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ORCIDPKDD / ECML 2021: Bilbao, Spain - Part I
- 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 I. Lecture Notes in Computer Science 12975, Springer 2021, ISBN 978-3-030-86485-9
Online Learning
- Hassan Saber, Léo Saci, Odalric-Ambrym Maillard, Audrey Durand:
Routine Bandits: Minimizing Regret on Recurring Problems. 3-18 - Martino Bernasconi de Luca, Edoardo Vittori, Francesco Trovò, Marcello Restelli:
Conservative Online Convex Optimization. 19-34 - Kaushik Roy, Qi Zhang, Manas Gaur, Amit P. Sheth:
Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits. 35-50 - Gerlando Re, Fabio Chiusano, Francesco Trovò, Diego Carrera, Giacomo Boracchi, Marcello Restelli:
Exploiting History Data for Nonstationary Multi-armed Bandit. 51-66 - Shizhong Liao, Junfan Li:
High-Probability Kernel Alignment Regret Bounds for Online Kernel Selection. 67-83
Reinforcement Learning
- Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda:
Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning. 87-103 - Weinan Zhang, Zhengyu Yang, Jian Shen, Minghuan Liu, Yimin Huang, Xing Zhang, Ruiming Tang, Zhenguo Li:
Learning to Build High-Fidelity and Robust Environment Models. 104-121 - Muhammad Rizki Maulana, Wee Sun Lee:
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning. 122-138 - Hongwei Wang, Lantao Yu, Zhangjie Cao, Stefano Ermon:
Multi-agent Imitation Learning with Copulas. 139-156 - Chenyi Liu, Nan Geng, Vaneet Aggarwal, Tian Lan, Yuan Yang, Mingwei Xu:
CMIX: Deep Multi-agent Reinforcement Learning with Peak and Average Constraints. 157-173 - Jian Shen, Mingcheng Chen, Zhicheng Zhang, Zhengyu Yang, Weinan Zhang, Yong Yu:
Model-Based Offline Policy Optimization with Distribution Correcting Regularization. 174-189 - Matthias Hutsebaut-Buysse, Tom De Schepper, Kevin Mets, Steven Latré:
Disagreement Options: Task Adaptation Through Temporally Extended Actions. 190-205 - Ariyan Bighashdel, Panagiotis Meletis, Pavol Jancura, Gijs Dubbelman:
Deep Adaptive Multi-intention Inverse Reinforcement Learning. 206-221 - Johannes Ackermann, Oliver Richter, Roger Wattenhofer:
Unsupervised Task Clustering for Multi-task Reinforcement Learning. 222-237 - Huixin Zhan, Wei-Ming Lin, Yongcan Cao:
Deep Model Compression via Two-Stage Deep Reinforcement Learning. 238-254 - Zac Wellmer, James T. Kwok:
Dropout's Dream Land: Generalization from Learned Simulators to Reality. 255-270 - Jonathan Leung, Zhiqi Shen, Zhiwei Zeng, Chunyan Miao:
Goal Modelling for Deep Reinforcement Learning Agents. 271-286
Time Series, Streams, and Sequence Models
- Anand Vir Singh Chauhan, Shivshankar Reddy, Maneet Singh, Karamjit Singh, Tanmoy Bhowmik:
Deviation-Based Marked Temporal Point Process for Marker Prediction. 289-304 - Jiangxia Cao, Xixun Lin, Xin Cong, Shu Guo, Hengzhu Tang, Tingwen Liu, Bin Wang:
Deep Structural Point Process for Learning Temporal Interaction Networks. 305-320 - Bingjie He, Shukai Li, Chen Zhang, Baihua Zheng, Fugee Tsung:
Holistic Prediction for Public Transport Crowd Flows: A Spatio Dynamic Graph Network Approach. 321-336 - Arnaud Giacometti, Arnaud Soulet:
Reservoir Pattern Sampling in Data Streams. 337-352 - Lin Liu, Li Wang, Tao Lian:
Discovering Proper Neighbors to Improve Session-Based Recommendation. 353-369 - Yinan Li, Fang Liu:
Continuous-Time Markov-Switching GARCH Process with Robust State Path Identification and Volatility Estimation. 370-387 - Yugang Ji, Tianrui Jia, Yuan Fang, Chuan Shi:
Dynamic Heterogeneous Graph Embedding via Heterogeneous Hawkes Process. 388-403 - Amal Saadallah, Matthias Jakobs, Katharina Morik:
Explainable Online Deep Neural Network Selection Using Adaptive Saliency Maps for Time Series Forecasting. 404-420 - Luca Frittoli, Diego Carrera, Giacomo Boracchi:
Change Detection in Multivariate Datastreams Controlling False Alarms. 421-436 - Nikolaj Tatti:
Approximation Algorithms for Confidence Bands for Time Series. 437-452 - Charles K. Assaad, Emilie Devijver, Éric Gaussier, Ali Aït-Bachir:
A Mixed Noise and Constraint-Based Approach to Causal Inference in Time Series. 453-468 - Andrea Castellani, Sebastian Schmitt, Barbara Hammer:
Estimating the Electrical Power Output of Industrial Devices with End-to-End Time-Series Classification in the Presence of Label Noise. 469-484 - Shayan Jawed, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka:
Multi-task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets. 485-501 - Lukasz Korycki, Bartosz Krawczyk:
Streaming Decision Trees for Lifelong Learning. 502-518
Transfer and Multi-task Learning
- Farzaneh Khoshnevisan, Min Chi:
Unifying Domain Adaptation and Domain Generalization for Robust Prediction Across Minority Racial Groups. 521-537 - Pengxin Guo, Chang Deng, Linjie Xu, Xiaonan Huang, Yu Zhang:
Deep Multi-task Augmented Feature Learning via Hierarchical Graph Neural Network. 538-553 - Etienne Bennequin, Victor Bouvier, Myriam Tami, Antoine Toubhans, Céline Hudelot:
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift. 554-569 - Tomoya Sakai:
Source Hypothesis Transfer for Zero-Shot Domain Adaptation. 570-586 - Xin-Chun Li, De-Chuan Zhan, Yunfeng Shao, Bingshuai Li, Shaoming Song:
FedPHP: Federated Personalization with Inherited Private Models. 587-602 - Lin Tian, Xiuzhen Zhang, Jey Han Lau:
Rumour Detection via Zero-Shot Cross-Lingual Transfer Learning. 603-618 - Xuejun Han, Yuhong Guo:
Continual Learning with Dual Regularizations. 619-634 - Sumaiya Tabassum Nimi, Md. Adnan Arefeen, Md. Yusuf Sarwar Uddin, Yugyung Lee:
EARLIN: Early Out-of-Distribution Detection for Resource-Efficient Collaborative Inference. 635-651
Semi-supervised and Few-Shot Learning
- Yanbin Liu, Makoto Yamada, Yao-Hung Hubert Tsai, Tam Le, Ruslan Salakhutdinov, Yi Yang:
LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport. 655-670 - Yassine Ouali, Céline Hudelot, Myriam Tami:
Spatial Contrastive Learning for Few-Shot Classification. 671-686 - Sunil Aryal, Jonathan R. Wells:
Ensemble of Local Decision Trees for Anomaly Detection in Mixed Data. 687-702
Learning Algorithms and Applications
- Manuel Garcia-Piqueras, José Hernández-Orallo:
Optimal Teaching Curricula with Compositional Simplicity Priors. 705-721 - Jian-Hui Duan, Wenzhong Li, Sanglu Lu:
FedDNA: Federated Learning with Decoupled Normalization-Layer Aggregation for Non-IID Data. 722-737 - Sarath Sivaprasad, Ankur Singh, Naresh Manwani, Vineet Gandhi:
The Curious Case of Convex Neural Networks. 738-754 - Robin Louiset, Pietro Gori, Benoit Dufumier, Josselin Houenou, Antoine Grigis, Edouard Duchesnay:
UCSL : A Machine Learning Expectation-Maximization Framework for Unsupervised Clustering Driven by Supervised Learning. 755-771 - Li Chou, Zichang Liu, Zhuang Wang, Anshumali Shrivastava:
Efficient and Less Centralized Federated Learning. 772-787 - Dorcas Ofori-Boateng, Ignacio Segovia-Dominguez, Cuneyt Gurcan Akcora, Murat Kantarcioglu, Yulia R. Gel:
Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks. 788-804
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