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ORCIDPKDD / ECML 2022: Grenoble, France - Part IV
- Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part IV. Lecture Notes in Computer Science 13716, Springer 2023, ISBN 978-3-031-26411-5
Reinforcement Learning
- Chengyin Li, Zheng Dong, Nathan Fisher, Dongxiao Zhu:
Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation. 3-19 - Haolin Zhou, Chaoqi Yang, Xiaofeng Gao, Qiong Chen, Gongshen Liu, Guihai Chen:
Multi-Objective Actor-Critics for Real-Time Bidding in Display Advertising. 20-37 - Guoxi Zhang
, Hisashi Kashima:
Batch Reinforcement Learning from Crowds. 38-51 - Andrew Chester, Michael Dann, Fabio Zambetta, John Thangarajah:
Oracle-SAGE: Planning Ahead in Graph-Based Deep Reinforcement Learning. 52-67 - Logan Dunbar, Benjamin Rosman, Anthony G. Cohn, Matteo Leonetti:
Reducing the Planning Horizon Through Reinforcement Learning. 68-83 - Lorenzo Steccanella, Anders Jonsson:
State Representation Learning for Goal-Conditioned Reinforcement Learning. 84-99 - Md. Masudur Rahman, Yexiang Xue:
Bootstrap State Representation Using Style Transfer for Better Generalization in Deep Reinforcement Learning. 100-115 - Georgios Papagiannis, Yunpeng Li:
Imitation Learning with Sinkhorn Distances. 116-131 - Yoshihiro Okawa, Tomotake Sasaki, Hitoshi Yanami, Toru Namerikawa:
Safe Exploration Method for Reinforcement Learning Under Existence of Disturbance. 132-147 - Avishek Ghosh, Sayak Ray Chowdhury:
Model Selection in Reinforcement Learning with General Function Approximations. 148-164
Multi-agent Reinforcement Learning
- Yue Zhao, José Hernández-Orallo:
Heterogeneity Breaks the Game: Evaluating Cooperation-Competition with Multisets of Agents. 167-182 - Jiajing Ling, Arambam James Singh, Nguyen Duc Thien, Akshat Kumar:
Constrained Multiagent Reinforcement Learning for Large Agent Population. 183-199 - Niklas Strauß, David Winkel, Max Berrendorf, Matthias Schubert:
Reinforcement Learning for Multi-Agent Stochastic Resource Collection. 200-215 - Eliran Abdoo, Ronen I. Brafman, Guy Shani, Nitsan Soffair:
Team-Imitate-Synchronize for Solving Dec-POMDPs. 216-232 - Joe Eappen, Suresh Jagannathan:
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems. 233-250 - Stephanie Milani, Zhicheng Zhang, Nicholay Topin, Zheyuan Ryan Shi, Charles A. Kamhoua, Evangelos E. Papalexakis, Fei Fang:
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-agent Reinforcement Learning. 251-266
Bandits and Online Learning
- Tianchi Zhao, Chicheng Zhang, Ming Li:
Hierarchical Unimodal Bandits. 269-283 - Steven Bilaj, Sofien Dhouib, Setareh Maghsudi:
Hypothesis Transfer in Bandits by Weighted Models. 284-299 - Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran:
Multi-agent Heterogeneous Stochastic Linear Bandits. 300-316 - Aymen Al Marjani, Tomás Kocák, Aurélien Garivier:
On the Complexity of All ε-Best Arms Identification. 317-332 - Junfan Li, Shizhong Liao:
Improved Regret Bounds for Online Kernel Selection Under Bandit Feedback. 333-348 - Maximilian Thiessen, Thomas Gärtner:
Online Learning of Convex Sets on Graphs. 349-364
Active and Semi-supervised Learning
- Zitong Wang, Li Wang, Raymond H. Chan, Tieyong Zeng:
Exploring Latent Sparse Graph for Large-Scale Semi-supervised Learning. 367-383 - Martin Bauw, Santiago Velasco-Forero, Jesús Angulo, Claude Adnet, Olivier Airiau:
Near Out-of-Distribution Detection for Low-Resolution Radar Micro-doppler Signatures. 384-399 - Qiang Huang, Jing Ma, Jundong Li, Huiyan Sun, Yi Chang:
SemiITE: Semi-supervised Individual Treatment Effect Estimation via Disagreement-Based Co-training. 400-417 - Sen Zhang, Senzhang Wang, Xiang Wang, Shigeng Zhang, Hao Miao, Junxing Zhu:
Multi-task Adversarial Learning for Semi-supervised Trajectory-User Linking. 418-434 - Vojtech Franc, Daniel Prusa, Andrii Yermakov:
Consistent and Tractable Algorithm for Markov Network Learning. 435-451 - Sahib Julka, Nikolas Kirschstein, Michael Granitzer, Alexander Lavrukhin, Ute V. Amerstorfer:
Deep Active Learning for Detection of Mercury's Bow Shock and Magnetopause Crossings. 452-467 - Daniel Kottke, Christoph Sandrock, Georg Krempl, Bernhard Sick:
A Stopping Criterion for Transductive Active Learning. 468-484 - Vincent Vercruyssen, Lorenzo Perini, Wannes Meert, Jesse Davis:
Multi-domain Active Learning for Semi-supervised Anomaly Detection. 485-501 - Mengyu Wang, Yijia Shao, Haowei Lin, Wenpeng Hu, Bing Liu:
CMG: A Class-Mixed Generation Approach to Out-of-Distribution Detection. 502-518 - Lirong Wu, Jun Xia, Zhangyang Gao, Haitao Lin, Cheng Tan, Stan Z. Li:
GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction. 519-535
Private and Federated Learning
- Jian Li, Bojian Wei, Yong Liu, Weiping Wang:
Non-IID Distributed Learning with Optimal Mixture Weights. 539-554 - Peng Tang, Rui Chen, Chongshi Jin, Gaoyuan Liu, Shanqing Guo:
Marginal Release Under Multi-party Personalized Differential Privacy. 555-571 - Zichen Ma, Yu Lu, Wenye Li, Shuguang Cui:
Beyond Random Selection: A Perspective from Model Inversion in Personalized Federated Learning. 572-586 - Zhanliang Huang, Yunwen Lei, Ata Kabán:
Noise-Efficient Learning of Differentially Private Partitioning Machine Ensembles. 587-603 - Qiyiwen Zhang, Zhiqi Bu, Kan Chen, Qi Long:
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability. 604-619 - Sambhav Solanki, Samhita Kanaparthy, Sankarshan Damle, Sujit Gujar:
Differentially Private Federated Combinatorial Bandits with Constraints. 620-637
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