| 1 |
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models |
提出离线分布鲁棒性蒸馏框架以提升视觉模型性能 |
distillation foundation model |
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| 2 |
Efficient Symbolic Policy Learning with Differentiable Symbolic Expression |
提出高效符号策略学习方法以解决深度强化学习的复杂性问题 |
reinforcement learning deep reinforcement learning DRL |
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| 3 |
DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing |
提出DreamSmooth以解决稀疏奖励预测瓶颈问题 |
reinforcement learning |
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| 4 |
A Statistical Guarantee for Representation Transfer in Multitask Imitation Learning |
提出统计保证以提升多任务模仿学习中的表示转移效率 |
imitation learning |
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| 5 |
Anytime-Competitive Reinforcement Learning with Policy Prior |
提出随时竞争强化学习算法以解决成本约束问题 |
reinforcement learning |
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| 6 |
Invariant Causal Imitation Learning for Generalizable Policies |
提出不变因果模仿学习以解决政策泛化问题 |
imitation learning |
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| 7 |
Diffusion Models for Reinforcement Learning: A Survey |
综述扩散模型在强化学习中的应用与挑战 |
reinforcement learning |
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| 8 |
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning |
提出RMechRP以解决化学反应预测的可解释性问题 |
contrastive learning |
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| 9 |
Dynamic Fair Federated Learning Based on Reinforcement Learning |
提出动态公平联邦学习算法DQFFL以解决数据异质性问题 |
reinforcement learning |
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| 10 |
A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories |
提出TAILO以解决离线模仿学习中的不完整轨迹问题 |
imitation learning behavior cloning |
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