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[null,null,"Titolo",null,"Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part I / / edited by Nuria Oliver, Fernando Pérez-Cruz, Stefan Kramer, Jesse Read, Jose A. Lozano"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021"]
[null,null,"ISBN",null,"3-030-86486-3"]
[null,null,"Edizione",null,"[1st ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (838 pages)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 12975"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Social sciences - Data processing"]
[{"$nulls":4},"Education - Data processing"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Computer Application in Social and Behavioral Sciences"]
[{"$nulls":4},"Computers and Education"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Computer Vision"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Invited Talks Abstracts -- WuDao: Pretrain the World -- The Value of Data for Personalization -- AI Fairness in Practice -- Safety and Robustness for Deep Learning with Provable Guarantees -- Contents - Part I -- Online Learning -- Routine Bandits: Minimizing Regret on Recurring Problems -- 1 Introduction -- 2 The Routine Bandit Setting -- 3 The KLUCB-RB Strategy -- 4 Sketch of Proof -- 5 Numerical Experiments -- 5.1 More Arms Than Bandits: A Beneficial Case -- 5.2 Increasing the Number of Bandit Instances -- 5.3 Critical Settings -- 6 Conclusion -- References -- Conservative Online Convex Optimization -- 1 Introduction -- 2 Background -- 3 Problem "]
[{"$nulls":4},"Formulation -- 4 The Conservative Projection Algorithm -- 4.1 The Conservative Ball -- 4.2 Description of the CP Algorithm -- 4.3 Analysis of the CP Algorithm -- 5 Experiments -- 5.1 Synthetic Regression Dataset -- 5.2 Online Classification: The IMDB Dataset -- 5.3 Online Classification: The SpamBase Dataset -- 6 Conclusions -- References -- Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits -- 1 Introduction -- 2 Problem Setting -- 3 Knowledge Infused Policy Gradients -- 4 Formulation of Knowledge Infusion -- 5 Regret Bound for KIPG -- 6 KIPG-Upper Confidence Bound -- 7 Experiments -- 7.1 Simulated Domains -- 7.2 Real-World Datasets -- 8 Conclusion and Future Work -- References -- Exploiting History Data for Nonstationary Multi-armed Bandit -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 4 The BR-MAB Algorithm -- 4.1 Break-Point Prediction Procedure -- 4.2 Recurrent Concepts Equivalence Test -- 4.3 Regret Analysis for Generic CD-MABs -- 4.4 Regret Analysis for the Break-Point Prediction Procedure -- 5 Experiments -- 5.1 Toy Example -- 5.2 Synthetic Setting -- 5.3 Yahoo! Setting -- 6 Conclusion and Future Works."]
[{"$nulls":4},"References -- High-Probability Kernel Alignment Regret Bounds for Online Kernel Selection -- 1 Introduction -- 1.1 Related Work -- 2 Problem Setting -- 3 A Nearly Optimal High-Probability Regret Bound -- 3.1 Warm-Up -- 3.2 A More Efficient Algorithm -- 3.3 Regret Bound -- 3.4 Time Complexity Analysis -- 4 Regret-Performance Trade-Off -- 4.1 Regret Bound -- 4.2 Budgeted EA2OKS -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Experimental Results -- 6 Conclusion -- References -- Reinforcement Learning -- Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Method -- 4.1 Overview -- 4.2 Ensemble Initialization -- 4.3 Joint Training -- 4.4 Intra-ensemble Knowledge Distillation -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Effectiveness of PIEKD -- 5.3 Effectiveness of Knowledge Distillation for Knowledge Sharing -- 5.4 Effectiveness of Selecting the Best-Performing Agent as the Teacher -- 5.5 Ablation Study on Ensemble Size -- 5.6 Ablation Study on Distillation Interval -- 6 Conclusion -- References -- Learning to Build High-Fidelity and Robust Environment Models -- 1 Introduction -- 2 Related Work -- 2.1 Simulator Building -- 2.2 Model-Based Reinforcement Learning -- 2.3 Offline Policy Evaluation -- 2.4 Robust Reinforcement Learning -- 3 Preliminaries -- 3.1 Markov Decision Process -- 3.2 Dual Markov Decision Process -- 3.3 Imitation Learning -- 4 Robust Learning to Simulate -- 4.1 Problem Definition -- 4.2 Single Behavior Policy Setting -- 4.3 Robust Policy Setting -- 5 Experiments -- 5.1 Experimental Protocol -- 5.2 Studied Environments and Baselines -- 5.3 Performance on Policy Value Difference Evaluation -- 5.4 Performance on Policy Ranking -- 5.5 Performance on Policy Improvement -- 5.6 Analysis on Hyperparameter -- 6 Conclusion -- References."]
[{"$nulls":4},"Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 Markov Decision Process and RL -- 3.2 Rainbow Agent -- 4 Rainbow Ensemble -- 5 Auxiliary Tasks for Ensemble RL -- 5.1 Network Architecture -- 5.2 Model Learning as Auxiliary Tasks -- 5.3 Object and Event Based Auxiliary Tasks -- 6 Theoretical Analysis -- 7 Experiments -- 7.1 Comparison to Prior Works -- 7.2 Bias-Variance-Covariance Measurements -- 7.3 On Independent Training of Ensemble -- 7.4 The Importance of Auxiliary Tasks -- 7.5 On Distributing the Auxiliary Tasks -- 8 Conclusions -- References -- Multi-agent Imitation Learning with Copulas -- 1 Introduction -- 2 Preliminaries -- 3 Modeling Multi-agent Interaction with Copulas -- 3.1 Copulas -- 3.2 "]
[{"$nulls":4},"Multi-agent Imitation Learning with Copulas -- 4 Related Work -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Results -- 5.3 Generalization of Copula -- 5.4 Copula Visualization -- 5.5 Trajectory Generation -- 6 Conclusion and Future Work -- A  Dataset Details -- B  Implementation Details -- References -- CMIX: Deep Multi-agent Reinforcement Learning with Peak and Average Constraints -- 1 Introduction -- 2 Background -- 2.1 QMIX -- 2.2 Constrained Reinforcement Learning -- 3 Problem Formulation -- 4 CMIX -- 4.1 Multi-objective Constrained Problem -- 4.2 CMIX Architecture -- 4.3 Gap Loss Function -- 4.4 CMIX Algorithm -- 5 Experiments -- 5.1 Blocker Game with Travel Cost -- 5.2 Vehicular Network Routing Optimization -- 5.3 Gap Loss Coefficient -- 6 Related Work -- 7 Conclusion -- References -- Model-Based Offline Policy Optimization with Distribution Correcting Regularization -- 1 Introduction -- 2 Preliminary -- 2.1 Markov Decision Processes -- 2.2 Offline RL -- 2.3 Model-Based RL -- 3 A Lower Bound of the True Expected Return -- 4 Method -- 4.1 Overall Framework."]
[{"$nulls":4},"4.2 Ratio Estimation via DICE -- 5 Experiment -- 5.1 Comparative Evaluation -- 5.2 Empirical Analysis -- 6 Related Work -- 6.1 Model-Free Offline RL -- 6.2 Model-Based Offline RL -- 7 Conclusion -- References -- Disagreement Options: Task Adaptation Through Temporally Extended Actions -- 1 Introduction -- 2 Preliminaries -- 3 Disagreement Options -- 3.1 Task Similarity: How to Select Relevant Priors? -- 3.2 Task Adaptation: How Should We Use the Prior Knowledge? -- 3.3 Prior Policy Acquisition -- 4 Experiments -- 4.1 3D MiniWorld -- 4.2 Photorealistic Simulator -- 5 Towards Real-World Task Adaptation -- 6 Related Work -- 7 Discussion -- 8 Conclusion -- References -- Deep Adaptive Multi-intention Inverse Reinforcement Learning -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 Approach -- 4.1 First Solution with Stochastic Expectation Maximization -- 4.2 Second Solution with Monte Carlo Expectation Maximization -- 5 Experimental Results -- 5.1 Benchmarks -- 5.2 Models -- 5.3 Metric -- 5.4 Implementations Details -- 5.5 Results -- 6 Conclusions -- References -- Unsupervised Task Clustering for Multi-task Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Background and Notation -- 4 Clustered Multi-task Learning -- 4.1 Convergence Analysis -- 5 Experiments -- 5.1 Pendulum -- 5.2 Bipedal Walker -- 5.3 Atari -- 5.4 Ablations -- 6 Conclusion -- References -- Deep Model Compression via Two-Stage Deep Reinforcement Learning -- 1 Introduction -- 2 A Deep Reinforcement Learning Compression Framework -- 2.1 State -- 2.2 Action -- 2.3 Reward -- 2.4 The Proposed DRL Compression Structure -- 3 Pruning -- 3.1 Pruning from C Dimension: Channel Pruning -- 3.2 Pruning from H and W Dimensions: Variational Pruning -- 4 Quantization -- 5 Experiments -- 5.1 Settings -- 5.2 MNIST and CIFAR-10 -- 5.3 ImageNet."]
[{"$nulls":4},"5.4 Variational Pruning via Information Dropout -- 5.5 Single Layer Acceleration Performance -- 5.6 Time Complexity -- 6 Conclusion -- References -- Dropout's Dream Land: Generalization from Learned Simulators to Reality -- 1 Introduction -- 2 Related Works -- 2.1 Dropout -- 2.2 Domain Randomization -- 2.3 World Models -- 3 Dropout's Dream Land -- 3.1 Learning the Dream Environment -- 3.2 Interacting with Dropout's Dream Land -- 3.3 Training the Controller -- 4 Experiments -- 4.1 Comparison with Baselines -- 4.2 Inference Dropout and Dream2Real Generalization -- 4.3 When Should Dropout Masks Be Randomized During Controller Training? -- 4.4 Comparison to Standard Regularization Methods -- 4.5 Comparison to Explicit Ensemble Methods -- 5 Conclusion -- References -- Goal Modelling "]
[{"$nulls":4},"for Deep Reinforcement Learning Agents -- 1 Introduction -- 2 Background -- 3 Deep Reinforcement Learning with Goal Net -- 4 Experiments -- 4.1 Two Keys -- 4.2 3D Four Rooms with Subgoals -- 4.3 Kitchen Navigation and Interaction -- 5 Related Work -- 6 Discussion and Conclusion -- References -- Time Series, Streams, and Sequence Models -- Deviation-Based Marked Temporal Point Process for Marker Prediction -- 1 Introduction -- 2 Related Work -- 3 Proposed Algorithm -- 3.1 Problem Definition -- 3.2 Preliminaries -- 3.3 Proposed Deviation-Based Marked Temporal Point Process -- 3.4 Implementation Details -- 4 Experiments and Protocols -- 5 Results and Analysis -- 6 Conclusion and Discussion -- References -- Deep Structural Point Process for Learning Temporal Interaction Networks -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Temporal Interaction Network -- 3.2 Temporal Point Process -- 4 Proposed Model -- 4.1 Overview -- 4.2 Embedding Layer -- 4.3 Topological Fusion Encoder -- 4.4 Attentive Shift Encoder -- 4.5 Model Training -- 4.6 Model Analysis -- 5 Experiments."]
[{"$nulls":4},"5.1 Datasets."]
[null,null,null,"Sommario/riassunto","The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media."]