Abstract is missing.
- Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial NetworksJun Zhuang, Mohammad Al Hasan. 3-18 [doi]
- Unsupervised Learning of Joint Embeddings for Node Representation and Community DetectionRayyan Ahmad Khan, Muhammad Umer Anwaar, Omran Kaddah, Zhiwei Han, Martin Kleinsteuber. 19-35 [doi]
- GraphAnoGAN: Detecting Anomalous Snapshots from Attributed GraphsSiddharth Bhatia, Yiwei Wang, Bryan Hooi, Tanmoy Chakraborty 0002. 36-51 [doi]
- The Bures Metric for Generative Adversarial NetworksHannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens. 52-66 [doi]
- Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreXiulong Yang, Hui Ye, Yang Ye, Xiang Li, Shihao Ji. 67-83 [doi]
- Gaussian Process Encoders: VAEs with Reliable Latent-Space UncertaintyJudith Bütepage, Lucas Maystre, Mounia Lalmas. 84-99 [doi]
- Variational Hyper-encoding NetworksPhuoc Nguyen, Truyen Tran 0001, Sunil Gupta 0001, Santu Rana, Hieu-Chi Dam, Svetha Venkatesh. 100-115 [doi]
- Principled Interpolation in Normalizing FlowsSamuel G. Fadel, Sebastian Mair 0001, Ricardo da Silva Torres, Ulf Brefeld. 116-131 [doi]
- CycleGAN Through the Lens of (Dynamical) Optimal TransportEmmanuel de Bézenac, Ibrahim Ayed, Patrick Gallinari. 132-147 [doi]
- Decoupling Sparsity and Smoothness in Dirichlet Belief NetworksYaqiong Li, Xuhui Fan, Ling Chen 0006, Bin Li 0015, Scott A. Sisson. 148-163 [doi]
- Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-BoundPaul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant. 167-183 [doi]
- Midpoint Regularization: From High Uncertainty Training Labels to Conservative Classification DecisionsHongyu Guo. 184-199 [doi]
- Learning Weakly Convex Sets in Metric SpacesEike Stadtländer, Tamás Horváth 0001, Stefan Wrobel. 200-216 [doi]
- Disparity Between Batches as a Signal for Early StoppingMahsa Forouzesh, Patrick Thiran. 217-232 [doi]
- Learning from Noisy Similar and Dissimilar DataSoham Dan, Han Bao 0002, Masashi Sugiyama. 233-249 [doi]
- Knowledge Distillation with Distribution MismatchDang Nguyen 0002, Sunil Gupta 0001, Trong Nguyen, Santu Rana, Phuoc Nguyen, Truyen Tran 0001, Ky Le, Shannon Ryan, Svetha Venkatesh. 250-265 [doi]
- Certification of Model Robustness in Active Class SelectionMirko Bunse, Katharina Morik. 266-281 [doi]
- Inter-domain Multi-relational Link PredictionLuu Huu Phuc, Koh Takeuchi, Seiji Okajima, Arseny Tolmachev, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima. 285-301 [doi]
- GraphSVX: Shapley Value Explanations for Graph Neural NetworksAlexandre Duval, Fragkiskos D. Malliaros. 302-318 [doi]
- Multi-view Self-supervised Heterogeneous Graph EmbeddingJianan Zhao 0002, Qianlong Wen, ShiYu Sun, Yanfang Ye, Chuxu Zhang. 319-334 [doi]
- Semantic-Specific Hierarchical Alignment Network for Heterogeneous Graph AdaptationYuanXin Zhuang, Chuan Shi, Cheng Yang 0002, Fuzhen Zhuang, Yangqiu Song. 335-350 [doi]
- The KL-Divergence Between a Graph Model and its Fair I-Projection as a Fairness RegularizerMaarten Buyl, Tijl De Bie. 351-366 [doi]
- On Generalization of Graph Autoencoders with Adversarial TrainingTianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy. 367-382 [doi]
- Inductive Link Prediction with Interactive Structure Learning on Attributed GraphShuo Yang, Binbin Hu, Zhiqiang Zhang 0012, Wang Sun, Yang Wang, Jun Zhou 0011, Hongyu Shan, Yuetian Cao, Borui Ye, Yanming Fang, Quan Yu. 383-398 [doi]
- Representation Learning on Multi-layered Heterogeneous NetworkDelvin Ce Zhang, Hady W. Lauw. 399-416 [doi]
- Adaptive Node Embedding Propagation for Semi-supervised ClassificationYuya Ogawa, Seiji Maekawa, Yuya Sasaki 0001, Yasuhiro Fujiwara, Makoto Onizuka. 417-433 [doi]
- Probing Negative Sampling for Contrastive Learning to Learn Graph RepresentationsShiyi Chen, Ziao Wang, Xinni Zhang, Xiaofeng Zhang, Dan Peng. 434-449 [doi]
- Beyond Low-Pass Filters: Adaptive Feature Propagation on GraphsShouheng Li, Dongwoo Kim, Qing Wang. 450-465 [doi]
- Zero-Shot Scene Graph Relation Prediction Through Commonsense Knowledge IntegrationXuan Kan, Hejie Cui, Carl Yang. 466-482 [doi]
- Graph Fraud Detection Based on Accessibility Score DistributionsMinji Yoon. 483-498 [doi]
- Correlation Clustering with Global Weight BoundsDomenico Mandaglio, Andrea Tagarelli, Francesco Gullo. 499-515 [doi]
- Modeling Multi-factor and Multi-faceted Preferences over Sequential Networks for Next Item RecommendationYingpeng Du, Hongzhi Liu, Zhonghai Wu. 516-531 [doi]
- PATHATTACK: Attacking Shortest Paths in Complex NetworksBenjamin A. Miller, Zohair Shafi, Wheeler Ruml, Yevgeniy Vorobeychik, Tina Eliassi-Rad, Scott Alfeld. 532-547 [doi]
- Embedding Knowledge Graphs Attentive to Positional and Centrality QualitiesAfshin Sadeghi, Diego Collarana, Damien Graux, Jens Lehmann 0001. 548-564 [doi]
- Reconnaissance for Reinforcement Learning with Safety ConstraintsShin-ichi Maeda, Hayato Watahiki, Yi Ouyang, Shintarou Okada, Masanori Koyama, Prabhat Nagarajan. 567-582 [doi]
- VeriDL: Integrity Verification of Outsourced Deep Learning ServicesBoxiang Dong, Bo Zhang 0051, Wendy Hui Wang. 583-598 [doi]
- A Unified Batch Selection Policy for Active Metric LearningPriyadarshini Kumari, Siddhartha Chaudhuri, Vivek S. Borkar, Subhasis Chaudhuri. 599-616 [doi]
- Off-Policy Differentiable Logic Reinforcement LearningLi Zhang, Xin Li, Mingzhong Wang, Andong Tian. 617-632 [doi]
- Causal Explanation of Convolutional Neural NetworksHichem Debbi. 633-649 [doi]
- Interpretable Counterfactual Explanations Guided by PrototypesArnaud Van Looveren, Janis Klaise. 650-665 [doi]
- Finding High-Value Training Data Subset Through Differentiable Convex ProgrammingSoumi Das, Arshdeep Singh, Saptarshi Chatterjee, Suparna Bhattacharya, Sourangshu Bhattacharya. 666-681 [doi]
- Consequence-Aware Sequential Counterfactual GenerationPhilip Naumann, Eirini Ntoutsi. 682-698 [doi]
- Studying and Exploiting the Relationship Between Model Accuracy and Explanation QualityYunzhe Jia, Eibe Frank, Bernhard Pfahringer, Albert Bifet, Nick Lim. 699-714 [doi]
- Explainable Multiple Instance Learning with Instance Selection Randomized TreesTomás Komárek, Jan Brabec, Petr Somol. 715-730 [doi]
- Adversarial Representation Learning with Closed-Form SolversBashir Sadeghi, Lan Wang, Vishnu Naresh Boddeti. 731-748 [doi]
- Learning Unbiased Representations via Rényi MinimizationVincent Grari, Oualid El Hajouji, Sylvain Lamprier, Marcin Detyniecki. 749-764 [doi]
- Diversity-Aware k-median: Clustering with Fair Center RepresentationSuhas Thejaswi, Bruno Ordozgoiti, Aristides Gionis. 765-780 [doi]
- Sibling Regression for Generalized Linear ModelsShiv Shankar, Daniel Sheldon. 781-795 [doi]
- Privacy Amplification via Iteration for Shuffled and Online PNSGDMatteo Sordello, Zhiqi Bu, Jinshuo Dong. 796-813 [doi]