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  1. Optimizing Data Collection for Machine Learning

    Published in JMLR (2026)
    Subjects: “…Artificial Intelligence & Machine Learning…”
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  2. An Augmentation Overlap Theory of Contrastive Learning

    Published in JMLR (2026)
    Subjects: “…Artificial Intelligence & Machine Learning…”
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  3. Geometry and Stability of Supervised Learning Problems

    Published in JMLR (2026)
    Subjects: “…Artificial Intelligence & Machine Learning…”
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  4. Reinforcement Learning for Infinite-Dimensional Systems

    Published in JMLR (2026)
    Subjects: “…Artificial Intelligence & Machine Learning…”
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  5. Categorical Semantics of Compositional Reinforcement Learning

    Published in JMLR (2026)
    Subjects: “…Artificial Intelligence & Machine Learning…”
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  6. Learning conditional distributions on continuous spaces

    Published in JMLR (2026)
    Subjects: “…Artificial Intelligence & Machine Learning…”
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  7. Optimizing Data Collection for Machine Learning

    Published in JMLR (2026)
    Subjects: “…Artificial Intelligence & Machine Learning…”
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  8. An Augmentation Overlap Theory of Contrastive Learning

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  9. Geometry and Stability of Supervised Learning Problems

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  10. Reinforcement Learning for Infinite-Dimensional Systems

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  11. Learning to Think from Multiple Thinkers

    Published in ArXiv cs.CC Recent Papers (2026)
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  12. Categorical Semantics of Compositional Reinforcement Learning

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  13. Possibilistic Predictive Uncertainty for Deep Learning

    Published in ArXiv cs.AI Recent Papers (2026)
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  14. Learning conditional distributions on continuous spaces

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  15. Exact and Approximate Algorithms for Polytree Learning

    Published in ArXiv cs.DS Recent Papers (2026)
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  16. Optimizing Data Collection for Machine Learning

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  17. Interpreting Reinforcement Learning Agents with Susceptibilities

    Published in ArXiv cs.LG Recent Papers (2026)
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  18. Learning Tree Automata with Term Rewriting

    Published in ArXiv cs.FL Recent Papers (2026)
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  19. Learning of Structurally Unambiguous Probabilistic Grammars

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  20. Residuality and Learning for Nondeterministic Nominal Automata

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