First adversarially robust data structure for c-approximate furthest neighbor search with query time matching the best known oblivious results for many parameter regimes.
Omnipredicting single-index models with multi-index models
4 Pith papers cite this work. Polarity classification is still indexing.
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Polynomial-time algorithms for the Polynomial Freiman-Ruzsa theorem and equivalent formulations over F_2^n, based on an optimized quadratic Goldreich-Levin procedure.
Introduces SCDL as a calibration measure that is fully actionable for full swap regret and testable with nearly optimal sample error while satisfying continuity and consistency.
A general framework and query-efficient algorithms for learning structured quantum unitaries based on Pauli spectrum support on small subgroups or sparsity, unifying prior results for multiple circuit classes.
citing papers explorer
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Adversarially Robust Approximate Furthest Neighbor
First adversarially robust data structure for c-approximate furthest neighbor search with query time matching the best known oblivious results for many parameter regimes.
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An algorithmic Polynomial Freiman-Ruzsa theorem
Polynomial-time algorithms for the Polynomial Freiman-Ruzsa theorem and equivalent formulations over F_2^n, based on an optimized quadratic Goldreich-Levin procedure.
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Testable and Actionable Calibration for Full Swap Regret
Introduces SCDL as a calibration measure that is fully actionable for full swap regret and testable with nearly optimal sample error while satisfying continuity and consistency.
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Efficient Learning of Structured Quantum Circuits via Pauli Dimensionality and Sparsity
A general framework and query-efficient algorithms for learning structured quantum unitaries based on Pauli spectrum support on small subgroups or sparsity, unifying prior results for multiple circuit classes.