Adaptive Conformal Guidance for Learning under Uncertainty
- URL: http://arxiv.org/abs/2502.16736v4
- Date: Mon, 29 Sep 2025 23:06:11 GMT
- Title: Adaptive Conformal Guidance for Learning under Uncertainty
- Authors: Rui Liu, Peng Gao, Yu Shen, Ming Lin, Pratap Tokekar,
- Abstract summary: We propose Adaptive Conformal Guidance (AdaConG) to modulate the influence of guidance signals based on associated uncertainty.<n>AdaConG enables models to reduce reliance on potentially misleading signals and enhance learning performance.<n>We validate AdaConG across diverse tasks, including knowledge distillation, semi-supervised image classification, gridworld navigation, and autonomous driving.
- Score: 21.41859269536973
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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 $6\times$ higher rewards than the best-performing baseline. These results highlight AdaConG as a broadly applicable solution for learning under uncertainty.
Related papers
- PromptCD: Test-Time Behavior Enhancement via Polarity-Prompt Contrastive Decoding [85.22047087898311]
We introduce Polarity-Prompt Contrastive Decoding (PromptCD), a test-time behavior control method that generalizes contrastive decoding to broader enhancement settings.<n>PromptCD constructs paired positive and negative guiding prompts for a target behavior and contrasts model responses to reinforce desirable outcomes.<n>Experiments on the "3H" alignment objectives demonstrate consistent and substantial improvements, indicating that post-trained models can achieve meaningful self-enhancement purely at test time.
arXiv Detail & Related papers (2026-02-24T08:56:52Z) - Global Variational Inference Enhanced Robust Domain Adaptation [7.414646586981638]
We propose a framework that learns continuous, class-conditional global priors via variational inference to enable structure-aware cross-domain alignment.<n>GVI-DA minimizes domain gaps through latent feature reconstruction, and mitigates posterior collapse using global codebook learning with randomized sampling.<n>It further improves robustness by discarding low-confidence pseudo-labels and generating reliable target-domain samples.
arXiv Detail & Related papers (2025-07-04T04:43:23Z) - Variational Supervised Contrastive Learning [50.79938854370321]
We propose Variational Supervised Contrastive Learning (VarCon), which reformulates supervised contrastive learning as variational inference over latent class variables.<n>VarCon achieves state-of-the-art performance for contrastive learning frameworks, reaching 79.36% Top-1 accuracy on ImageNet-1K and 78.29% on CIFAR-100 with a ResNet-50 encoder.
arXiv Detail & Related papers (2025-06-09T04:19:12Z) - Feedback Guidance of Diffusion Models [14.162420300295365]
Interval-Free Guidance (CFG) has become standard for improving sample fidelity in conditional diffusion models.<n>We propose FeedBack Guidance (FBG), which uses a state-dependent coefficient to self-regulate guidance amounts based on need.
arXiv Detail & Related papers (2025-06-06T13:46:32Z) - KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG [63.82127103851471]
Retrieval-Augmented Generation (RAG) enables large language models to access broader knowledge sources.<n>We demonstrate that enhancing generative models' capacity to process noisy content is equally critical for robust performance.<n>We present KARE-RAG, which improves knowledge utilization through three key innovations.
arXiv Detail & Related papers (2025-06-03T06:31:17Z) - Learning to Reason without External Rewards [100.27210579418562]
Training large language models (LLMs) for complex reasoning via Reinforcement Learning with Verifiable Rewards (RLVR) is effective but limited by reliance on costly, domain-specific supervision.<n>We explore Reinforcement Learning from Internal Feedback (RLIF), a framework that enables LLMs to learn from intrinsic signals without external rewards or labeled data.<n>We propose Intuitor, an RLIF method that uses a model's own confidence, termed self-certainty, as its sole reward signal.
arXiv Detail & Related papers (2025-05-26T07:01:06Z) - Co-STAR: Collaborative Curriculum Self-Training with Adaptive Regularization for Source-Free Video Domain Adaptation [5.122518070721238]
Co-STAR integrates curriculum learning with collaborative self-training between a source-trained teacher and a contrastive vision-language model (CLIP)
Our curriculum learning approach employs a reliability-based weight function that measures bidirectional prediction alignment between the teacher and CLIP, balancing between confident and uncertain predictions.
To further improve adaptation, we propose Adaptive Curriculum Regularization, which modifies the learning priority of samples in a probabilistic, adaptive manner based on their confidence scores and prediction stability.
arXiv Detail & Related papers (2025-04-15T23:47:35Z) - Without Paired Labeled Data: End-to-End Self-Supervised Learning for Drone-view Geo-Localization [2.733505168507872]
Drone-view Geo-Localization (DVGL) aims to achieve accurate localization of drones by retrieving the most relevant GPS-tagged satellite images.<n>Existing methods heavily rely on strictly pre-paired drone-satellite images for supervised learning.<n>We propose an end-to-end self-supervised learning method with a shallow backbone network.
arXiv Detail & Related papers (2025-02-17T02:53:08Z) - CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution Generalization [0.7366405857677226]
In this work, we introduce a simple yet powerful approach, CGLearn, which relies on the agreement of gradients across various environments.
Our proposed method demonstrates superior performance compared to state-of-the-art methods in both linear and nonlinear settings.
Comprehensive experiments on both synthetic and real-world datasets highlight its effectiveness in diverse scenarios.
arXiv Detail & Related papers (2024-11-09T02:36:39Z) - Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays [18.68871336059738]
Direction of arrival (DoA) estimation is a common sensing problem in radar, sonar, audio, and wireless communication systems.<n>This study introduces a joint DoA estimation and hardware impairment learning scheme following a model-based approach.
arXiv Detail & Related papers (2024-11-06T09:14:26Z) - Disentangling Masked Autoencoders for Unsupervised Domain Generalization [57.56744870106124]
Unsupervised domain generalization is fast gaining attention but is still far from well-studied.
Disentangled Masked Auto (DisMAE) aims to discover the disentangled representations that faithfully reveal intrinsic features.
DisMAE co-trains the asymmetric dual-branch architecture with semantic and lightweight variation encoders.
arXiv Detail & Related papers (2024-07-10T11:11:36Z) - Towards Robust and Interpretable EMG-based Hand Gesture Recognition using Deep Metric Meta Learning [37.21211404608413]
We propose a shift to deep metric-based meta-learning in EMG PR to supervise the creation of meaningful and interpretable representations.
We derive a robust class proximity-based confidence estimator that leads to a better rejection of incorrect decisions.
arXiv Detail & Related papers (2024-04-17T23:37:50Z) - Improve Knowledge Distillation via Label Revision and Data Selection [37.74822443555646]
This paper proposes to rectify the teacher's inaccurate predictions using the ground truth.
In the latter, we introduce a data selection technique to choose suitable training samples to be supervised by the teacher.
Experiment results demonstrate the effectiveness of our proposed method, and show that our method can be combined with other distillation approaches.
arXiv Detail & Related papers (2024-04-03T02:41:16Z) - Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward
Comprehensive Benchmarks [60.82579717007963]
We introduce an enhanced evaluation framework designed to more accurately gauge the effectiveness, consistency, and overall capability of Graph Contrastive Learning (GCL) methods.
arXiv Detail & Related papers (2024-02-24T01:47:56Z) - Selective Knowledge Sharing for Privacy-Preserving Federated
Distillation without A Good Teacher [52.2926020848095]
Federated learning is vulnerable to white-box attacks and struggles to adapt to heterogeneous clients.
This paper proposes a selective knowledge sharing mechanism for FD, termed Selective-FD.
arXiv Detail & Related papers (2023-04-04T12:04:19Z) - Improving Adaptive Conformal Prediction Using Self-Supervised Learning [72.2614468437919]
We train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores.
We empirically demonstrate the benefit of the additional information using both synthetic and real data on the efficiency (width), deficit, and excess of conformal prediction intervals.
arXiv Detail & Related papers (2023-02-23T18:57:14Z) - Differentiating Student Feedbacks for Knowledge Tracing [28.669001606806525]
We propose a framework to reweight the contribution of different responses based on their discrimination in training.<n>We also introduce an adaptive predictive score fusion technique to maintain accuracy on less discriminative responses.
arXiv Detail & Related papers (2022-12-16T13:55:07Z) - Distantly-Supervised Named Entity Recognition with Adaptive Teacher
Learning and Fine-grained Student Ensemble [56.705249154629264]
Self-training teacher-student frameworks are proposed to improve the robustness of NER models.
In this paper, we propose an adaptive teacher learning comprised of two teacher-student networks.
Fine-grained student ensemble updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise.
arXiv Detail & Related papers (2022-12-13T12:14:09Z) - Learning Domain Adaptive Object Detection with Probabilistic Teacher [93.76128726257946]
We present a simple yet effective framework, termed as Probabilistic Teacher (PT)
PT aims to capture the uncertainty of unlabeled target data from a gradually evolving teacher and guides the learning of a student in a mutually beneficial manner.
We also present a novel Entropy Focal Loss (EFL) to further facilitate the uncertainty-guided self-training.
arXiv Detail & Related papers (2022-06-13T16:24:22Z) - Agree to Disagree: Diversity through Disagreement for Better
Transferability [54.308327969778155]
We propose D-BAT (Diversity-By-disAgreement Training), which enforces agreement among the models on the training data.
We show how D-BAT naturally emerges from the notion of generalized discrepancy.
arXiv Detail & Related papers (2022-02-09T12:03:02Z) - Shuffle Augmentation of Features from Unlabeled Data for Unsupervised
Domain Adaptation [21.497019000131917]
Unsupervised Domain Adaptation (UDA) is a branch of transfer learning where labels for target samples are unavailable.
In this paper, we propose Shuffle Augmentation of Features (SAF) as a novel UDA framework.
SAF learns from the target samples, adaptively distills class-aware target features, and implicitly guides the classifier to find comprehensive class borders.
arXiv Detail & Related papers (2022-01-28T07:11:05Z) - Robust Pre-Training by Adversarial Contrastive Learning [120.33706897927391]
Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness.
We improve robustness-aware self-supervised pre-training by learning representations consistent under both data augmentations and adversarial perturbations.
arXiv Detail & Related papers (2020-10-26T04:44:43Z) - Grasping Detection Network with Uncertainty Estimation for
Confidence-Driven Semi-Supervised Domain Adaptation [17.16216430459064]
This paper presents an approach enabling the easy domain adaptation through a novel grasping detection network with confidence-driven semi-supervised learning.
The proposed grasping detection network specially provides a prediction uncertainty estimation mechanism by leveraging on Feature Pyramid Network (FPN), and the mean-teacher semi-supervised learning utilizes such uncertainty information to emphasizing the consistency loss only for those unlabelled data with high confidence.
Our results show that the proposed network can achieve high success rate on the Cornell grasping dataset, and for domain adaptation with very limited data, the confidence-driven mean teacher outperforms the original mean teacher and direct training by more than 10% in evaluation
arXiv Detail & Related papers (2020-08-20T07:42:45Z) - Foreseeing the Benefits of Incidental Supervision [83.08441990812636]
This paper studies whether we can, in a single framework, quantify the benefits of various types of incidental signals for a given target task without going through experiments.
We propose a unified PAC-Bayesian motivated informativeness measure, PABI, that characterizes the uncertainty reduction provided by incidental supervision signals.
arXiv Detail & Related papers (2020-06-09T20:59:42Z) - Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic
Segmentation [63.75774438196315]
Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain to a new target domain with only unlabeled data.
Most existing methods suffer from noticeable negative transfer resulting from either the error-prone discriminator network or the unreasonable teacher model.
We propose an uncertainty-aware consistency regularization method for cross-domain semantic segmentation.
arXiv Detail & Related papers (2020-04-19T15:30:26Z) - Learning Adaptive Loss for Robust Learning with Noisy Labels [59.06189240645958]
Robust loss is an important strategy for handling robust learning issue.
We propose a meta-learning method capable of robust hyper tuning.
Four kinds of SOTA loss functions are attempted to be minimization, general availability and effectiveness.
arXiv Detail & Related papers (2020-02-16T00:53:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.