Under independence and tail conditions on random symmetric matrices, the DNN relaxation of the standard quadratic program is exact with probability tending to 1, the optimizer is unique and rank one, and recoverable in O(n^2) time.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
In-context symbolic regression methods improve robustness of symbolic formula recovery from KANs, cutting median OFAT test MSE by up to 99.8 percent across hyperparameter sweeps.
A framework combining Feynman-Kac correctors with a guiding potential mines and repairs novel trajectories to enable diffusion policies to discover diverse executable behaviors in robotic manipulation.
Introduces a traceable virtual sea trial framework in the MARUS simulator for automated TC and ZZ manoeuvres with data conditioning to produce SI-ready datasets for USV hydrodynamic derivative identification.
citing papers explorer
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Exactness of the DNN Relaxation for Random Standard Quadratic Programs
Under independence and tail conditions on random symmetric matrices, the DNN relaxation of the standard quadratic program is exact with probability tending to 1, the optimizer is unique and rank one, and recoverable in O(n^2) time.
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In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks
In-context symbolic regression methods improve robustness of symbolic formula recovery from KANs, cutting median OFAT test MSE by up to 99.8 percent across hyperparameter sweeps.
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Guided Discovery of New Behaviors using Diffusion Policies
A framework combining Feynman-Kac correctors with a guiding potential mines and repairs novel trajectories to enable diffusion policies to discover diverse executable behaviors in robotic manipulation.
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Traceable Virtual Sea Trials in the Marine Robotics Unity Simulator for Manoeuvring Assessment of Unmanned Surface Vehicles
Introduces a traceable virtual sea trial framework in the MARUS simulator for automated TC and ZZ manoeuvres with data conditioning to produce SI-ready datasets for USV hydrodynamic derivative identification.