| 1 |
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models |
提出统一因果表示学习与基础模型的方法以学习可解释概念 |
representation learning large language model foundation model |
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| 2 |
Reinforcement Learning from Human Feedback with Active Queries |
提出基于主动查询的强化学习方法以提高人类反馈效率 |
reinforcement learning RLHF DPO |
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| 3 |
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks |
提出偏差利用机制以解决连续控制强化学习中的估计偏差问题 |
reinforcement learning deep reinforcement learning policy learning |
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| 4 |
Graph Contrastive Learning with Low-Rank Regularization and Low-Rank Attention for Noisy Node Classification |
提出GCL-LRR以解决图神经网络在噪声节点分类中的挑战 |
representation learning contrastive learning |
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| 5 |
InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling |
提出InfoRM以解决强化学习中的奖励黑客问题 |
reinforcement learning RLHF |
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| 6 |
Learning Interpretable Policies in Hindsight-Observable POMDPs through Partially Supervised Reinforcement Learning |
提出部分监督强化学习框架以提升POMDP中的可解释性 |
reinforcement learning deep reinforcement learning |
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| 7 |
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption |
提出CR-OMLE和CR-PMLE以解决模型基础强化学习中的对抗性腐蚀问题 |
reinforcement learning model-based RL |
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| 8 |
Dataset Clustering for Improved Offline Policy Learning |
提出行为感知深度聚类以提升离线策略学习效果 |
policy learning |
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| 9 |
Active Disruption Avoidance and Trajectory Design for Tokamak Ramp-downs with Neural Differential Equations and Reinforcement Learning |
提出基于强化学习的托卡马克等离子体安全降温策略 |
reinforcement learning |
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| 10 |
When Representations Align: Universality in Representation Learning Dynamics |
提出有效理论以揭示表示学习动态的普遍性 |
representation learning |
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| 11 |
FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning |
提出FedSiKD以解决非独立同分布数据下的联邦学习问题 |
distillation |
✅ |
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