SeqRejectron constructs a stopping rule with a small set of validator policies to achieve horizon-free sample complexity for selective imitation learning under arbitrary dynamics shifts.
IEEE Transactions on information theory , volume=
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A regime theory selects the optimal controller class for LLM action decisions from a nested lattice of four classes using three data-estimable bottlenecks, with a Bernstein-tight threshold and empirical matches on multiple benchmarks.
SSL pretraining enhances calibrated confidence and selective performance in DR screening, yet benefits on reliability plateau and longer pretraining does not reliably improve abstention.
citing papers explorer
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Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
SeqRejectron constructs a stopping rule with a small set of validator policies to achieve horizon-free sample complexity for selective imitation learning under arbitrary dynamics shifts.
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A Regime Theory of Controller Class Selection for LLM Action Decisions
A regime theory selects the optimal controller class for LLM action decisions from a nested lattice of four classes using three data-estimable bottlenecks, with a Bernstein-tight threshold and empirical matches on multiple benchmarks.
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Knowing When Not to Predict: Self Supervised Learning and Abstention for Safer DR Screening
SSL pretraining enhances calibrated confidence and selective performance in DR screening, yet benefits on reliability plateau and longer pretraining does not reliably improve abstention.