ECML-PKDD 2024
272 papers
A Deep Cut into Split Federated Self-Supervised Learning
Marcin Przewiezlikowski, Marcin Osial, Bartosz Zielinski, Marek Smieja A Human-Centric Assessment of the Usefulness of Attribution Methods in Computer Vision
Wiem Ben Rim, Ammar Shaker, Zhao Xu, Kiril Gashteovski, Bhushan Kotnis, Carolin Lawrence, Jürgen Quittek, Sascha Saralajew A Merge Sort Based Ranking System for the Evaluation of Large Language Models
Chenchen Li, Linfeng Shi, Chunyi Zhou, Zhaoxin Huan, Chengfu Tang, Xiaolu Zhang, Xudong Wang, Jun Zhou, Song Liu A Theoretically Grounded Extension of Universal Attacks from the Attacker's Viewpoint
Jordan Patracone, Paul Viallard, Emilie Morvant, Gilles Gasso, Amaury Habrard, Stéphane Canu A Unified Contrastive Loss for Self-Training
Aurélien Gauffre, Julien Horvat, Massih-Reza Amini A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation
Yongkang Liu, Ercong Nie, Shi Feng, Zheng Hua, Zifeng Ding, Daling Wang, Yifei Zhang, Hinrich Schütze Adaptively Denoising Graph Neural Networks for Knowledge Distillation
Yuxin Guo, Cheng Yang, Chuan Shi, Ke Tu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou Advancing Graph Counterfactual Fairness Through Fair Representation Learning
Zichong Wang, Zhibo Chu, Ronald Blanco, Zhong Chen, Shu-Ching Chen, Wenbin Zhang AEMLO: AutoEncoder-Guided Multi-Label Oversampling
Ao Zhou, Bin Liu, Jin Wang, Kaiwei Sun, Kelin Liu ART: Actually Robust Training
Sebastian Chwilczynski, Kacper Trebacz, Karol Cyganik, Mateusz Malecki, Dariusz Brzezinski Backdoor Attacks with Input-Unique Triggers in NLP
Xukun Zhou, Jiwei Li, Tianwei Zhang, Lingjuan Lyu, Muqiao Yang, Jun He Boosting Long-Tail Data Classification with Sparse Prototypical Networks
Alexei Figueroa, Jens-Michalis Papaioannou, Conor Fallon, Alexandra Bekiaridou, Keno K. Bressem, Stavros Zanos, Felix A. Gers, Wolfgang Nejdl, Alexander Löser Boosting Patient Representation Learning via Graph Contrastive Learning
Zhenhao Zhang, Yuxi Liu, Jiang Bian, Antonio Jimeno-Yepes, Jun Shen, Fuyi Li, Guodong Long, Flora D. Salim Boosting Protein Language Models with Negative Sample Mining
Yaoyao Xu, Xinjian Zhao, Xiaozhuang Song, Benyou Wang, Tianshu Yu Bundle Recommendation with Item-Level Causation-Enhanced Multi-View Learning
Huy-Son Nguyen, Tuan-Nghia Bui, Long-Hai Nguyen, Hung Hoang, Cam-Van Thi Nguyen, Hoang-Quynh Le, Duc-Trong Le CAM-Based Methods Can See Through Walls
Magamed Taimeskhanov, Ronan Sicre, Damien Garreau Continual Neural Computation
Matteo Tiezzi, Simone Marullo, Federico Becattini, Stefano Melacci Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE
Jiaxu Liu, Xinping Yi, Sihao Wu, Xiangyu Yin, Tianle Zhang, Xiaowei Huang, Shi Jin Continuously Deep Recurrent Neural Networks
Andrea Ceni, Peter Ford Dominey, Claudio Gallicchio, Alessio Micheli, Luca Pedrelli, Domenico Tortorella Counterfactual-Based Root Cause Analysis for Dynamical Systems
Juliane Weilbach, Sebastian Gerwinn, Karim Said Barsim, Martin Fränzle Deep Sketched Output Kernel Regression for Structured Prediction
Tamim El Ahmad, Junjie Yang, Pierre Laforgue, Florence d'Alché-Buc DiffSynth: Latent In-Iteration Deflickering for Realistic Video Synthesis
Zhongjie Duan, Lizhou You, Chengyu Wang, Cen Chen, Ziheng Wu, Weining Qian, Jun Huang Dimensionality-Induced Information Loss of Outliers in Deep Neural Networks
Kazuki Uematsu, Kosuke Haruki, Taiji Suzuki, Mitsuhiro Kimura, Takahiro Takimoto, Hideyuki Nakagawa Dynamics Adaptive Safe Reinforcement Learning with a Misspecified Simulator
Ruiqi Xue, Ziqian Zhang, Lihe Li, Feng Chen, Yi-Chen Li, Yang Yu, Lei Yuan Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties
A. V. Arun Kumar, Alistair Shilton, Sunil Gupta, Santu Rana, Stewart Greenhill, Svetha Venkatesh Enhancing Multi-Objective Optimisation Through Machine Learning-Supported Multiphysics Simulation
Diego Botache, Jens Decke, Winfried Ripken, Abhinay Dornipati, Franz Götz-Hahn, Mohamed Ayeb, Bernhard Sick Evaluating Negation with Multi-Way Joins Accelerates Class Expression Learning
Nikolaos Karalis, Alexander Bigerl, Caglar Demir, Liss Heidrich, Axel-Cyrille Ngonga Ngomo Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience
Thanh Trung Huynh, Trong Bang Nguyen, Phi Le Nguyen, Thanh Tam Nguyen, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer Federated Frank-Wolfe Algorithm
Ali Dadras, Sourasekhar Banerjee, Karthik Prakhya, Alp Yurtsever FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
Hongyu Zhang, Dongyi Zheng, Lin Zhong, Xu Yang, Jiyuan Feng, Yunqing Feng, Qing Liao Frugal Generative Modeling for Tabular Data
Alice Lacan, Blaise Hanczar, Michèle Sebag Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting
Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme GLADformer: A Mixed Perspective for Graph-Level Anomaly Detection
Fan Xu, Nan Wang, Hao Wu, Xuezhi Wen, Dalin Zhang, Siyang Lu, Binyong Li, Wei Gong, Hai Wan, Xibin Zhao GraphRPM: Risk Pattern Mining on Industrial Large Attributed Graphs
Sheng Tian, Xintan Zeng, Yifei Hu, Baokun Wang, Yongchao Liu, Yue Jin, Changhua Meng, Chuntao Hong, Tianyi Zhang, Weiqiang Wang Guiding Catalogue Enrichment with User Queries
Yupei Du, Jacek Golebiowski, Philipp Schmidt, Ziawasch Abedjan Harnessing Superclasses for Learning from Hierarchical Databases
Nicolas Urbani, Sylvain Rousseau, Yves Grandvalet, Leonardo Tanzi Hyperbolic Delaunay Geometric Alignment
Aniss Aiman Medbouhi, Giovanni Luca Marchetti, Vladislav Polianskii, Alexander Kravberg, Petra Poklukar, Anastasia Varava, Danica Kragic Individual Fairness with Group Awareness Under Uncertainty
Zichong Wang, Jocelyn Dzuong, Xiaoyong Yuan, Zhong Chen, Yanzhao Wu, Xin Yao, Wenbin Zhang Interpretable and Fair Mechanisms for Abstaining Classifiers
Daphne Lenders, Andrea Pugnana, Roberto Pellungrini, Toon Calders, Dino Pedreschi, Fosca Giannotti KAFÈ: Kernel Aggregation for FEderated
Pian Qi, Diletta Chiaro, Fabio Giampaolo, Francesco Piccialli Label Privacy Source Coding in Vertical Federated Learning
Dashan Gao, Sheng Wan, Hanlin Gu, Lixin Fan, Xin Yao, Qiang Yang Leveraging Plasticity in Incremental Decision Trees
Marco Heyden, Heitor Murilo Gomes, Edouard Fouché, Bernhard Pfahringer, Klemens Böhm Linear Modeling of the Adversarial Noise Space
Jordan Patracone, Lucas Anquetil, Yuan Liu, Gilles Gasso, Stéphane Canu Long-Term Fairness in Ride-Hailing Platform
Yufan Kang, Jeffrey Chan, Wei Shao, Flora D. Salim, Christopher Leckie Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection
Jakub Horenin, Veronika Magdanz, Islam S. M. Khalil, Anke Klingner, Alexander Kovalenko, Miroslav Cepek MedSyn: LLM-Based Synthetic Medical Text Generation Framework
Gleb Kumichev, Pavel Blinov, Yulia Kuzkina, Vasily Goncharov, Galina Zubkova, Nikolai Zenovkin, Aleksei Goncharov, Andrey V. Savchenko MetaQuRe: Meta-Learning from Model Quality and Resource Consumption
Raphael Fischer, Marcel Wever, Sebastian Buschjäger, Thomas Liebig MixerFlow: MLP-Mixer Meets Normalising Flows
Eshant English, Matthias Kirchler, Christoph Lippert Model Fusion via Neuron Transplantation
Muhammed Öz, Nicholas Kiefer, Charlotte Debus, Jasmin Hörter, Achim Streit, Markus Götz Multi-Intent Driven Contrastive Sequential Recommendation
Yiyuan Zheng, Beibei Li, Beihong Jin, Rui Zhao, Weijiang Lai, Tao Xiang Novel Node Category Detection Under Subpopulation Shift
Hsing-Huan Chung, Shravan Chaudhari, Yoav Wald, Xing Han, Joydeep Ghosh On the Robustness of Global Feature Effect Explanations
Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek On the Two Sides of Redundancy in Graph Neural Networks
Franka Bause, Samir Moustafa, Johannes Langguth, Wilfried N. Gansterer, Nils M. Kriege Online $\textrm{L}{\natural }$-Convex Minimization
Ken Yokoyama, Shinji Ito, Tatsuya Matsuoka, Kei Kimura, Makoto Yokoo Pattern or Artifact? Interactively Exploring Embedding Quality with TRACE
Edith Heiter, Liesbet Martens, Ruth Seurinck, Martin Guilliams, Tijl De Bie, Yvan Saeys, Jefrey Lijffijt PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
Qingqing Ge, Zeyuan Zhao, Yiding Liu, Anfeng Cheng, Xiang Li, Shuaiqiang Wang, Dawei Yin Quantification over Time
Feiyu Li, Hassan Habibi Gharakheili, Gustavo Batista Reinventing Node-Centric Traffic Forecasting for Improved Accuracy and Efficiency
Xu Liu, Yuxuan Liang, Chao Huang, Hengchang Hu, Yushi Cao, Bryan Hooi, Roger Zimmermann Secure Aggregation Is Not Private Against Membership Inference Attacks
Khac-Hoang Ngo, Johan Östman, Giuseppe Durisi, Alexandre Graell i Amat Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks
Chenghua Gong, Xiang Li, Jianxiang Yu, Yao Cheng, Jiaqi Tan, Chengcheng Yu Self-SLAM: A Self-Supervised Learning Based Annotation Method to Reduce Labeling Overhead
Alfiya M. Shaikh, Hrithik Nambiar, Kshitish Ghate, Swarnali Banik, Sougata Sen, Surjya Ghosh, Vaskar Raychoudhury, Niloy Ganguly, Snehanshu Saha Simple Graph Condensation
Zhenbang Xiao, Yu Wang, Shunyu Liu, Huiqiong Wang, Mingli Song, Tongya Zheng Simultaneous Linear Connectivity of Neural Networks Modulo Permutation
Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite Spatial Transfer Learning for Estimating PM2.5 in Data-Poor Regions
Shrey Gupta, Yongbee Park, Jianzhao Bi, Suyash Gupta, Andreas Züfle, Avani Wildani, Yang Liu Spatial-Temporal PDE Networks for Traffic Flow Forecasting
Tianshu Bao, Hua Wei, Junyi Ji, Daniel B. Work, Taylor T. Johnson Spatiotemporal Covariance Neural Networks
Andrea Cavallo, Mohammad Sabbaqi, Elvin Isufi Symbolic Prompt Tuning Completes the App Promotion Graph
Zhongyu Ouyang, Chunhui Zhang, Shifu Hou, Shang Ma, Chaoran Chen, Toby Li, Xusheng Xiao, Chuxu Zhang, Yanfang Ye Tackling Oversmoothing in GNN via Graph Sparsification
Tanvir Hossain, Khaled Mohammed Saifuddin, Muhammad Ifte Khairul Islam, Farhan Tanvir, Esra Akbas The Price of Labelling: A Two-Phase Federated Self-Learning Approach
Tahani Aladwani, Shameem Puthiya Parambath, Christos Anagnostopoulos, Fani Deligianni TiNID: A Transfer and Interpretable LLM-Enhanced Framework for New Intent Discovery
Shun Zhang, Chaoran Yan, Jian Yang, Wei Zhang, Changyu Ren, Tongliang Li, Jiaqi Bai, Zhoujun Li Towards Few-Shot Self-Explaining Graph Neural Networks
Jingyu Peng, Qi Liu, Linan Yue, Zaixi Zhang, Kai Zhang, Yunhao Sha Towards Open-World Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
Wujiang Xu, Xuying Ning, Wenfang Lin, Mingming Ha, Qiongxu Ma, Qianqiao Liang, Xuewen Tao, Linxun Chen, Bing Han, Minnan Luo Uplift Modeling Under Limited Supervision
George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, Jun Pang VulEXplaineR: XAI for Vulnerability Detection on Assembly Code
Samaneh Mahdavifar, Mohd Saqib, Benjamin C. M. Fung, Philippe Charland, Andrew Walenstein Σ-GPTs: A New Approach to Autoregressive Models
Arnaud Pannatier, Evann Courdier, François Fleuret