Conformal risk control for bounded non-monotone losses over a grid of size m achieves excess risk of order sqrt(log m / n) with n calibration samples, which is minimax optimal.
Achieving risk control in online learning settings.arXiv preprint arXiv:2205.09095
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Co-GLANCE distills vision-language models into an end-to-end onboard model for occlusion segmentation and robot allocation, using conformal prediction plus selective abstention to trigger active perception and achieve 25-36% higher accuracy with 350x lower latency than cloud baselines.
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Conformal Risk Control under Non-Monotone Losses: Theory and Finite-Sample Guarantees
Conformal risk control for bounded non-monotone losses over a grid of size m achieves excess risk of order sqrt(log m / n) with n calibration samples, which is minimax optimal.
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Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming
Co-GLANCE distills vision-language models into an end-to-end onboard model for occlusion segmentation and robot allocation, using conformal prediction plus selective abstention to trigger active perception and achieve 25-36% higher accuracy with 350x lower latency than cloud baselines.