Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • When Demonstrations meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Demonstrations meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning

    Alfredo Garcia, Mingyi Hong, Chenliang Li, Siliang Zeng p65531-65565 from Advances in Neural Information Processing Systems 36
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  • When Do Flat Minima Optimizers Work?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Do Flat Minima Optimizers Work?

    Jean Kaddour, Matt Kusner, Linqing Liu, Ricardo Silva p16577-16595 from Advances in Neural Information Processing Systems 35
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  • When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability

    Xiao-Wen Chang, Jie Fu, Chenqing Hua, Jure Leskovec, Qincheng Lu, Sitao Luan, Doina Precup, Minkai Xu, Jiaqi Zhu p28748-28760 from Advances in Neural Information Processing Systems 36
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  • When Do Neural Nets Outperform Boosted Trees on Tabular Data?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Do Neural Nets Outperform Boosted Trees on Tabular Data?

    Micah Goldblum, Sujay Khandagale, Duncan McElfresh, Vishak Prasad C, Ganesh Ramakrishnan, Jonathan Valverde, Colin White p76336-76369 from Advances in Neural Information Processing Systems 36
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  • When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment

    Pierre-Luc Bacon, Benjamin Eysenbach, Michel Ma, Tianwei Ni p50429-50452 from Advances in Neural Information Processing Systems 36
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  • When Does Confidence-Based Cascade Deferral Suffice?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Does Confidence-Based Cascade Deferral Suffice?

    Neha Gupta, Wittawat Jitkrittum, Sanjiv Kumar, Aditya Menon, Harikrishna Narasimhan, Ankit Rawat p9891-9906 from Advances in Neural Information Processing Systems 36
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  • When Does Differentially Private Learning Not Suffer in High Dimensions?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Does Differentially Private Learning Not Suffer in High Dimensions?

    Abhradeep Guha Thakurta, Tatsunori Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin-Tat Lee, Xuechen Li, Daogao Liu p28616-28630 from Advances in Neural Information Processing Systems 35
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  • When Does Dough Become a Bagel? Analyzing the Remaining Mistakes on ImageNet

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Does Dough Become a Bagel? Analyzing the Remaining Mistakes on ImageNet

    Benjamin Caine, Sara Fridovich-Keil, Raphael Gontijo Lopes, Rebecca Roelofs, Vijay Vasudevan p6720-6734 from Advances in Neural Information Processing Systems 35
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  • When Does Group Invariant Learning Survive Spurious Correlations?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Does Group Invariant Learning Survive Spurious Correlations?

    Yimeng Chen, Yanyan Lan, Zhi-Ming Ma, Ruibin Xiong p7038-7051 from Advances in Neural Information Processing Systems 35
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  • When Does Optimizing a Proper Loss Yield Calibration?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Does Optimizing a Proper Loss Yield Calibration?

    Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran p72071-72095 from Advances in Neural Information Processing Systems 36
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  • When does perceptual alignment benefit vision representations?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When does perceptual alignment benefit vision representations?

    Lucy Chai, Trevor Darrell, Stephanie Fu, Phillip Isola, Simon Kornblith, Lukas Muttenthaler, Shobhita Sundaram, Netanel Tamir p55314-55341 from Advances in Neural Information Processing Systems 37
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  • When Does Return-Conditioned Supervised Learning Work for Offline Reinforcement Learning?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Does Return-Conditioned Supervised Learning Work for Offline Reinforcement Learning?

    Alberto Bietti, David Brandfonbrener, Joan Bruna, Jacob Buckman, Romain Laroche p1542-1553 from Advances in Neural Information Processing Systems 35
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  • When is Agnostic Reinforcement Learning Statistically Tractable?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When is Agnostic Reinforcement Learning Statistically Tractable?

    Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nati Srebro p27820-27879 from Advances in Neural Information Processing Systems 36
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  • When is an Embedding Model More Promising than Another?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When is an Embedding Model More Promising than Another?

    Ismail AYED, Jackie CHEUNG, Maxime DARRIN, Philippe FORMONT, Pablo PIANTANIDA p68330-68379 from Advances in Neural Information Processing Systems 37
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  • When Is Inductive Inference Possible?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Is Inductive Inference Possible?

    Zhou Lu p92721-92744 from Advances in Neural Information Processing Systems 37
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  • When is Multicalibration Post-Processing Necessary?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When is Multicalibration Post-Processing Necessary?

    Siddartha Devic, Dutch Hansen, Preetum Nakkiran, Vatsal Sharan p38383-38455 from Advances in Neural Information Processing Systems 37
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  • When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search

    Xuan Chen, Wenbo Guo, Yuzhou Nie, Xiangyu Zhang p26814-26845 from Advances in Neural Information Processing Systems 37
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  • When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models

    Xuming Hu, Yangning Li, Yinghui Li, Yuanzhen Luo, Shirong Ma, Philip Yu, Hai-Tao Zheng, Qingyu Zhou p112433-112458 from Advances in Neural Information Processing Systems 37
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  • When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits

    Achraf Azize, Debabrota Basu p32199-32210 from Advances in Neural Information Processing Systems 35
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  • When to Act and When to Ask: Policy Learning With Deferral Under Hidden Confounding

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When to Act and When to Ask: Policy Learning With Deferral Under Hidden Confounding

    Marah Ghoummaid, Uri Shalit p56108-56135 from Advances in Neural Information Processing Systems 37
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  • When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning

    Chelsea Finn, Archit Sharma, Fahim Tajwar, Annie Xie p16918-16930 from Advances in Neural Information Processing Systems 35
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  • When to Intervene: Learning Optimal Intervention Policies for Critical Events

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When to Intervene: Learning Optimal Intervention Policies for Critical Events

    Chiranjib Bhattacharyya, Niranjan Damera Venkata p30114-30126 from Advances in Neural Information Processing Systems 35
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  • When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment

    Fernando Gonzalez, Zhijing Jin, Ojasv Kamal, Sydney Levine, Rada Mihalcea, Mrinmaya Sachan, Maarten Sap, Bernhard Schölkopf, Josh Tenenbaum p28458-28473 from Advances in Neural Information Processing Systems 35
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  • When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL

    Yarden As, Florian Dörfler, Andreas Krause, Bhavya Sukhija, Lenart Treven p63654-63685 from Advances in Neural Information Processing Systems 37
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