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ORCIDPKDD / ECML 2021: Bilbao, Spain - Part III
- 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 III. Lecture Notes in Computer Science 12977, Springer 2021, ISBN 978-3-030-86522-1
Generative Models
- Philipp F. M. Baumann, Torsten Hothorn, David Rügamer:
Deep Conditional Transformation Models. 3-18 - Alexander Rakowski, Christoph Lippert:
Disentanglement and Local Directions of Variance. 19-34 - Dang Pham, Tuan M. V. Le:
Neural Topic Models for Hierarchical Topic Detection and Visualization. 35-51 - Arpita Kundu, Subhasis Ghosh, Indrajit Bhattacharya:
Semi-structured Document Annotation Using Entity and Relation Types. 52-68 - Benoit Gaujac, Ilya Feige, David Barber:
Learning Disentangled Representations with the Wasserstein Autoencoder. 69-84
Search and optimization
- Manuel Nonnenmacher, David Reeb, Ingo Steinwart:
Which Minimizer Does My Neural Network Converge To? 87-102 - Gaël Poux-Médard, Julien Velcin, Sabine Loudcher:
Information Interaction Profile of Choice Adoption. 103-118 - Ting-Wu Chin, Ari S. Morcos, Diana Marculescu:
Joslim: Joint Widths and Weights Optimization for Slimmable Neural Networks. 119-134 - Jia Bi, Steve R. Gunn:
A Variance Controlled Stochastic Method with Biased Estimation for Faster Non-convex Optimization. 135-150 - Sebastian Buschjäger, Philipp-Jan Honysz, Lukas Pfahler, Katharina Morik:
Very Fast Streaming Submodular Function Maximization. 151-166 - Yang Li, Shihao Ji:
Dep-L0: Improving L0-Based Network Sparsification via Dependency Modeling. 167-183 - Soham Dan, Dushyant Sahoo:
Variance Reduced Stochastic Proximal Algorithm for AUC Maximization. 184-199 - Armin Moharrer, Khashayar Kamran, Edmund Yeh, Stratis Ioannidis:
Robust Regression via Model Based Methods. 200-216 - Yueming Lyu, Ivor W. Tsang:
Black-Box Optimizer with Stochastic Implicit Natural Gradient. 217-232 - Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán:
More General and Effective Model Compression via an Additive Combination of Compressions. 233-248 - Bruno Veloso, Luciano Caroprese, Matthias König, Sónia Teixeira, Giuseppe Manco, Holger H. Hoos, João Gama:
Hyper-parameter Optimization for Latent Spaces. 249-264 - Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter:
Bayesian Optimization with a Prior for the Optimum. 265-296 - Ekhine Irurozki, Aritz Pérez, Jesus L. Lobo, Javier Del Ser:
Rank Aggregation for Non-stationary Data Streams. 297-313 - Yun Yue, Yongchao Liu, Suo Tong, Minghao Li, Zhen Zhang, Chunyang Wen, Huanjun Bao, Lihong Gu, Jinjie Gu, Yixiang Mu:
Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction. 314-329 - Phuoc Nguyen, Truyen Tran, Ky Le, Sunil Gupta, Santu Rana, Dang Nguyen, Trong Nguyen, Shannon Ryan, Svetha Venkatesh:
Fast Conditional Network Compression Using Bayesian HyperNetworks. 330-345 - Hon Sum Alec Yu, Dingling Yao, Christoph Zimmer, Marc Toussaint, Duy Nguyen-Tuong:
Active Learning in Gaussian Process State Space Model. 346-361 - Simona Maggio, Léo Dreyfus-Schmidt:
Ensembling Shift Detectors: An Extensive Empirical Evaluation. 362-377
Supervised Learning
- Zhiyong Hao, Yixuan Jiang, Huihua Yu, Hsiao-Dong Chiang:
Adaptive Learning Rate and Momentum for Training Deep Neural Networks. 381-396 - Zhuo Yang, Yufei Han, Xiangliang Zhang:
Attack Transferability Characterization for Adversarially Robust Multi-label Classification. 397-413 - Jérémie Donà, Patrick Gallinari:
Differentiable Feature Selection, A Reparameterization Approach. 414-429 - Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha:
ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining. 430-445 - Victor Hamer, Pierre Dupont:
Robust Selection Stability Estimation in Correlated Spaces. 446-461 - Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier:
Gradient-Based Label Binning in Multi-label Classification. 462-477 - Lior Aloni, Omer Bobrowski, Ronen Talmon:
Joint Geometric and Topological Analysis of Hierarchical Datasets. 478-493 - Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin:
Reparameterized Sampling for Generative Adversarial Networks. 494-509 - Kan Chen, Zhiqi Bu, Shiyun Xu:
Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing. 510-526 - Lucas Kania, Manuel Schürch, Dario Azzimonti, Alessio Benavoli:
Sparse Information Filter for Fast Gaussian Process Regression. 527-542 - Panagiotis A. Traganitis, Georgios B. Giannakis:
Bayesian Crowdsourcing with Constraints. 543-559
Text Mining and Natural Language Processing
- Endri Kacupaj, Shyamnath Premnadh, Kuldeep Singh, Jens Lehmann, Maria Maleshkova:
VOGUE: Answer Verbalization Through Multi-Task Learning. 563-579 - Zhenyu Zhang, Bowen Yu, Xiaobo Shu, Tingwen Liu:
NA-Aware Machine Reading Comprehension for Document-Level Relation Extraction. 580-595 - Wenxian Shi, Yuxuan Song, Hao Zhou, Bohan Li, Lei Li:
Follow Your Path: A Progressive Method for Knowledge Distillation. 596-611 - Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani:
TaxoRef: Embeddings Evaluation for AI-driven Taxonomy Refinement. 612-627 - Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein:
MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients. 628-643 - Amir Pouran Ben Veyseh, Minh Van Nguyen, Bonan Min, Thien Huu Nguyen:
Augmenting Open-Domain Event Detection with Synthetic Data from GPT-2. 644-660 - Jingzhou Liu, Yiming Yang:
Enhancing Summarization with Text Classification via Topic Consistency. 661-676 - Anton Chernyavskiy, Dmitry Ilvovsky, Preslav Nakov:
Transformers: "The End of History" for Natural Language Processing? 677-693
Image Processing, Computer Vision and Visual Analytics
- Uday Singh Saini, Pravallika Devineni, Evangelos E. Papalexakis:
Subspace Clustering Based Analysis of Neural Networks. 697-712 - Siyuan Li, Haitao Lin, Zelin Zang, Lirong Wu, Jun Xia, Stan Z. Li:
Invertible Manifold Learning for Dimension Reduction. 713-728 - Youze Xu, Yan Yan, Jing-Hao Xue, Yang Lu, Hanzi Wang:
Small-Vote Sample Selection for Label-Noise Learning. 729-744 - Gauthier Van Vracem, Siegfried Nijssen:
Iterated Matrix Reordering. 745-761 - Delvin Ce Zhang, Hady W. Lauw:
Semi-supervised Semantic Visualization for Networked Documents. 762-778 - Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu:
Self-supervised Multi-task Representation Learning for Sequential Medical Images. 779-794 - Shuyi Zhang, Chao Pan, Liyan Song, Xiaoyu Wu, Zheng Hu, Ke Pei, Peter Tino, Xin Yao:
Label-Assisted Memory Autoencoder for Unsupervised Out-of-Distribution Detection. 795-810 - Samir Chowdhury, David Miller, Tom Needham:
Quantized Gromov-Wasserstein. 811-827
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