Active lifting of data distributions via latent-sequence sampling and max-rate uncertainty reduction formally derives slow-thinking LLMs and places them on representation and sampler hierarchies that can be climbed.
Theory of reproducing kernels.Transactions of the American mathematical society, 68(3):337–404
4 Pith papers cite this work. Polarity classification is still indexing.
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Kernel covariance embeddings of non-atomic Borel probability measures on locally compact Polish spaces induce singular centered Gaussians in the RKHS, making equality testing equivalent to singularity testing via the Feldman-Hajek dichotomy.
KNM is a new unsupervised kernel-based method for fault detection in PV systems that achieves higher accuracy than standard benchmarks like OCSVM, iForest, and LOF on sensor faults and partial shading scenarios.
xRFM merges kernel-based feature learning with tree structures for scalable, interpretable tabular modeling and reports top performance on 100 regression and competitive results on 200 classification datasets versus 31 baselines including GBDTs and TabPFNv2.
citing papers explorer
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A First-Principles Theory of Slow Thinking and Active Perception
Active lifting of data distributions via latent-sequence sampling and max-rate uncertainty reduction formally derives slow-thinking LLMs and places them on representation and sampler hierarchies that can be climbed.
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Kernel Embeddings and the Separation of Measure Phenomenon
Kernel covariance embeddings of non-atomic Borel probability measures on locally compact Polish spaces induce singular centered Gaussians in the RKHS, making equality testing equivalent to singularity testing via the Feldman-Hajek dichotomy.
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An unsupervised kernel norm monitoring for fault detection in a time series photovoltaic system
KNM is a new unsupervised kernel-based method for fault detection in PV systems that achieves higher accuracy than standard benchmarks like OCSVM, iForest, and LOF on sensor faults and partial shading scenarios.
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xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
xRFM merges kernel-based feature learning with tree structures for scalable, interpretable tabular modeling and reports top performance on 100 regression and competitive results on 200 classification datasets versus 31 baselines including GBDTs and TabPFNv2.