Active Learning

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Overview

Active learning studies how to choose data or queries so models can learn effectively with fewer labels, measurements, or experiments. Common approaches include uncertainty sampling, diversity and coreset selection, Bayesian information gain, batch strategies, and cost-aware or human-in-the-loop querying; scientific applications also use Gaussian processes, multi-fidelity models, and physics-based acquisition to guide simulations and laboratory work. Work spans image and language benchmarks, medical annotation, robotics, and scientific discovery, with newer methods using foundation-model representations and adapting selection to class imbalance, domain shift, or label noise. A central open tension is that no acquisition strategy wins everywhere: gains depend on the task, budget, model and data, and random sampling can match or outperform sophisticated methods, making realistic evaluations and strong baselines essential.

Latest papers

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296 papers39 papers in the last four weeks, up 22% on the four weeks before. 0.2% of all new papers.

Feb 17, 2026

Beyond Labels: Information-Efficient Human-in-the-Loop Learning using Ranking and Selection Queries

Integrating human expertise into machine learning systems often reduces the role of experts to labeling oracles, a paradigm that limits the amount of information exchanged and fails to capture the nuances of human judgment. We address this challenge by developing a human-in-the-loop framework to learn binary classifiers with rich query types, consisting of item ranking and exemplar selection. We first introduce probabilistic human response models for these rich queries motivated by the relationship experimentally observed between the perceived implicit score of an item and its distance to the unknown classifier. Using these models, we then design active learning algorithms that leverage the rich queries to increase the information gained per interaction. We provide theoretical bounds on sample complexity and develop a tractable and computationally efficient variational approximation. Through experiments with simulated annotators derived from crowdsourced word-sentiment and image-aesthetic datasets, we demonstrate significant reductions on sample complexity. We further extend active learning strategies to select queries that maximize information rate, explicitly balancing informational value against annotation cost. This algorithm in the word sentiment classification task reduces learning time by more than 57% compared to traditional label-only active learning.

Belén Martín-Urcelay, Yoonsang Lee, Matthieu R. Bloch +1

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.

Human-in-the-Loop AnnotationActive Learning
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Feb 3, 2026

Distilling LLM Reasoning into Graph of Concept Predictors

Deploying Large Language Models (LLMs) for discriminative workloads is often limited by inference latency, compute, and API costs at scale. Active distillation reduces these costs by querying an LLM oracle to train small discriminative students, but most pipelines distill only final labels, discarding intermediate reasoning signals and offering limited diagnostics of what reasoning is missing and where errors arise. We propose Graph of Concept Predictors (GCP), a reasoning-aware active distillation framework in which the teacher's reasoning is elicited as a directed acyclic graph of intermediate concepts and mirrored in the student. GCP enhances sample efficiency through a graph-aware acquisition strategy that weights per-concept uncertainty, gradient diversity, and coverage by node centrality. Additionally, it improves training stability and efficiency by performing targeted sub-module retraining, which attributes downstream loss to specific concept predictors and updates only the most influential modules. Experiments on eight NLP classification benchmarks demonstrate that GCP enhances performance under limited annotation budgets while yielding more interpretable and controllable training dynamics. Code is available at https://github.com/Ziyang-Yu/GCP.

Ziyang Yu, Liang Zhao

Department of Computer Science Emory University Atlanta, USA

CoT DistillationActive LearningText ClassificationLanguage Model Distillation
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Feb 2, 2026

Correcting Boundary Bias and Observation Independence in Bayesian Experimental Design

In many experimental settings, active learning can improve sample efficiency by sequentially selecting where to measure, which is particularly valuable when experiments are expensive. Gaussian processes with variance-based acquisition criteria are widely used for this purpose, but have two limitations. First, they are observation-independent: their posterior variance depends only on where samples are acquired, not on what is measured, impairing their sensitivity to the structure of the acquired data. Second, they inflate the variance near boundaries, leading to excessive sampling at the edges of the space compared to the interior. These limitations undermine the gains in sampling efficiency expected from sequential acquisition. We address both limitations. We derive a reconstruction-driven design density and use the posterior mean to build a training-free warp that places more measurements where the target function varies rapidly. A geometric equalizer separately corrects boundary bias. Across sixteen synthetic and two real-data benchmarks, the geometric equalizer consistently improves function reconstruction by correcting boundary bias, while the reconstruction warp provides further gains by concentrating measurements where the posterior mean varies rapidly.

Sanna Jarl, Jens Sjölund, Jonathan J. S. Scragg +1

Department of Material Science & Engineering Uppsala University RISE · Department of Information Technology Uppsala University · RISE

Active LearningAdaptive SamplingBayesian Experimental DesignGaussian Process RegressionActive Experimental Design
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Feb 2, 2026

Towards Optimality in Active Preference Learning

Aligning large language models (LLMs) depends on high-quality datasets of human preference labels, which are costly to collect. Although active learning has been studied to improve sample efficiency relative to passive collection, many existing approaches adopt classical experimental design criteria such as G- or D-optimality. These objectives are not tailored to the structure of preference learning, leaving open the design of problem-specific algorithms. In this work, we identify a simple intuition specific to preference learning that calls into question the suitability of these existing design objectives. Motivated by this insight, we propose two active learning algorithms. The first provides the first instance-dependent label complexity guarantee for this setting, and the second is a simple, practical greedy method. We show the optimality of our instance-dependent term up to a constant depending on the logit bound, compared with a lower-bound characteristic. We evaluate our algorithms on real-world preference datasets and synthetic data observing improved sample efficiency compared to existing methods.

Yao Zhao, Kwang-Sung Jun

Department of Computer Science, University of Arizona, Tucson, USA.

Active Preference LearningActive LearningPreference LearningPairwise Preference LearningLLM Alignment
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Jan 8, 2026

Active Learning under Simulated Class Imbalance and Label Shift in Binary Semantic Segmentation

The aim of Active Learning is to select the most informative samples from an unlabelled set of data. This is useful in cases where the amount of data is large and labelling is expensive, such as in machine vision or medical imaging. Two particularities of machine vision are first, that most of the images produced are free of defects, and second, that the amount of images produced is so big that we cannot store all acquired images. This results, on the one hand, in a strong class imbalance in defect distribution and, on the other hand, in a potential label shift caused by limited storage. To understand how these two forms of imbalance affect active learning algorithms, we propose a simulation study based on two open-source datasets. Our contribution is not a new acquisition strategy but a controlled and reproducible protocol that synthetically constructs datasets in which the levels of class imbalance and label shift are fixed and vary independently. Four standard active learning selection strategies are compared: random sampling, entropy-based selection, core-set selection and an imbalance aware strategy called galaxy sampling. We empirically find that active learning, and in particular the entropy-based and core-set selections, remains competitive with and typically superior to random sampling even for highly imbalanced datasets, and we quantify the loss of efficiency that occurs under a strong label shift.

Julien Combes, Jean-François Coeurjolly, Alexandre Derville

SVH, Michelin · SVH · Michelin

Active LearningLabel ShiftClass-Imbalanced Learning
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Dec 30, 2025

Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference

This work develops an active learning framework to intelligently enrich data-driven reduced-order models (ROMs) of parametric dynamical systems, which can serve as the foundation of virtual assets in a digital twin. Data-driven ROMs are explainable, computationally efficient scientific machine learning models that aim to preserve the underlying physics of complex dynamical simulations. Since the quality of data-driven ROMs is sensitive to the quality of the limited training data, we seek to identify training parameters for which using the associated training data results in the best possible parametric ROM. Our approach uses the operator inference methodology, a regression-based strategy which can be tailored to particular parametric structure for a large class of problems. We establish a probabilistic version of parametric operator inference, casting the learning problem as a Bayesian linear regression. Prediction uncertainties stemming from the resulting probabilistic ROM solutions are used to design a sequential adaptive sampling scheme to select new training parameter vectors that promote ROM stability and accuracy globally in the parameter domain. We conduct numerical experiments for several nonlinear parametric systems of partial differential equations and compare the results to ROMs trained on random parameter samples. The results demonstrate that the proposed adaptive sampling strategy consistently yields more stable and accurate ROMs than random sampling does under the same computational budget.

Shane A. McQuarrie, Mengwu Guo, Anirban Chaudhuri

Department of Mathematics, Brigham Young University · Centre for Mathematical Sciences, Lund University · Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin

Active LearningPartial Differential EquationsReduced-Order Modeling
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Dec 18, 2025

Monte Carlo Query Search: Active Capability Assessment of AI Agents

Black-box AI (BBAI) systems, including foundation-model agents, are increasingly used for sequential decision making. Safe deployment requires methods for characterizing what such systems can do, when they can do it, and what outcomes may result. We introduce Monte Carlo Query Synthesis (MCQS), an active query-synthesis method for learning symbolic stochastic capability models of BBAIs. MCQS models capabilities as conditional probability distributions over outcomes and formulates capability learning as an active learning problem over policies. Our approach uses Monte Carlo tree search to synthesize queries that induce BBAI execution trajectories with high discriminative value between extremal hypothesis models: the lattice meet and join corresponding to the most pessimistic and optimistic hypotheses consistent with the observations. Executing these queries with the agent yields information-rich state-action trajectories that speed up learning by pruning inconsistent hypotheses. We prove soundness, completeness, and convergence properties under standard realizability and sampling assumptions. Experiments with multiple BBAI systems show that MCQS learns accurate capability models more efficiently than baseline query strategies.

Daniel Bramblett, Rushang Karia, Adrian Ciotinga +3

Arizona State University, AZ, USA · Indian Institute of Technology Madras, India

AI Agent EvaluationActive Learning
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Dec 14, 2025

Active Learning with Imperfect Labels: Optimal Labeler Assignment and Sample Selection

Active Learning (AL) is commonly used in applications where labeling data is expensive or time-consuming. In practice, however, labels are often noisy due to varying labeler expertise and annotation uncertainty, especially for complex or ambiguous samples. Learning from such imperfectly labeled data can degrade classifier performance. We propose an AL framework that explicitly accounts for label noise by optimally assigning labelers and selecting samples to minimize labeling error. Our approach, called OLAS (Optimal Labeler Assignment and Sampling), uses a noise model that depends on both labeler accuracy and model uncertainty to guide these decisions. We develop two tractable optimization formulations: one for assigning samples to labelers to minimize worst-case noise, and another for selecting samples while controlling overall label noise. Theoretical results provide closed-form solutions under mild conditions. Empirical evaluations on benchmark datasets and a real-world warranty claim classification problem show that OLAS achieves the highest or near-highest classification accuracy among existing AL strategies across most settings, using only a single label per sample.

Pouya Ahadi, Blair Winograd, Camille Zaug +3

School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332, USA · Ford Motor Company

Human-in-the-Loop AnnotationActive LearningLearning with Noisy Labels
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Oct 10, 2025

Batch Bayesian Active Learning with Partial Batch Label Sampling

Over the past couple of decades, many active learning acquisition functions have been proposed, leaving practitioners with an unclear choice of which to use. Bayesian active learning offers principled objectives with explainable intuition, including Expected Error Reduction (EER), Expected Predictive Information Gain (EPIG), and Bayesian Active Learning by Disagreements (BALD). A key challenge of such methods is the difficulty of extending to the batch setting, leading to either computational challenges (BatchBALD) or dramatic performance drops (top-BB selection). In this work, we extend active learning's Bayesian Decision Theory framework to derive Partial Batch Label Sampling (ParBaLS) for the EPIG algorithm. We show experimentally that ParBaLS EPIG gives superior performance for a variety of datasets with different budgets using Bayesian logistic regression on embeddings from large pre-trained models. Our code is available at https://github.com/ADDAPT-ML/ParBaLS.

Kangping Hu, Stephen Mussmann

Department of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332

Active Learning
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Oct 10, 2025

Large Language Model Selection with Limited Annotations

Choosing a Large Language Model (LLM) for a given task requires comparing many strong candidates, yet standard evaluation relies on costly annotations over fixed evaluation sets. To address this challenge, we develop SELECT-LLM, the first framework for active model selection of LLMs. SELECT-LLM aims to find a small set of queries whose annotations are most informative for identifying the best LLM for a given task. To this end, we introduce a query selection rule based on expected information gain, computed from pairwise similarities between candidate model outputs. Because this rule only uses generated model responses, SELECT-LLM can be applied across candidate models without assumptions about their architecture or access to model weights. This makes it suitable for both open-weight and black-box LLMs. We evaluate SELECT-LLM across 23 datasets, 156 evaluated models, diverse task families, and multiple text evaluation metrics. Across all experiments, SELECT-LLM improves over the strongest baseline in every setting, with annotation cost reductions up to 81.8% for best model selection and up to 84.78% for near-best model selection.

Yavuz Durmazkeser, Patrik Okanovic, Andreas Kirsch +2

TU Delft · ETH Zurich

Model SelectionLLM EvaluationActive LearningInformation Gain
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Oct 1, 2025

Multidimensional Bayesian Active Machine Learning of Working Memory Task Performance

While adaptive experimental design has outgrown one-dimensional, staircase-based adaptations, most cognitive experiments still control a single factor and summarize performance with a scalar. In contrast, we validate here a Bayesian, two-axis, active-classification approach in an immersive virtual testing environment, using a 5-by-5 spatial working-memory reconstruction task as an application domain. Two variables are controlled: spatial load L (number of occupied tiles) and feature-binding load K (number of distinct colors) of items. Stimulus acquisition is guided by posterior uncertainty of a nonparametric Gaussian Process (GP) probabilistic classifier, which outputs a surface over (L, K) rather than a single threshold or maximum span value. In a young adult population, we compare GP-driven Adaptive Mode (AM) with a traditional adaptive staircase Classic Mode (CM), which varies L only at K = 3. Parity between the methods is achieved for this cohort, with an intraclass correlation coefficient of 0.755 at K = 3. As a secondary exploratory result, AM reveals individual differences in interactions between spatial load and feature binding. AM estimates converge more quickly than other sampling strategies, demonstrating that only about 30 samples are required for accurate fitting of the full model.

Dom CP Marticorena, Chris Wissmann, Zeyu Lu +1

Department of Biomedical Engineering, Washington University, 1 Brookings Drive, St. Louis, MO 63130 · Department of Computer Science and Engineering, Washington University, 1 Brookings Drive, St. Louis, MO 63130

Active Experimental DesignActive LearningBayesian Experimental Design
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Sep 30, 2025

CPATTA: Conformal Supervision Allocation For Active Test-Time Adaptation

Active Test-Time Adaptation (ATTA) improves model robustness under domain shift by selectively querying human annotations at deployment, but existing methods use heuristic uncertainty measures and suffer from low data selection efficiency, wasting human annotation budget. We propose Conformal Prediction Active TTA (CPATTA), which first brings principled, conformal uncertainty with coverage-aware online calibration into ATTA. CPATTA employs smoothed conformal scores with a top-KK certainty measure, an online weight-update algorithm driven by pseudo coverage, a domain-shift detector that adapts human supervision, and a staged update scheme that balances human-labeled and model-labeled data. Extensive experiments demonstrate that CPATTA consistently outperforms the state-of-the-art ATTA methods by around 5% in accuracy.

Tingyu Shi, Fan Lyu, Haihua Zhu +2

Computer Science and Engineering, University of California San Diego · New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences · College of Computer Science and Electronic Engineering, Hunan University

Distribution Shift RobustnessTest-Time AdaptationActive LearningConformal Prediction
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Sep 29, 2025

Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification

Active learning (AL) aims to build high-quality labeled datasets by iteratively selecting the most informative samples from an unlabeled pool under limited annotation budgets. However, in fine-grained image classification, assessing this informativeness reliably is especially challenging due to subtle differences between classes. In this paper, we introduce a novel active learning method, combining discrepancy-confusion uncertainty and calibration diversity for active fine-grained image classification (DECERN), to effectively perceive the distinctiveness between fine-grained images and evaluate the sample value. DECERN introduces a multifaceted informativeness measure that combines discrepancy-confusion uncertainty and calibration diversity. The discrepancy-confusion uncertainty quantifies the structural stability and category directionality of fine-grained unlabeled data during local feature fusion. Subsequently, uncertainty-weighted clustering is performed to diversify the uncertainty samples. Then we calibrate the diversity to maximize the global diversity of the selected sample while maintaining its local representativeness. Extensive experiments conducted on 7 fine-grained image datasets across 39 distinct experimental settings demonstrate that our method achieves superior performance compared to state-of-the-art methods.

Yinghao Jin, Xi Yang

Jilin University, China

Active LearningFine-Grained Image ClassificationImage Classification
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Sep 25, 2025

Actively Learning Halfspaces without Synthetic Data

In the classic point location problem, one is given an arbitrary dataset X⊂RdX \subset \mathbb{R}^d of nn points with query access to an unknown halfspace f:Rd→{0,1}f : \mathbb{R}^d \to \{0,1\}, and the goal is to learn the label of every point in XX. This problem is extremely well-studied and a nearly-optimal O~(dlog⁡n)\widetilde{O}(d \log n) query algorithm is known due to Hopkins-Kane-Lovett-Mahajan (FOCS 2020). However, their algorithm is granted the power to query arbitrary points outside of XX (point synthesis), and in fact without this power there is an Ω(n)Ω(n) query lower bound due to Dasgupta (NeurIPS 2004). In this work our goal is to design efficient algorithms for learning halfspaces without point synthesis. To circumvent the Ω(n)Ω(n) lower bound, we consider learning halfspaces whose normal vectors come from a set of size DD, and show tight bounds of Θ(D+log⁡n)Θ(D + \log n). As a corollary, we obtain an optimal O(d+log⁡n)O(d + \log n) query deterministic learner for axis-aligned halfspaces, closing a previous gap of O(dlog⁡n)O(d \log n) vs. Ω(d+log⁡n)Ω(d + \log n). In fact, our algorithm solves the more general problem of learning a Boolean function ff over nn elements which is monotone under at least one of DD provided orderings. Our technical insight is to exploit the structure in these orderings to perform a binary search in parallel rather than considering each ordering sequentially, and we believe our approach may be of broader interest. Furthermore, we use our exact learning algorithm to obtain nearly optimal algorithms for PAC-learning. We show that O(min⁡(D+log⁡(1/ε),1/ε)⋅log⁡D)O(\min(D + \log(1/\varepsilon), 1/\varepsilon) \cdot \log D) queries suffice to learn ff within error ε\varepsilon, even in a setting when ff can be adversarially corrupted on a cεc\varepsilon-fraction of points, for a sufficiently small constant cc. This bound is optimal up to a log⁡D\log D factor, including in the realizable setting.

Hadley Black, Kasper Green Larsen, Arya Mazumdar +2

Baruch College, CUNY · Aarhus University · University of California, San Diego

Query ComplexityBoolean Function LearningActive LearningHalfspace LearningPAC Learning
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Aug 31, 2025

Ultra Strong Machine Learning: LLM-Generated Explanations Do Not Yet Suffice for Teaching Humans Active Learning Strategy

Active learning is a general learning mechanism shared by artificial and human learners. Whether AI can teach humans such a strategy that transfers across domains is an open question. Ultra Strong Machine Learning (USML), a system whose explanations quantifiably improve human out-of-sample performance compared to self-learning, is uniquely positioned to answer this question. Prior USML work relied on hand-crafted explanation templates that require expert effort for each new domain and do not scale. We developed an explanation pipeline combining Inductive Logic Programming (ILP) with large language models (LLMs) to automate explanation generation and scoring. We tested whether these explanations achieve USML in a human trial teaching active learning strategies across three related domains. Our exploratory results show that concise, expert-written explanations benefit learners with higher initial performance, while pipeline-generated explanations provide no advantage over self-learning despite being rated as higher quality from an LLM-as-judge evaluation. This case study reveals a systematic gap that LLM quality metrics do not predict human learning outcomes. Our findings point to explanation complexity relative to task difficulty as a key factor, and call for explanation methods and evaluation criteria grounded in human cognitive constraints rather than LLM preference.

Lun Ai, Johannes Langer, Ute Schmid +1

Department of Computing, Imperial College London, UK · Faculty Information Systems and Applied Computer Science, University of Bamberg, Germany

Active LearningHuman-in-the-Loop AIInductive Logic ProgrammingExplainability Evaluation
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Aug 31, 2025

Why Pool When You Can Flow? Active Learning with GFlowNets

The scalability of pool-based active learning is limited by the computational cost of evaluating large unlabeled datasets, a challenge that is particularly acute in virtual screening for drug discovery. While active learning strategies such as Bayesian Active Learning by Disagreement (BALD) prioritize informative samples, it remains computationally intensive when scaled to libraries containing billions samples. In this work, we introduce BALD-GFlowNet, a generative active learning framework that circumvents this issue. Our method leverages Generative Flow Networks (GFlowNets) to directly sample objects in proportion to the BALD reward. By replacing traditional pool-based acquisition with generative sampling, BALD-GFlowNet achieves scalability that is independent of the size of the unlabeled pool. In our virtual screening experiment, we show that BALD-GFlowNet achieves a performance comparable to that of standard BALD baseline while generating more structurally diverse molecules, offering a promising direction for efficient and scalable molecular discovery.

Renfei Zhang, Mohit Pandey, Artem Cherkasov +1

School of Computer Science, Simon Fraser University, Burnaby, BC, Canada · Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada · Diagen AI +1

Active LearningVirtual ScreeningGenerative Flow NetworksMolecular Generation
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Jun 27, 2025

Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds

High-stakes decision-making involves navigating multiple competing objectives with expensive evaluations. For instance, in brachytherapy, clinicians must balance maximizing tumor coverage (e.g., an aspirational target or soft bound of >95% coverage) against strict organ dose limits (e.g., a non-negotiable hard bound of <601cGy to the bladder). Selecting Pareto-optimal solutions that match implicit preferences is challenging, as exhaustive Pareto frontier exploration is computationally and cognitively prohibitive, necessitating interactive frameworks to guide users. While decision-makers (DMs) often possess domain knowledge to narrow the search via such soft-hard bounds, current methods often lack systematic approaches to iteratively refine these multi-faceted preference structures. Furthermore, DMs often require confidence that they have not overlooked superior alternatives, a paramount necessity in high-stakes scenarios. We present Active-MoSH, an interactive local-global framework designed for this process. Its local component integrates probabilistic preference learning with an active sampling strategy to adaptively refine Pareto subsets while minimizing cognitive burden. To bolster decision confidence, Active-MoSH's global component, C-MoSH, leverages multi-objective sensitivity analysis to identify potentially overlooked, high-value points beyond immediate feedback. We demonstrate Active-MoSH's performance benefits through diverse synthetic and real-world applications. A high-stakes case study with real cervical cancer brachytherapy treatment plans and an image selection user study further validate our hypotheses regarding the framework's ability to improve convergence, enhance DM confidence, and provide expressive preference articulation.

Edward Chen, Sang T. Truong, Natalie Dullerud +2

Department of Computer Science, Stanford University, Stanford, California, USA

Active LearningMulti-Criteria Decision AnalysisMulti-Objective OptimizationPreference Learning
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Jun 1, 2025

PCoreSet: Effective Active Learning through Knowledge Distillation from Vision-Language Models

Knowledge distillation (KD) is a widely used framework for training compact, task-specific models by transferring the knowledge from teacher models. However, its application to active learning (AL), which aims to minimize annotation costs through iterative sample selection, remains underexplored. This gap stems from the fact that KD typically assumes access to sufficient labeled data, whereas AL operates in data-scarce scenarios where task-specific teacher models are often unavailable. In this paper, we first introduce ActiveKD, a framework that integrates AL with KD by leveraging the zero- and few-shot capabilities of large vision-language models (VLMs). A key aspect of ActiveKD is the structured prediction bias of VLMs--i.e., their predictions form clusters in the probability space. We regard this structure as an inductive bias of the teacher model, capturing generalizable output patterns beneficial to student learning. To exploit this bias, we propose Probabilistic CoreSet (PCoreSet), a selection strategy that maximizes coverage in the probability space rather than the feature space. PCoreSet strategically selects probabilistically diverse unlabeled samples, facilitating more efficient transfer of teacher knowledge under limited annotation budgets. Extensive evaluations on 11 datasets show that ActiveKD consistently improves every selection method over the no-distillation baseline (e.g., +29.07% on ImageNet, averaged over methods). Under ActiveKD, several widely-used AL selectors underperform Random and none consistently beats it; in contrast, PCoreSet is the only selector that consistently outperforms Random across 5 students and 3 teachers, always achieving the best performance except for the first 2 AL rounds. Our code is available at https://github.com/erjui/PCoreSet.

Seongjae Kang, Dong Bok Lee, Hyungjoon Jang +2

VUNO Inc. · KAIST

Active LearningCoreset SelectionKnowledge DistillationVLM Distillation
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May 19, 2025

Investigating Active Sampling for Hardness Classification with Vision-Based Tactile Sensors

Hardness is one of the most important object properties that humans and robots perceive through touch. This paper investigates active sampling strategies for sample-efficient hardness classification with vision-based tactile sensors (VBTS), using information-theoretic measures of model uncertainty. We evaluated three probabilistic classifier models and two active sampling strategies and a random sampling baseline on a hardness classification task, with our own dataset of VBTS samples collected by a robot and a previously published dataset. In addition, we conducted a human study on the hardness classification task with the same objects as our dataset. Our findings indicate that the active sampling approaches, driven by variance and entropy as measures of model uncertainty, perform best with accuracy reaching 90%, compared to human accuracy of 48%, demonstrating the effectiveness of using VBTS for hardness classification. More information can be found at: https://fheller1.github.io/ActiveHardness

Junyi Chen, Frederik Heller, Mario Gómez Andreu +7

Intelligent Autonomous Systems Lab, Department of Computer Science, TU Darmstadt, Germany. · German Research Center for AI (DFKI) · Department of Psychology, University of Giessen, Germany +2

Active LearningRobotic Tactile SensingTactile SensingVision-Based Tactile Sensing
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Mar 7, 2025

Near-Polynomially Competitive Active Logistic Regression

We address the problem of active logistic regression in the realizable setting. It is well known that active learning can require exponentially fewer label queries compared to passive learning, in some cases using log⁡1\eps\log \frac{1}{\eps} rather than \poly(1/\eps)\poly(1/\eps) labels to get error \eps\eps larger than the optimum. We present the first algorithm that is polynomially competitive with the optimal algorithm on every input instance, up to a factor d2d^2 and factors polylogarithmic in the error and domain size. In particular, on a simple family of logistic threshold instances with a uniform distribution, our algorithm uses \polylog(1/\eps)\polylog(1/\eps) labels, whereas every passive learner requires Ω(1/\eps2)Ω(1/\eps^2) labels. Our algorithm is based on sampling and can be extended to learn more general class of functions. We further support our theoretical results with experiments demonstrating performance gains for logistic regression compared to existing active learning algorithms.

Yihan Zhou, Eric Price, Trung Nguyen

The University of Texas at Austin

Active LearningLogistic RegressionStatistical Learning Theory
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Feb 1, 2025

Active Learning with Bayesian Multi-Fidelity Laplace Neural Operators for Oscillatory Parametric PDEs

Surrogate models of parametric dynamical systems are essential for many-query and real-time predictions in engineering applications such as design optimization and digital twins. However, generating high-fidelity (HF) training data over a broad range of parameters and operating conditions remains computationally expensive. To address this challenge, we propose a Bayesian multi-fidelity Laplace neural operator (MF-LNO) for uncertainty-aware active learning of oscillatory parametric PDEs. Specifically, the proposed Bayesian MF-LNO iteratively calibrates the discrepancy between low- and high-fidelity data, where predictive uncertainty guides the adaptive acquisition of informative HF trajectories. Such predictive uncertainty is quantified via replica-exchange stochastic gradient Langevin dynamics (reSGLD), whose broad posterior exploration enables uncertainty to serve as an error indicator for adaptive HF sample acquisition. Numerical experiments on the Lorenz system, Duffing oscillator, and beam dynamics demonstrate that uncertainty-guided HF sample acquisition consistently outperforms random sampling, while the proposed Bayesian MF-LNO achieves higher prediction accuracy than MF-DeepONet with predictive uncertainty quantification. These results demonstrate that Bayesian multi-fidelity LNOs, combined with uncertainty-guided active learning, provide a data-efficient framework for operator learning in engineering dynamical systems.

Bongseok Kim, Haoyang Zheng, Michael Penwarden +1

School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, 47907, IN, USA. · Department of Mathematics, Purdue University, 150 North University Street, West Lafayette, 47907, IN, USA.

Dynamical SystemsSurrogate ModelingUncertainty QuantificationActive LearningPDE Operator LearningNeural Operators
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Jan 3, 2025

Active Learning Enables Generation of Molecules that Advance the Known Pareto Front

Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up to 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model.

Evan R. Antoniuk, Peggy Li, Nathan Keilbart +3

Molecular OptimizationOOD GeneralizationActive LearningMolecular GenerationMolecular Property Prediction
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Dec 20, 2024

SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data

Improving the reliability and completeness of colonoscopic inspection is critical for reducing missed lesions and improving colorectal cancer prevention. Reliable scene understanding is essential for navigation, reconstruction, and assessment of inspection completeness. Anatomical structures such as mucosal folds provide stable geometric cues for endoscope localization, while surgical instruments introduce dynamic occlusions that complicate visual interpretation. However, existing gastrointestinal endoscopy datasets largely focus on disease detection or artifact segmentation, leaving a gap in precise annotations of structural landmarks and instruments. We introduce SegCol, a dataset and benchmark for semantic segmentation of colon fold edges and surgical instruments derived from the EndoMapper dataset. SegCol provides manually annotated pixel-level masks for three instrument classes and thin fold-edge structures across temporally consistent image sequences. It forms the basis of the SegCol Challenge, organized as part of the EndoVis Challenge at MICCAI 2024, evaluating both supervised segmentation and annotation-efficient active learning. We further study segmentation metrics, including Dice, ODS/OIS, AP, and CLDice, under structural perturbations and different object geometries, and analyze participating methods, architectural choices, and active learning strategies. Our findings show that metric behavior strongly depends on target structure, highlighting the need for carefully selected evaluation protocols in endoscopic segmentation. Details are available at https://www.synapse.org/Synapse:syn54124209/wiki/626563, and code at https://github.com/surgical-vision/segcol_challenge.

Xinwei Ju, Rema Daher, Razvan Caramalau +7

UCL Hawkes Institute, University College London, London, UK · Department of Computer Science, University College London, London, UK · Medtronic, London, UK +3

EndoscopyImage SegmentationActive LearningMedical Image SegmentationSemantic SegmentationMedical Image Benchmarks
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Oct 9, 2024

A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences

In rapidly evolving e-commerce industry, the capability of selecting high-quality data for model training is essential. This study introduces the High-Utility Sequential Pattern Mining using SHAP values (HUSPM-SHAP) model, a utility mining-based active learning strategy to tackle this challenge. We found that the parameter settings for positive and negative SHAP values impact the model's mining outcomes, introducing a key consideration into the active learning framework. Through extensive experiments aimed at predicting behaviors that do lead to purchases or not, the designed HUSPM-SHAP model demonstrates its superiority across diverse scenarios. The model's ability to mitigate labeling needs while maintaining high predictive performance is highlighted. Our findings demonstrate the model's capability to refine e-commerce data processing, steering towards more streamlined, cost-effective prediction modeling.

Danny Y. C. Wang, Lars Arne Jordanger, Jerry Chun-Wei Lin

Department of C. E. M., Høgskulen på Vestlandet, Bergen, Norway

Active LearningConversion Rate PredictionE-Commerce
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Aug 22, 2024

Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities

Machine Learning (ML) has become central to Autonomous Vehicles (AVs), supporting perception, prediction, planning, control, and decision-making in dynamic environments. However, achieving full autonomy in cluttered and complex scenarios, such as intricate intersections, diverse scenes, varied trajectories, and complex missions, remains challenging; moreover, data labeling is still a major bottleneck. These limitations motivate Human-in-the-Loop Machine Learning (HITL-ML), in which human input is incorporated through validation, annotation, task organization, reward design, action correction, preference feedback, and supervisory intervention. To advance safe and ethical autonomy, this paper presents a tutorial survey of HITL-ML for AVs, focusing on Curriculum Learning (CL), Human-in-the-Loop Reinforcement Learning (HITL-RL), Human-in-the-Loop Large Language Models (HITL-LLMs), Active Learning (AL), and ethical principles. We first review CL methods that structure training from simple to complex tasks, covering navigation, path planning, obstacle avoidance, data collection, landing, intersection handling, motion planning, and UAV swarm coordination. We then examine HITL-RL through reward shaping, action injection, demonstrations, preference-based feedback, and interactive learning, emphasizing improved learning efficiency, safer policy exploration, and real-time intervention. Next, we review HITL-LLM through collaboration and oversight and specify key challenges. After that, we discuss AL for perception, anomaly detection, semantic mapping, object detection, vehicle recognition, and security-related classification. Ethical principles are reviewed as technical requirements for transparency, accountability, human oversight, safety, security, regulatory compliance, and reliability of human input.

Yousef Emami, Mohammadhossein Homaei, Miguel Gutiérrez Gaitán +4

Active LearningAutonomous DrivingHuman-in-the-Loop AI
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Oct 28, 2023

A Competitive Algorithm for Agnostic Active Learning

For some hypothesis classes and input distributions, active agnostic learning needs exponentially fewer samples than passive learning; for other classes and distributions, it offers little to no improvement. The most popular algorithms for agnostic active learning express their performance in terms of a parameter called the disagreement coefficient, but it is known that these algorithms are inefficient on some inputs. We take a different approach to agnostic active learning, getting an algorithm that is competitive with the optimal algorithm for any binary hypothesis class HH and distribution DXD_X over XX. In particular, if any algorithm can use m∗m^* queries to get O(η)O(η) error, then our algorithm uses O(m∗log⁡∣H∣)O(m^* \log |H|) queries to get O(η)O(η) error. Our algorithm lies in the vein of the splitting-based approach of Dasgupta [2004], which gets a similar result for the realizable (η=0η= 0) setting. We also show that it is NP-hard to do better than our algorithm's O(log⁡∣H∣)O(\log |H|) overhead in general.

Eric Price, Yihan Zhou

Department of Computer Science, University of Texas at Austin

Active LearningAgnostic Learning
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