations of individual roles and individual level behavior of all the students in the group. We come across the following challenges when building these models: 1) Limited training data, 2) Severe class label imbala…
Duke University（杜克大学）；University of Minnesota-Twin Cities（明尼苏达大学双城分校）
Collaborations among multiple organizations, such as financial institutions, medical centers, and retail markets in decentralized settings are crucial to providing improved service and performance. However, the underlying organizations may have little interest in sharing their local data, models, and objective functions. These requirements have created new challenges for multi-organization collaboration. In this work, we propose Gradient Assisted Learning (GAL), a new method for multiple organizations to assist each other in supervised learning tasks without sharing local data, models, and obj…
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；HEC Montreal（蒙特利尔高等商学院）；Montreal Institute of Learning Algorithms（蒙特利尔学习算法研究所）；Peking University（北京大学）
CoHuB: A Simulation Benchmark for Multi-Humanoid Collaboration
Yonsei University（延世大学）；Gwangju Institute of Science and Technology（光州科学技术院）；University of California, Berkeley（加州大学伯克利分校）；Seoul National University（首尔大学）
DPMT: Dual Process Multi-scale Theory of Mind Framework for Real-time Human-AI Collaboration
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（认知与决策智能复杂系统重点实验室，自动化研究所，中国科学院）；School of Future Technology, University of Chinese Academy of Sciences（未来技术学院，中国科学院大学）；Nankai University（南开大学）；Tsinghua University（清华大学）
Duke University（杜克大学）；University of Minnesota-Twin Cities（明尼苏达大学双城分校）
Recommender Systems (RSs) are operated locally by different organizations in many realistic scenarios. If various organizations can fully share their data and perform computation in a centralized manner, they may significantly improve the accuracy of recommendations. However, collaborations among multiple organizations in enhancing the performance of recommendations are primarily limited due to the difficulty of sharing data and models. To address this challenge, we propose Decentralized Multi-Target Cross-Domain Recommendation (DMTCDR) with Multi-Target Assisted Learning (MTAL) and Assisted A…
ATOM: Instantiating Budget-Controllable Multi-Agent Collaboration via Nucleus-Electron Hierarchy
Zhejiang University（浙江大学）；Ningbo Global Innovation Center, Zhejiang University（浙江大学宁波全球创新中心）；Zhejiang Key Laboratory of Digital-Intelligence Service Technology（浙江省数字智能服务技术重点实验室）；Xidian University（西安电子科技大学）
CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness
School of Computer Science & Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence（交通数据挖掘与具身智能北京市重点实验室）
University of Illinois at Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）；Snap Inc.（Snap公司）
Multi-view networks are broadly present in real-world applications. In the meantime, network embedding has emerged as an effective representation learning approach for networked data. Therefore, we are motivated to study the problem of multi-view network embedding with a focus on the optimization objectives that are specific and important in embedding this type of network. In our practice of embedding real-world multi-view networks, we explicitly identify two such objectives, which we refer to as preservation and collaboration. The in-depth analysis of these two objectives is discussed through…
A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）
Peking University（北京大学）；Beijing Institute for GAI (BIGAI)（北京通用人工智能研究院）
This paper presents a multi-agent reinforcement learning (MARL) scheme for proactive Multi-Camera Collaboration in 3D Human Pose Estimation in dynamic human crowds. Traditional fixed-viewpoint multi-camera solutions for human motion capture (MoCap) are limited in capture space and susceptible to dynamic occlusions. Active camera approaches proactively control camera poses to find optimal viewpoints for 3D reconstruction. However, current methods still face challenges with credit assignment and environment dynamics. To address these issues, our proposed method introduces a novel Collaborative T…
