Protocol learns k-local Lindbladians to ε accuracy with Õ(n^{2k}/ε²) samples and projects to valid generators; improves to log n under sparsity assumptions.
Peng Zhao, Yu-Jie Zhang, Lijun Zhang, and Zhi-Hua Zhou
5 Pith papers cite this work, alongside 316 external citations. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
Introduces budgeted heteroskedastic multi-judge estimation and proves instance-optimality of an adaptive inverse-variance weighted estimator via matching upper and lower bounds.
A direct plug-in kernel estimator for Schrödinger bridge time-series drifts achieves uniform non-asymptotic bounds, pointwise CLT under undersmoothing, and minimax-rate optimal adaptive selection.
The paper defines an intrinsic drift budget C_T in Fisher-Rao distance along the learner-environment trajectory and proves prequential reproducibility gaps bounded by order T^{-1/2} + C_T/T with a matching lower bound on regular subclasses.
Nearest-neighbour matching achieves usual convergence rates under general transferability conditions on source-target distribution pairs, relaxing compact support and bounded density assumptions.
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Robust Structure Learning of $k$-local Lindbladians
Protocol learns k-local Lindbladians to ε accuracy with Õ(n^{2k}/ε²) samples and projects to valid generators; improves to log n under sparsity assumptions.
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Instance-Optimal Estimation with Multiple LLM Judges on a Budget
Introduces budgeted heteroskedastic multi-judge estimation and proves instance-optimality of an adaptive inverse-variance weighted estimator via matching upper and lower bounds.
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Direct Estimation of Schr\"odinger Bridge Time-Series Drifts: Finite-Sample, Asymptotic, and Adaptive Guarantees
A direct plug-in kernel estimator for Schrödinger bridge time-series drifts achieves uniform non-asymptotic bounds, pointwise CLT under undersmoothing, and minimax-rate optimal adaptive selection.
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Learning under Distributional Drift: Prequential Reproducibility as an Intrinsic Statistical Resource
The paper defines an intrinsic drift budget C_T in Fisher-Rao distance along the learner-environment trajectory and proves prequential reproducibility gaps bounded by order T^{-1/2} + C_T/T with a matching lower bound on regular subclasses.
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Nearest-Neighbour Matching on Unbounded Supports and Covariate Shift Transfer
Nearest-neighbour matching achieves usual convergence rates under general transferability conditions on source-target distribution pairs, relaxing compact support and bounded density assumptions.