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ORCIDPKDD / ECML 2023: Turin, Italy - Part II
- Danai Koutra
, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi:
Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part II. Lecture Notes in Computer Science 14170, Springer 2023, ISBN 978-3-031-43414-3
Computer Vision
- Wenkai Chen, Chuang Zhu, Mengting Li:
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels. 3-19 - Alexander Rakowski, Christoph Lippert:
DCID: Deep Canonical Information Decomposition. 20-35 - Yu Zhang, Chuang Zhu, Guoqing Yang, Siqi Chen:
Negative Prototypes Guided Contrastive Learning for Weakly Supervised Object Detection. 36-51 - Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks. 52-68 - Qiqi Zhou, Yichen Zhu:
Make a Long Image Short: Adaptive Token Length for Vision Transformers. 69-85 - Dan Yao, Zhixin Li:
Graph Rebasing and Joint Similarity Reconstruction for Cross-Modal Hash Retrieval. 86-102 - Shuxian Li, Liyan Song, Xiaoyu Wu, Zheng Hu, Yiu-ming Cheung, Xin Yao:
ARConvL: Adaptive Region-Based Convolutional Learning for Multi-class Imbalance Classification. 103-120
Deep Learning
- Riccardo Schiavone, Francesco Galati, Maria A. Zuluaga:
Binary Domain Generalization for Sparsifying Binary Neural Networks. 123-140 - Zhanglu Yan, Shida Wang, Kaiwen Tang, Weng-Fai Wong:
Efficient Hyperdimensional Computing. 141-155 - Weipeng Fuzzy Huang, Junjie Tao, Changbo Deng, Ming Fan, Wenqiang Wan, Qi Xiong, Guangyuan Piao:
Rényi Divergence Deep Mutual Learning. 156-172 - Eduardo Brandao, Stefan Duffner, Rémi Emonet, Amaury Habrard, François Jacquenet, Marc Sebban:
Is My Neural Net Driven by the MDL Principle? 173-189 - Daan Roordink, Sibylle Hess:
Scoring Rule Nets: Beyond Mean Target Prediction in Multivariate Regression. 190-205 - Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj:
Learning Distinct Features Helps, Provably. 206-222 - Srinivas Anumasa, Geetakrishnasai Gunapati, P. K. Srijith:
Continuous Depth Recurrent Neural Differential Equations. 223-238
Fairness
- Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Ben Hu:
Mitigating Algorithmic Bias with Limited Annotations. 241-258 - Zichong Wang, Charles Wallace, Albert Bifet, Xin Yao, Wenbin Zhang:
FG2AN: Fairness-Aware Graph Generative Adversarial Networks. 259-275 - Yacine Gaci, Boualem Benatallah, Fabio Casati, Khalid Benabdeslem:
Targeting the Source: Selective Data Curation for Debiasing NLP Models. 276-294 - François Hu, Philipp Ratz, Arthur Charpentier:
Fairness in Multi-Task Learning via Wasserstein Barycenters. 295-312 - Jiaxu Zhao, Lu Yin, Shiwei Liu, Meng Fang, Mykola Pechenizkiy:
REST: Enhancing Group Robustness in DNNs Through Reweighted Sparse Training. 313-329 - Sandra Gilhuber, Rasmus Hvingelby, Mang Ling Ada Fok, Thomas Seidl:
How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning. 330-347
Federated Learning
- Zhaoyu Wang, Pingchuan Ma, Shuai Wang:
Towards Practical Federated Causal Structure Learning. 351-367 - Chenguang Xiao, Shuo Wang:
Triplets Oversampling for Class Imbalanced Federated Datasets. 368-383 - Muhammad Tahir Munir, Muhammad Mustansar Saeed, Mahad Ali, Zafar Ayyub Qazi, Agha Ali Raza, Ihsan Ayyub Qazi:
Learning Fast and Slow: Towards Inclusive Federated Learning. 384-401 - Yuexin Xuan, Xiaojun Chen, Zhendong Zhao, Bisheng Tang, Ye Dong:
Practical and General Backdoor Attacks Against Vertical Federated Learning. 402-417
Few-Shot Learning
- Xin Liu, Yilin Lyu, Liping Jing, Tieyong Zeng, Jian Yu:
Not All Tasks Are Equal: A Parameter-Efficient Task Reweighting Method for Few-Shot Learning. 421-437 - Yunlong Yu, Lisha Jin, Yingming Li:
Boosting Generalized Few-Shot Learning by Scattering Intra-class Distribution. 438-453 - Xin Liu, Shijing Wang, Kairui Zhou, Yilin Lyu, Mingyang Song, Liping Jing, Tieyong Zeng, Jian Yu:
vMF Loss: Exploring a Scattered Intra-class Hypersphere for Few-Shot Learning. 454-470 - Zhaochen Li, Kedian Mu:
Meta-HRNet: A High Resolution Network for Coarse-to-Fine Few-Shot Classification. 471-487
Generative Models
- Deji Zhao, Donghong Han, Ye Yuan, Chao Wang, Shuangyong Song:
MuSE: A Multi-scale Emotional Flow Graph Model for Empathetic Dialogue Generation. 491-507 - Timur Sudak, Sebastian Tschiatschek:
Posterior Consistency for Missing Data in Variational Autoencoders. 508-524 - Jiaqi Bai, Zhao Yan, Ze Yang, Jian Yang, Xinnian Liang, Hongcheng Guo, Zhoujun Li:
KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation. 525-542 - Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak:
Learning Data Representations with Joint Diffusion Models. 543-559 - Clément Vignac, Nagham Osman, Laura Toni, Pascal Frossard:
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation. 560-576 - Mayi Xu, Ke Sun, Yongqi Li, Tieyun Qian:
Cold-Start Multi-hop Reasoning by Hierarchical Guidance and Self-verification. 577-592 - Minh Nguyen, Kishan K. C., Toan Nguyen, Ankit Chadha, Thuy Vu:
Efficient Fine-Tuning Large Language Models for Knowledge-Aware Response Planning. 593-611 - Shuyang Jiang, Jun Zhang, Jiangtao Feng, Lin Zheng, Lingpeng Kong:
Attentive Multi-Layer Perceptron for Non-autoregressive Generation. 612-629 - Kun Zhou, Xiao Liu, Yeyun Gong, Wayne Xin Zhao, Daxin Jiang, Nan Duan, Ji-Rong Wen:
MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders Are Better Dense Retrievers. 630-647
Graph Contrastive Learning
- Shuyun Gu, Xiao Wang, Chuan Shi:
Duplicate Multi-modal Entities Detection with Graph Contrastive Self-training Network. 651-665 - Jin Li, Bingshi Li, Qirong Zhang, Xinlong Chen, Xinyang Huang, Longkun Guo, Yang-Geng Fu:
Graph Contrastive Representation Learning with Input-Aware and Cluster-Aware Regularization. 666-682 - Hongjiang Chen, Pengfei Jiao, Huijun Tang, Huaming Wu:
Temporal Graph Representation Learning with Adaptive Augmentation Contrastive. 683-699 - Hao Yan, Senzhang Wang, Jun Yin, Chaozhuo Li, Junxing Zhu, Jianxin Wang:
Hierarchical Graph Contrastive Learning. 700-715
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