Paper

  • Replicability in Learning: Geometric Partitions and KKM-Sperner Lemma

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Replicability in Learning: Geometric Partitions and KKM-Sperner Lemma

    Jason Vander Woude, Peter Dixon, A. Pavan, Jamie Radcliffe, N. V. Vinodchandran p78996-79028 from Advances in Neural Information Processing Systems 37
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  • Replicability in Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Replicability in Reinforcement Learning

    Amin Karbasi, Grigoris Velegkas, Lin Yang, Felix Zhou p74702-74735 from Advances in Neural Information Processing Systems 36
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  • Replicable Clustering

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Replicable Clustering

    Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou p39277-39320 from Advances in Neural Information Processing Systems 36
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  • Replicable Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Replicable Reinforcement Learning

    Eric Eaton, Marcel Hussing, Michael Kearns, Jessica Sorrell p15172-15185 from Advances in Neural Information Processing Systems 36
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  • Replicable Uniformity Testing

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Replicable Uniformity Testing

    Sihan Liu, Christopher Ye p32039-32075 from Advances in Neural Information Processing Systems 37
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  • RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content

    João Monteiro, Pierre-André Noël, Étienne Marcotte, Sai Rajeswar, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian p24242-24276 from Advances in Neural Information Processing Systems 37
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  • RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability

    Chuning Zhu, Max Simchowitz, Siri Gadipudi, Abhishek Gupta p32445-32467 from Advances in Neural Information Processing Systems 36
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  • REPORT - Sound As Healing Light Waves

    Interdisciplinary Society For Quantitative Research in Music and Medicine

    REPORT - Sound As Healing Light Waves

    Iasos p154-162 from 7th Biennial Conference of the Interdisciplinary Society for Quantitative Research in Music and Medicine (ISQRMM 2023)
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  • Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning

    Francesca Bartolucci, Emmanuel De Bézenac, Bogdan Raonic, Roberto Molinaro, Siddhartha Mishra, Rima Alaifari p69661-69672 from Advances in Neural Information Processing Systems 36
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  • Representation Learning via Consistent Assignment of Views over Random Partitions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Representation Learning via Consistent Assignment of Views over Random Partitions

    Thalles Santos Silva, Adín Ramírez Rivera p39582-39601 from Advances in Neural Information Processing Systems 36
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  • Representation Noising: A Defence Mechanism Against Harmful Finetuning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Representation Noising: A Defence Mechanism Against Harmful Finetuning

    Domenic Rosati, Jan Wehner, Kai Williams, Lukasz Bartoszcze, David Atanasov, Robie Gonzales, Subhabrata Majumdar, Carsten Maple, Hassan Sajjad, Frank Rudzicz p12636-12676 from Advances in Neural Information Processing Systems 37
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  • Representational Strengths and Limitations of Transformers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Representational Strengths and Limitations of Transformers

    Clayton Sanford, Daniel Hsu, Matus Telgarsky p36677-36707 from Advances in Neural Information Processing Systems 36
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