Proposes Cost-Aware Adaptive Conformal Inference framework providing dual statistical guarantees on long-run violation frequency and cumulative violation cost for runtime assurance in dynamic environments.
Predictability: A problem partly solved
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Evolving lifted data vectors under a chaotic dynamical system before softmax classification accelerates training and improves accuracy over standard and lifted-only baselines on perturbed orthogonal vectors.
Small transformers learn to forecast unseen dynamical systems in-context by using delay embeddings to recover the manifold and forecasting its invariant sets via a transfer-operator strategy.
citing papers explorer
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Cost-Aware Adaptive Conformal Inference for Runtime Assurance in Dynamic Environments
Proposes Cost-Aware Adaptive Conformal Inference framework providing dual statistical guarantees on long-run violation frequency and cumulative violation cost for runtime assurance in dynamic environments.
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Enhancing classification accuracy through chaos
Evolving lifted data vectors under a chaotic dynamical system before softmax classification accelerates training and improves accuracy over standard and lifted-only baselines on perturbed orthogonal vectors.
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Transformers for dynamical systems learn transfer operators in-context
Small transformers learn to forecast unseen dynamical systems in-context by using delay embeddings to recover the manifold and forecasting its invariant sets via a transfer-operator strategy.