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Adaptive Conformal Guidance for Learning under Uncertainty

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Conformal PredictionLearning under UncertaintyLearning with Guidance
Abstract:

Learning with guidance has proven effective across a wide range of machine learning systems. Guidance may, for example, come from annotated datasets in supervised learning, pseudo-labels in semi-supervised learning, and expert demonstration policies in reinforcement learning. However, guidance signals can be noisy due to domain shifts and limited data availability and may not generalize well. Blindly trusting such signals when they are noisy, incomplete, or misaligned with the target domain can lead to degraded performance. To address these challenges, we propose Adaptive Conformal Guidance (AdaConG), a simple yet effective approach that dynamically modulates the influence of guidance signals based on their associated uncertainty, quantified via split conformal prediction (CP). By adaptively adjusting to guidance uncertainty, AdaConG enables models to reduce reliance on potentially misleading signals and enhance learning performance. We validate AdaConG across diverse tasks, including knowledge distillation, semi-supervised image classification, gridworld navigation, and autonomous driving. Experimental results demonstrate that AdaConG improves performance and robustness under imperfect guidance, e.g., in gridworld navigation, it accelerates convergence and achieves over ×6\times 6 higher rewards than the best-performing baseline. These results highlight AdaConG as a broadly applicable solution for learning under uncertainty.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes Adaptive Conformal Guidance (AdaConG), a framework that uses split conformal prediction to quantify uncertainty in guidance signals and adaptively modulate their influence during training. Within the taxonomy, it resides in the 'Conformal Prediction-Based Guidance Modulation' leaf, which contains only one other sibling paper (AUKT). This leaf sits under 'Uncertainty-Driven Guidance and Decision Modulation', a moderately populated branch with approximately 10 papers across three sub-branches. The sparse population of the conformal prediction leaf suggests this is an emerging research direction rather than a crowded area.

The taxonomy reveals that neighboring leaves focus on probabilistic uncertainty modulation, including generative model approaches (diffusion models) and inference-based methods (adaptive dropout, neural network uncertainty). The broader 'Uncertainty-Driven Guidance' branch contrasts with 'Adaptive Control with Uncertainty Estimation', which emphasizes parameter adaptation and observer-based methods for control systems. AdaConG's positioning indicates it bridges uncertainty quantification (via conformal prediction) with guidance signal modulation, diverging from control-theoretic approaches that directly estimate system parameters or disturbances. The scope notes clarify that this branch excludes direct control methods, focusing instead on decision and guidance modulation.

Among 27 candidates examined across three contributions, no clearly refutable prior work was identified. The core AdaConG framework examined 10 candidates with zero refutations, suggesting limited direct overlap in the conformal prediction-based guidance modulation space. The broad applicability claim examined 7 candidates without refutations, indicating the cross-domain validation (knowledge distillation, semi-supervised learning, navigation, autonomous driving) may represent novel application breadth. The embedding of conformal prediction into training loops examined 10 candidates with no refutations. These statistics reflect a focused search scope rather than exhaustive coverage, and the sparse conformal prediction leaf corroborates limited prior work in this specific direction.

Based on the limited search scope of 27 candidates and the sparse taxonomy leaf containing only one sibling paper, the work appears to occupy a relatively unexplored niche within uncertainty-driven guidance modulation. The absence of refutable candidates across all contributions suggests novelty, though this conclusion is constrained by the top-K semantic search methodology. The taxonomy structure indicates that while uncertainty quantification and adaptive control are mature areas, the specific integration of conformal prediction for guidance signal modulation during training represents a less-developed research direction.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
27
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: adaptive modulation of guidance signals based on uncertainty quantification. This field addresses how control and decision systems can dynamically adjust their guidance strategies by leveraging estimates of uncertainty, ensuring robust performance under model mismatch, disturbances, and incomplete information. The taxonomy reveals several major branches: Adaptive Control with Uncertainty Estimation focuses on parameter adaptation and observer-based methods that refine system models online, often employing techniques like neural networks or disturbance observers (e.g., Spacecraft Disturbance Estimation[12], Active Disturbance Rejection[27]). Uncertainty-Driven Guidance and Decision Modulation emphasizes direct use of uncertainty metrics to shape guidance laws, including conformal prediction frameworks and adaptive dropout strategies (Adaptive Dropout Rates[6]). Constraint Handling and Safety-Critical Control tackles systems where safety guarantees are paramount, leveraging barrier functions and tube-based predictive control (Control Barrier Functions[32], Tube Model Predictive[20]). Robust and Adaptive Control for Specific System Classes tailors methods to particular dynamics such as flexible joints, unmanned vehicles, or nonlinear mechanical systems (Flexible Joint Control[5], Unmanned Surface Vessels[29]). Application-Specific Uncertainty-Aware Systems explores domain-driven implementations ranging from robotics to traffic management (Robot Crowd Navigation[28], Proactive Traffic Signal[18]). Several active lines of work highlight contrasting philosophies and trade-offs. One cluster pursues real-time disturbance estimation and rejection, balancing computational efficiency with robustness in the face of unknown dynamics (Iterative Learning Control[9], Command Filter Tracking[10]). Another emphasizes formal safety certificates through barrier functions and set-based methods, trading off conservatism for provable guarantees (Safe PDE Control[1]). Within the Uncertainty-Driven Guidance branch, Adaptive Conformal Guidance[0] stands out by integrating conformal prediction to modulate guidance signals, closely aligning with AUKT[8], which also leverages uncertainty quantification for adaptive decision-making. Compared to works like Knee Exoskeleton SMC[3] or Coordinated Robots Impedance[11] that focus on specific robotic platforms with sliding mode or impedance control, Adaptive Conformal Guidance[0] offers a more general framework for uncertainty-aware modulation applicable across diverse guidance tasks. This positioning reflects a broader trend toward principled uncertainty quantification methods that can inform adaptive strategies without requiring exhaustive domain-specific tuning.

Claimed Contributions

Adaptive Conformal Guidance (AdaConG) framework

The authors introduce AdaConG, a framework that uses split conformal prediction to quantify uncertainty in guidance signals and adaptively weight their influence during training. This enables models to reduce reliance on potentially misleading guidance while maintaining robust learning capabilities.

10 retrieved papers
Broad applicability across diverse learning systems

The authors demonstrate that their framework can be applied to multiple learning paradigms, including supervised learning with knowledge distillation, semi-supervised learning with pseudo-labels, and reinforcement learning with imitation policy guidance, making it a general solution for learning under uncertainty.

7 retrieved papers
Embedding conformal prediction into training loop

Unlike prior work that uses conformal prediction primarily for post-hoc calibration, the authors integrate split conformal prediction directly into the training process to inform real-time training dynamics by adaptively weighting guidance signals based on their uncertainty.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Adaptive Conformal Guidance (AdaConG) framework

The authors introduce AdaConG, a framework that uses split conformal prediction to quantify uncertainty in guidance signals and adaptively weight their influence during training. This enables models to reduce reliance on potentially misleading guidance while maintaining robust learning capabilities.

Contribution

Broad applicability across diverse learning systems

The authors demonstrate that their framework can be applied to multiple learning paradigms, including supervised learning with knowledge distillation, semi-supervised learning with pseudo-labels, and reinforcement learning with imitation policy guidance, making it a general solution for learning under uncertainty.

Contribution

Embedding conformal prediction into training loop

Unlike prior work that uses conformal prediction primarily for post-hoc calibration, the authors integrate split conformal prediction directly into the training process to inform real-time training dynamics by adaptively weighting guidance signals based on their uncertainty.

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