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ORCIDPKDD / ECML 2021: Bilbao, Spain - Part II
- Nuria Oliver
, Fernando Pérez-Cruz, Stefan Kramer, Jesse Read, José Antonio Lozano:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part II. Lecture Notes in Computer Science 12976, Springer 2021, ISBN 978-3-030-86519-1
Generative Models
- Jun Zhuang, Mohammad Al Hasan:
Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks. 3-18 - Rayyan Ahmad Khan, Muhammad Umer Anwaar, Omran Kaddah, Zhiwei Han, Martin Kleinsteuber:
Unsupervised Learning of Joint Embeddings for Node Representation and Community Detection. 19-35 - Siddharth Bhatia, Yiwei Wang, Bryan Hooi, Tanmoy Chakraborty:
GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs. 36-51 - Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
The Bures Metric for Generative Adversarial Networks. 52-66 - Xiulong Yang, Hui Ye, Yang Ye, Xiang Li, Shihao Ji:
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More. 67-83 - Judith Bütepage, Lucas Maystre, Mounia Lalmas:
Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty. 84-99 - Phuoc Nguyen, Truyen Tran, Sunil Gupta, Santu Rana, Hieu-Chi Dam, Svetha Venkatesh:
Variational Hyper-encoding Networks. 100-115 - Samuel G. Fadel, Sebastian Mair, Ricardo da Silva Torres, Ulf Brefeld:
Principled Interpolation in Normalizing Flows. 116-131 - Emmanuel de Bézenac, Ibrahim Ayed, Patrick Gallinari:
CycleGAN Through the Lens of (Dynamical) Optimal Transport. 132-147 - Yaqiong Li, Xuhui Fan, Ling Chen, Bin Li, Scott A. Sisson:
Decoupling Sparsity and Smoothness in Dirichlet Belief Networks. 148-163
Algorithms and Learning Theory
- Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant:
Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound. 167-183 - Hongyu Guo:
Midpoint Regularization: From High Uncertainty Training Labels to Conservative Classification Decisions. 184-199 - Eike Stadtländer, Tamás Horváth, Stefan Wrobel:
Learning Weakly Convex Sets in Metric Spaces. 200-216 - Mahsa Forouzesh, Patrick Thiran:
Disparity Between Batches as a Signal for Early Stopping. 217-232 - Soham Dan, Han Bao, Masashi Sugiyama:
Learning from Noisy Similar and Dissimilar Data. 233-249 - Dang Nguyen, Sunil Gupta, Trong Nguyen, Santu Rana, Phuoc Nguyen, Truyen Tran, Ky Le, Shannon Ryan, Svetha Venkatesh:
Knowledge Distillation with Distribution Mismatch. 250-265 - Mirko Bunse, Katharina Morik:
Certification of Model Robustness in Active Class Selection. 266-281
Graphs and Networks
- Luu Huu Phuc, Koh Takeuchi, Seiji Okajima, Arseny Tolmachev, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima:
Inter-domain Multi-relational Link Prediction. 285-301 - Alexandre Duval, Fragkiskos D. Malliaros:
GraphSVX: Shapley Value Explanations for Graph Neural Networks. 302-318 - Jianan Zhao, Qianlong Wen, Shiyu Sun, Yanfang Ye, Chuxu Zhang:
Multi-view Self-supervised Heterogeneous Graph Embedding. 319-334 - Yuanxin Zhuang, Chuan Shi, Cheng Yang, Fuzhen Zhuang, Yangqiu Song:
Semantic-Specific Hierarchical Alignment Network for Heterogeneous Graph Adaptation. 335-350 - Maarten Buyl, Tijl De Bie:
The KL-Divergence Between a Graph Model and its Fair I-Projection as a Fairness Regularizer. 351-366 - Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
On Generalization of Graph Autoencoders with Adversarial Training. 367-382 - Shuo Yang, Binbin Hu, Zhiqiang Zhang, Wang Sun, Yang Wang, Jun Zhou, Hongyu Shan, Yuetian Cao, Borui Ye, Yanming Fang, Quan Yu:
Inductive Link Prediction with Interactive Structure Learning on Attributed Graph. 383-398 - Delvin Ce Zhang, Hady W. Lauw:
Representation Learning on Multi-layered Heterogeneous Network. 399-416 - Yuya Ogawa, Seiji Maekawa, Yuya Sasaki, Yasuhiro Fujiwara, Makoto Onizuka:
Adaptive Node Embedding Propagation for Semi-supervised Classification. 417-433 - Shiyi Chen, Ziao Wang, Xinni Zhang, Xiaofeng Zhang, Dan Peng:
Probing Negative Sampling for Contrastive Learning to Learn Graph Representations. 434-449 - Shouheng Li, Dongwoo Kim, Qing Wang:
Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs. 450-465 - Xuan Kan, Hejie Cui, Carl Yang:
Zero-Shot Scene Graph Relation Prediction Through Commonsense Knowledge Integration. 466-482 - Minji Yoon:
Graph Fraud Detection Based on Accessibility Score Distributions. 483-498 - Domenico Mandaglio, Andrea Tagarelli, Francesco Gullo:
Correlation Clustering with Global Weight Bounds. 499-515 - Yingpeng Du, Hongzhi Liu, Zhonghai Wu:
Modeling Multi-factor and Multi-faceted Preferences over Sequential Networks for Next Item Recommendation. 516-531 - Benjamin A. Miller, Zohair Shafi, Wheeler Ruml, Yevgeniy Vorobeychik, Tina Eliassi-Rad, Scott Alfeld:
PATHATTACK: Attacking Shortest Paths in Complex Networks. 532-547 - Afshin Sadeghi, Diego Collarana, Damien Graux, Jens Lehmann:
Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities. 548-564
Interpretation, Explainability, Transparency, Safety
- Shin-ichi Maeda, Hayato Watahiki, Yi Ouyang, Shintarou Okada, Masanori Koyama, Prabhat Nagarajan:
Reconnaissance for Reinforcement Learning with Safety Constraints. 567-582 - Boxiang Dong, Bo Zhang, Wendy Hui Wang:
VeriDL: Integrity Verification of Outsourced Deep Learning Services. 583-598 - Priyadarshini Kumari, Siddhartha Chaudhuri, Vivek S. Borkar, Subhasis Chaudhuri:
A Unified Batch Selection Policy for Active Metric Learning. 599-616 - Li Zhang, Xin Li, Mingzhong Wang, Andong Tian:
Off-Policy Differentiable Logic Reinforcement Learning. 617-632 - Hichem Debbi:
Causal Explanation of Convolutional Neural Networks. 633-649 - Arnaud Van Looveren, Janis Klaise:
Interpretable Counterfactual Explanations Guided by Prototypes. 650-665 - Soumi Das, Arshdeep Singh, Saptarshi Chatterjee, Suparna Bhattacharya, Sourangshu Bhattacharya:
Finding High-Value Training Data Subset Through Differentiable Convex Programming. 666-681 - Philip Naumann, Eirini Ntoutsi:
Consequence-Aware Sequential Counterfactual Generation. 682-698 - Yunzhe Jia, Eibe Frank, Bernhard Pfahringer, Albert Bifet, Nick Jin Sean Lim:
Studying and Exploiting the Relationship Between Model Accuracy and Explanation Quality. 699-714 - Tomás Komárek, Jan Brabec, Petr Somol:
Explainable Multiple Instance Learning with Instance Selection Randomized Trees. 715-730 - Bashir Sadeghi, Lan Wang, Vishnu Naresh Boddeti:
Adversarial Representation Learning with Closed-Form Solvers. 731-748 - Vincent Grari, Oualid El Hajouji, Sylvain Lamprier, Marcin Detyniecki:
Learning Unbiased Representations via Rényi Minimization. 749-764 - Suhas Thejaswi, Bruno Ordozgoiti, Aristides Gionis:
Diversity-Aware k-median: Clustering with Fair Center Representation. 765-780 - Shiv Shankar, Daniel Sheldon:
Sibling Regression for Generalized Linear Models. 781-795 - Matteo Sordello, Zhiqi Bu, Jinshuo Dong:
Privacy Amplification via Iteration for Shuffled and Online PNSGD. 796-813
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