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Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions
Open accessAbstract
We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitous, from robot planning algorithms that rely on pedestrian predictions, to calibrating autonomous manufacturing to exhibit high throughput and low error, to the choice of trusting a nominal policy versus switching to a safe backup policy at run-time. The decisions produced by our algorithms are safe in the sense that they come with provable statistical guarantees of having low risk without any assumptions on the world model whatsoever; the observations need not be I.I.D. and can even be adversarial. The theory extends results from conformal prediction to calibrate decisions directly, without requiring the construction of prediction sets. Experiments demonstrate the utility of our approach in robot motion planning around humans and robot manufacturing.
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CCO calibrates conservatism online using Conformal Decision Theory, ensuring that undesirable outcomes remain below a user-specified target threshold with finite-time bounds and no distributional assumptions.
“CCO calibrates this conservatism online using Conformal Decision Theory, ensuring that undesirable outcomes remain below a user-specified target threshold with finite-time bounds and no distributional assumptions.”
From Calibrating Conservatism for Scalable Oversight (2026) · cites this paper as Lekeufack et al., 2024
The Introduction and Section III describe online adjustment of conservatism and risk control for arbitrary input-target sequences. The finite-time bound, however, is on average loss and permits it to exceed the user-specified threshold by a term that shrinks over time; the online guarantee also assumes the decision family is eventually safe. Thus the source does not guarantee that undesirable outcomes always remain below the threshold. (LLM, conf=0.87)
Experimental — accuracy not yet measuredCDT guarantees that, for any realization of the deployment sequence, the empirical average loss converges to at most α, with explicit finite-time bounds depending on λ_safe, λ_0, and η.
“CDT guarantees that, for any realization of the deployment sequence, the empirical average loss converges to at most α, with explicit finite-time bounds depending on λ safe , λ 0 , and η.”
From Calibrating Conservatism for Scalable Oversight (2026) · cites this paper as Lekeufack et al., 2024
Theorem 1 (Section III; page unavailable) bounds empirical risk for any realization of the data, and its finite-time bound approaches the target risk level. The guarantee requires an eventually safe decision family with ε_safe ≤ ε; the citing sentence omits that condition. The bound also includes K, and the paper uses ε and λ₁ rather than α and λ₀. (LLM, conf=0.88)
Experimental — accuracy not yet measuredCDT provides a general framework for online calibration of decision rules with worst-case risk control.
“Conformal Decision Theory (CDT), introduced by Lekeufack et al. (2024), provides a general framework for online calibration of decision rules with worst-case risk control.”
From Calibrating Conservatism for Scalable Oversight (2026) · cites this paper as Lekeufack et al., 2024
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Metadata
- Authors
- Lekeufack, J., Angelopoulos, A. N., Bajcsy, A., Jordan, M. I., & Malik, J.
- Affiliations
- University of California, Berkeley (US)
- Carnegie Mellon University (US)
- Venue
- 2024 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2024-05-13
- Pages
- 11668-11675
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W4401414276
- Cited by
- 18
- References
- 53
- Citations by year
- 2026: 2 · 2025: 12 · 2024: 4
- FWCI
- 5.31
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- conformal prediction, statistical guarantees, autonomous manufacturing, non-IID data
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Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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