Titlebook: Web and Big Data; 6th International Jo Bohan Li,Lin Yue,Toshiyuki Amagasa Conference proceedings 2023 The Editor(s) (if applicable) and The

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Non-stationary Dueling Bandits for Online Learning to Rank non-stationary dueling bandits with strong theoretical guarantees. The main idea of our method is to run multiple dueling bandits gradient descent (DBGD) algorithms with different step sizes in parallel and employ a meta algorithm to dynamically combine these DBGD algorithms according to their real

enterprise

Non-stationary Dueling Bandits for Online Learning to Rank non-stationary dueling bandits with strong theoretical guarantees. The main idea of our method is to run multiple dueling bandits gradient descent (DBGD) algorithms with different step sizes in parallel and employ a meta algorithm to dynamically combine these DBGD algorithms according to their real

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High-Order Correlation Embedding for Large-Scale Multi-modal Hashingerent modalities. Moreover, DMCH selects the training samples from reliable to unreliable ones to extract heuristic information contained by the instance learning order, which increases the robustness of the model. Furthermore, the specific semantic labels are utilized as specific prior knowledge to

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conscience

Reasoning Path Generation for Answering Multi-hop Questions Over Knowledge Graphy in the question can be linked as the beginning of the reasoning path. Furthermore, we propose a reward shaping policy consisting of three parts to enhance intermediate supervision signals, which alleviates the problem of reward delay and sparsity of reward. Extensive experiments on multiple benchm

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Min Xieo increasing numbers of governmental agencies and legislative bodies around the world, for some of these chemicals have resulted in a few mishaps from improper 978-1-4615-8458-2Series ISSN 0179-5953 Series E-ISSN 2197-6554

Osmosis

Min Xie to increasing numbers of governmental agencies and legislative bodies around the world, for some of these chemicals have resulted in a few mishaps from imprope978-1-4615-8469-8978-1-4615-8467-4Series ISSN 0179-5953 Series E-ISSN 2197-6554