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ORCIDPKDD / ECML 2020: Ghent, Belgium
- Frank Hutter
, Kristian Kersting, Jefrey Lijffijt, Isabel Valera:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part II. Lecture Notes in Computer Science 12458, Springer 2021, ISBN 978-3-030-67660-5
Deep Learning Optimization and Theory
- Yu Tang, Zhigang Kan, Dequan Sun, Linbo Qiao, Jingjing Xiao, Zhiquan Lai, Dongsheng Li:
ADMMiRNN: Training RNN with Stable Convergence via an Efficient ADMM Approach. 3-18 - Amit Kadan, Hu Fu:
Exponential Convergence of Gradient Methods in Concave Network Zero-Sum Games. 19-34 - Zana Rashidi, Kasra Ahmadi K. A., Aijun An, Xiaogang Wang:
Adaptive Momentum Coefficient for Neural Network Optimization. 35-51 - Elbruz Ozen, Alex Orailoglu:
Squeezing Correlated Neurons for Resource-Efficient Deep Neural Networks. 52-68 - Philip Sperl, Jan-Philipp Schulze, Konstantin Böttinger:
Activation Anomaly Analysis. 69-84 - Jiayu Liu, Ioannis Chiotellis, Rudolph Triebel, Daniel Cremers:
Effective Version Space Reduction for Convolutional Neural Networks. 85-100 - Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari:
A Principle of Least Action for the Training of Neural Networks. 101-117
Active Learning
- Jonas Soenen, Sebastijan Dumancic, Toon van Craenendonck, Hendrik Blockeel:
Tackling Noise in Active Semi-supervised Clustering. 121-136 - Agnes Tegen, Paul Davidsson, Jan A. Persson:
A Taxonomy of Interactive Online Machine Learning Strategies. 137-153 - Lu Yin, Vlado Menkovski, Mykola Pechenizkiy:
Knowledge Elicitation Using Deep Metric Learning and Psychometric Testing. 154-169
Adversarial Learning
- Sebastian Pölsterl, Christian Wachinger:
Adversarial Learned Molecular Graph Inference and Generation. 173-189 - Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu:
A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI. 190-205 - Benedikt Böing, Rajarshi Roy, Emmanuel Müller, Daniel Neider:
Quality Guarantees for Autoencoders via Unsupervised Adversarial Attacks. 206-222 - Xiaochen Yang, Mingzhi Dong, Yiwen Guo, Jing-Hao Xue:
Metric Learning for Categorical and Ambiguous Features: An Adversarial Method. 223-238 - Kun Xu, Chao Du, Chongxuan Li, Jun Zhu, Bo Zhang:
Learning Implicit Generative Models by Teaching Density Estimators. 239-255 - Kangwook Lee, Changho Suh, Kannan Ramchandran:
Reprogramming GANs via Input Noise Design. 256-271 - Puneet Mangla, Vedant Singh, Vineeth N. Balasubramanian:
On Saliency Maps and Adversarial Robustness. 272-288 - Haripriya Harikumar, Vuong Le, Santu Rana, Sourangshu Bhattacharya, Sunil Gupta, Svetha Venkatesh:
Scalable Backdoor Detection in Neural Networks. 289-304
Federated Learning
- Erika Duriakova, Weipéng Huáng, Elias Z. Tragos, Aonghus Lawlor, Barry Smyth, James Geraci, Neil Hurley:
An Algorithmic Framework for Decentralised Matrix Factorisation. 307-323 - Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan Eeik Tan, Suleiman A. Khan, Muhammad Ammad-ud-din:
Federated Multi-view Matrix Factorization for Personalized Recommendations. 324-347 - Wei Chen, Kartikeya Bhardwaj, Radu Marculescu:
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning. 348-363 - Michael Strobl, Jörg Sander, Ricardo J. G. B. Campello, Osmar R. Zaïane:
Model-Based Clustering with HDBSCAN. 364-379
Kernel Methods and Online Learning
- Hadar Sivan, Moshe Gabel, Assaf Schuster:
Incremental Sensitivity Analysis for Kernelized Models. 383-398 - Bruno Ordozgoiti, Lluís A. Belanche Muñoz:
Off-the-Grid: Fast and Effective Hyperparameter Search for Kernel Clustering. 399-415 - Jason Rhuggenaath, Paulo Roberto de Oliveira da Costa, Yingqian Zhang, Alp Akcay, Uzay Kaymak:
Low-Regret Algorithms for Strategic Buyers with Unknown Valuations in Repeated Posted-Price Auctions. 416-436
Partial Label Learning
- Yue Sun, Gengyu Lyu, Songhe Feng:
Partial Label Learning via Subspace Representation and Global Disambiguation. 439-454 - Haobo Wang, Yuzhou Qiang, Chen Chen, Weiwei Liu, Tianlei Hu, Zhao Li, Gang Chen:
Online Partial Label Learning. 455-470 - Yao Yao, Chen Gong, Jiehui Deng, Jian Yang:
Network Cooperation with Progressive Disambiguation for Partial Label Learning. 471-488 - Gengyu Lyu, Songhe Feng, Yi Jin, Yidong Li:
Partial Label Learning via Self-Paced Curriculum Strategy. 489-505
Reinforcement Learning
- Arjun Manoharan, Rahul Ramesh, Balaraman Ravindran:
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning. 509-524 - Edward Beeching, Jilles Dibangoye, Olivier Simonin, Christian Wolf:
EgoMap: Projective Mapping and Structured Egocentric Memory for Deep RL. 525-540 - Arthur Aubret, Laëtitia Matignon, Salima Hassas:
ELSIM: End-to-End Learning of Reusable Skills Through Intrinsic Motivation. 541-556 - Tai Hoang, Ngo Anh Vien:
Graph-Based Motion Planning Networks. 557-573
Transfer and Multi-task Learning
- Amélie Barbe, Marc Sebban, Paulo Gonçalves, Pierre Borgnat, Rémi Gribonval:
Graph Diffusion Wasserstein Distances. 577-592 - Francesco Alesiani, Shujian Yu, Ammar Shaker, Wenzhe Yin:
Towards Interpretable Multi-task Learning Using Bilevel Programming. 593-608 - Kaixuan Zhang, Qinglong Wang, C. Lee Giles:
Deep Learning, Grammar Transfer, and Transportation Theory. 609-623 - Xin Cong, Bowen Yu, Tingwen Liu, Shiyao Cui, Hengzhu Tang, Bin Wang:
Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering. 624-639 - Qian Chen, Yuntao Du, Zhiwen Tan, Yi Zhang, Chongjun Wang:
Unsupervised Domain Adaptation with Joint Domain-Adversarial Reconstruction Networks. 640-656 - Jitin Krishnan, Hemant Purohit, Huzefa Rangwala:
Diversity-Based Generalization for Unsupervised Text Classification Under Domain Shift. 657-672
Bayesian Optimization and Few-Shot Learning
- Jungtaek Kim, Seungjin Choi:
On Local Optimizers of Acquisition Functions in Bayesian Optimization. 675-690 - Phuc Luong, Dang Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh:
Bayesian Optimization with Missing Inputs. 691-706 - Bingcong Li, Bo Han, Zhuowei Wang, Jing Jiang, Guodong Long:
Confusable Learning for Large-Class Few-Shot Classification. 707-723 - Guanyu Yang, Kaizhu Huang, Rui Zhang, John Yannis Goulermas, Amir Hussain:
Inductive Generalized Zero-Shot Learning with Adversarial Relation Network. 724-739
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