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ORCIDPKDD / ECML 2024: Vilnius, Lithuania - Part II
- Albert Bifet
, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part II. Lecture Notes in Computer Science 14942, Springer 2024, ISBN 978-3-031-70343-0
Research Track
- Xingtu Liu:
Landscape Analysis of Stochastic Policy Gradient Methods. 3-17 - Andrea Cavallo, Mohammad Sabbaqi, Elvin Isufi:
Spatiotemporal Covariance Neural Networks. 18-34 - Zhanyu Liu, Jianrong Ding, Guanjie Zheng:
Frequency Enhanced Pre-training for Cross-City Few-shot Traffic Forecasting. 35-52 - Zhenbang Xiao, Yu Wang, Shunyu Liu, Huiqiong Wang, Mingli Song, Tongya Zheng:
Simple Graph Condensation. 53-71 - Changlu Chen, Yanbin Liu, Ling Chen, Chengqi Zhang:
Multivariate Traffic Demand Prediction via 2D Spectral Learning and Global Spatial Optimization. 72-88 - Marie C. Kempkes, Vedran Dunjko, Evert P. L. van Nieuwenburg, Jakob Spiegelberg:
Reliable Classifications with Guaranteed Confidence Using the Dempster-Shafer Theory of Evidence. 89-105 - Abdur Rahman Bin Md Faizullah, Ashok Urlana, Rahul Mishra:
LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers. 106-124 - Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek:
On the Robustness of Global Feature Effect Explanations. 125-142 - Jong-Ik Park, Carlee Joe-Wong:
Federated Learning with Flexible Architectures. 143-161 - Yongkang Liu, Ercong Nie, Shi Feng, Zheng Hua, Zifeng Ding, Daling Wang, Yifei Zhang, Hinrich Schütze:
A Unified Data Augmentation Framework for Low-Resource Multi-domain Dialogue Generation. 162-177 - Tri-Nhan Vo, Dang Nguyen, Kien Do, Sunil Gupta:
Improving Diversity in Black-Box Few-Shot Knowledge Distillation. 178-196 - Chenghua Gong, Xiang Li, Jianxiang Yu, Yao Cheng, Jiaqi Tan, Chengcheng Yu:
Self-pro: A Self-prompt and Tuning Framework for Graph Neural Networks. 197-215 - Minh Hoang Nguyen, Hung Le, Svetha Venkatesh:
Variable-Agnostic Causal Exploration for Reinforcement Learning. 216-232 - Shijie Li, Inigo Jauregi Unanue, Massimo Piccardi:
LayerGLAT: A Flexible Non-autoregressive Transformer for Single-Pass and Multi-pass Prediction. 233-249 - Alvin Heng, Abdul Fatir Ansari, Harold Soh:
Generative Modeling with Flow-Guided Density Ratio Learning. 250-267 - Xiangyu Zheng, Guogang Tian, Sen Wang, Zhixiang Huang:
ADR: An Adversarial Approach to Learn Decomposed Representations for Causal Inference. 268-284 - Marco Bjarne Schuster, Boris Wiegand, Jilles Vreeken:
Data is Moody: Discovering Data Modification Rules from Process Event Logs. 285-302 - Sijia Zhou, Yunwen Lei, Ata Kabán:
Self-certified Tuple-Wise Deep Learning. 303-320 - Koren Abitbul, Yehuda Dar:
How Much Training Data Is Memorized in Overparameterized Autoencoders? An Inverse Problem Perspective on Memorization Evaluation. 321-339 - Matteo Tiezzi, Simone Marullo, Federico Becattini, Stefano Melacci:
Continual Neural Computation. 340-356 - Dat Phan-Trong, Hung The Tran, Alistair Shilton, Sunil Gupta:
PINN-BO: A Black-Box Optimization Algorithm Using Physics-Informed Neural Networks. 357-374 - Xuehao Wang, Weisen Jiang, Shuai Fu, Yu Zhang:
Enhancing Sharpness-Aware Minimization by Learning Perturbation Radius. 375-391 - Wenxi Li, Ruxin Zhang, Haozhe Lin, Yuchen Guo, Chao Ma, Xiaokang Yang:
SaccadeDet: A Novel Dual-Stage Architecture for Rapid and Accurate Detection in Gigapixel Images. 392-408 - Quanyan Gao, Chao Li, Qinmin Yang:
Physics-Informed Spatio-Temporal Model for Human Mobility Prediction. 409-425 - Thanapol Phungtua-Eng, Yoshitaka Yamamoto:
Adaptive Seasonal-Trend Decomposition for Streaming Time Series Data with Transitions and Fluctuations in Seasonality. 426-443 - Marcin Przewiezlikowski, Marcin Osial, Bartosz Zielinski, Marek Smieja:
A Deep Cut Into Split Federated Self-Supervised Learning. 444-459
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