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sample-complexity

topic5 events
papersTODAY 04:00 UTC

Study Tightens Sample Complexity Bounds for Valiant's CNF Learning Algorithm

Researchers revisit Valiant's 1984 algorithm for learning CNF formulas with bounded clause size and variable degree from uniformly random satisfying assignments. Working in the local lemma regime, they derive near-tight sample complexity guarantees under a stated condition relating clause size to variable degree. The work is a theoretical contribution to computational learning theory rather than a practical tool release.

research/learning-theory/sample-complexity/cnf
papersTODAY 04:00 UTC

New Gap Entropy Method Nears Instance-Wise Optimal Best-Arm Identification

Researchers introduce a quantity called gap entropy for the best-arm identification problem with independent Gaussian arms, where the goal is to find the highest-mean arm using as few samples as possible at a given confidence level. They show that an algorithm based on this measure comes close to the optimal sample complexity for each individual problem instance. The work is a theoretical contribution posted to arXiv and has not yet been peer reviewed.

arxiv/bandits/sample-complexity/theory
papersSEP 10 04:00 UTC

Paper Analyzes Sample Complexity of Quantum Entanglement Allocation

A new arXiv preprint examines how many past requests are required to determine which qubits should be entangled. The authors find the answer hinges on the allocation choices produced by the queries, and that a larger memory need not require additional data. The memory in their model holds a classical bit plus an answer, according to the abstract.

quantum-computing/research/sample-complexity/entanglement
papersSEP 10 04:00 UTC

Optimal sample complexity for low-rank quantum state tomography with joint measurements

A new paper determines the optimal sample complexity for estimating an unknown low-rank quantum state when each measurement can act jointly on at most t copies. The results characterize how the state's rank and dimension shape the number of samples needed to reach a target error in this bounded-measurement setting.

quantum/research/tomography/sample-complexity
papersSEP 10 04:00 UTC

Researchers prove gap-entropy conjecture for fixed-confidence best-arm identification

A new arXiv paper in machine learning theory settles the gap-entropy conjecture, an open problem in best-arm identification for multi-armed bandits. The proof covers the fixed-confidence setting with independent unit-variance Gaussian arms, means bounded in [0,1], and a single optimal arm. The result confirms that the entropy of suboptimality gaps governs the sample complexity needed to identify the best arm.

bandits/theory/sample-complexity/best-arm-identification