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Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Albert Bifet,Jesse Davis,Indrė Žliobaitė Confer

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Dynamics Adaptive Safe Reinforcement Learning with a Misspecified Simulatortraditional methods. Subsequently, DASaR aligns the estimated value functions in the simulator and the real-world environment via inverse dynamics-based relabeling of reward and cost signals. Furthermore, to deal with the underestimation of cost value functions, DASaR employs uncertainty estimation
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FairFlow: An Automated Approach to Model-Based Counterfactual Data Augmentation for NLP paper proposes FairFlow, an automated approach to generating parallel data for training counterfactual text generator models that limits the need for human intervention. Furthermore, we show that FairFlow significantly overcomes the limitations of dictionary-based word-substitution approaches whils
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MEGA: Multi-encoder GNN Architecture for Stronger Task Collaboration and Generalizationng of each task. This architecture allows for independent learning from multiple pretext tasks, followed by a simple self-supervised dimensionality reduction technique to combine the insights gleaned. Through extensive experiments, we demonstrate the superiority of our approach, showcasing an averag
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MetaQuRe: Meta-learning from Model Quality and Resource Consumptionurce consumption of models evaluated across hundreds of data sets and four execution environments. We use this data to put our methodology into practice and conduct an in-depth analysis of how our approach and data set can help in making AutoML more resource-aware, which represents our third contrib
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Propagation Structure-Semantic Transfer Learning for Robust Fake News Detectiontion under a teacher-student architecture. Specifically, we design dual teacher models to learn semantics knowledge and structure knowledge from noisy news content and propagation structure independently. Besides, we design a Multi-channel Knowledge Distillation (MKD) loss to enable the student mode
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