mlsanityData to 14 Oct 2026

Class-Conditional Conformal Prediction for Signal Temporal Logic Inference with Many Classes

arXiv 2610.14352 ↗First posted 9 Oct 2026cs.LGcited by 0

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Abstract (arXiv)

Signal Temporal Logic (STL) inference provides interpretable formulas for time series classification. For multiclass classification, however, accurate predictions do not necessarily ensure that the inferred formulas are satisfied consistently with the true class. We introduce the positive satisfaction rate and codeword satisfaction rate to quantify satisfaction of required formulas and consistency with the complete class codeword, respectively. We develop a two-stage procedure that first learns discriminative formulas using a margin-based objective and then refines predicate thresholds toward a small positive robustness target while fixing the formula structure and temporal intervals. Satisfaction and ranking penalties discourage violations of required formulas and loss of class separation. The decoding loss is used as a nonconformity score and calibrated separately for each class, providing finite-sample class-conditional coverage under intra-class exchangeability. Experiments on naval, synthetic, and robot manipulation trajectories demonstrate discrepancies between classification accuracy and formula satisfaction. The results further reveal a data-dependent tradeoff between robustness tightness and satisfaction, while class-conditional calibration mitigates undercoverage that can be hidden by pooled calibration.

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References · 18

  1. 2021CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Taskscited by 584
  2. 2020Classification with Valid and Adaptive Coveragecited by 396
  3. 2023Conformal Prediction With Conditional Guaranteescited by 152
  4. 2020Backpropagation through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methodscited by 54
  5. 2023Conformal Prediction for Deep Classifier via Label Rankingcited by 53
  6. 2024TimeX++: Learning Time-Series Explanations with Information Bottleneckcited by 33
  7. 2019Interpretable Classification of Time-Series Data using Efficient Enumerative Techniquescited by 20
  8. 2024Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibrationcited by 19
  9. 2018Information-Guided Temporal Logic Inference with Prior Knowledgecited by 13
  10. 2025Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coveragecited by 13
  11. 2024TLINet: Differentiable Neural Network Temporal Logic Inferencecited by 9
  12. 2022Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Descriptioncited by 8
6 more
  1. 2021Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networkscited by 5
  2. 2024Interpretable Imitation Learning via Generative Adversarial STL Inference and Controlcited by 3
  3. 2024Multi-class Temporal Logic Neural Networkscited by 3
  4. 2024Learning Temporal Logic Predicates from Data with Statistical Guaranteescited by 3
  5. 2025Conformal Prediction for Signal Temporal Logic Inferencecited by 3
  6. 2025Towards Interpretable Concept Learning over Time Series via Temporal Logic Semanticscited by 1

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