Paper

  • Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate

    Can Jin, Tong Che, Hongwu Peng, Yiyuan Li, Dimitris Metaxas, Marco Pavone p966-994 from Advances in Neural Information Processing Systems 37
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  • Learning from Uncertain Data: From Possible Worlds to Possible Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Uncertain Data: From Possible Worlds to Possible Models

    Jiongli Zhu, Su Feng, Boris Glavic, Babak Salimi p106955-107002 from Advances in Neural Information Processing Systems 37
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  • Learning from Visual Observation via Offline Pretrained State-to-Go Transformer

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Visual Observation via Offline Pretrained State-to-Go Transformer

    Bohan Zhou, Ke Li, Jiechuan Jiang, Zongqing Lu p59585-59605 from Advances in Neural Information Processing Systems 36
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  • Learning Functional Transduction

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Functional Transduction

    Mathieu Chalvidal, Thomas Serre, Rufin Vanrullen p73852-73865 from Advances in Neural Information Processing Systems 36
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  • Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm

    Qinbo Bai, Washim Mondal, Vaneet Aggarwal p108566-108599 from Advances in Neural Information Processing Systems 37
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  • Learning General World Models in a Handful of Reward-Free Deployments

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning General World Models in a Handful of Reward-Free Deployments

    Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip Ball, Oleh Rybkin, S Roberts, Tim Rocktäschel, Edward Grefenstette p26820-26838 from Advances in Neural Information Processing Systems 35
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  • Learning Generalizable Agents via Saliency-guided Features Decorrelation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Generalizable Agents via Saliency-guided Features Decorrelation

    Sili Huang, Yanchao Sun, Jifeng Hu, Siyuan Guo, Hechang Chen, Yi Chang, Lichao Sun, Bo Yang p39363-39381 from Advances in Neural Information Processing Systems 36
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  • Learning Generalizable Models for Vehicle Routing Problems Via Knowledge Distillation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Generalizable Models for Vehicle Routing Problems Via Knowledge Distillation

    Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, Yeow Meng Chee p31226-31238 from Advances in Neural Information Processing Systems 35
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  • Learning Generalizable Part-Based Feature Representation for 3D Point Clouds

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Generalizable Part-Based Feature Representation for 3D Point Clouds

    Xin Wei, Xiang Gu, Jian Sun p29305-29318 from Advances in Neural Information Processing Systems 35
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  • Learning Generalized Linear Programming Value Functions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Generalized Linear Programming Value Functions

    Tu Anh-Nguyen, Joey Huchette, Christian Tjandraatmadja p135437-135463 from Advances in Neural Information Processing Systems 37
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  • Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems

    Rushang Karia, Rashmeet Kaur Nayyar, Siddharth Srivastava p30625-30637 from Advances in Neural Information Processing Systems 35
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  • Learning Goal-Conditioned Representations for Language Reward Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Goal-Conditioned Representations for Language Reward Models

    Vaskar Nath, Dylan Slack, Jeff Da, Yuntao Ma, Hugh Zhang, Spencer Whitehead, Sean Hendryx p117070-117108 from Advances in Neural Information Processing Systems 37
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