A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.
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SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.
Geometric Pareto Control embeds Pareto solutions in a Lie group submanifold and navigates via Riemannian gradient flow to achieve 100% feasibility and low suboptimality in control tasks without retraining.
citing papers explorer
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Multi-Fidelity Quantile Regression
A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.
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Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions
SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.
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Geometric Pareto Control: Riemannian Gradient Flow of Energy Function via Lie Group Homotopy
Geometric Pareto Control embeds Pareto solutions in a Lie group submanifold and navigates via Riemannian gradient flow to achieve 100% feasibility and low suboptimality in control tasks without retraining.