Latest Active Learning Research Papers
The newest Active Learning papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Active Learning so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.
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- Kronecker Delta Rule for Long Context Linear AttentionAnamaria-Roberta Hartl, Levente Zólyomi, Michael List, Günter Klambauer et al. · arXiv · Oct 19, 2026
We propose KronLSTM, a linear attention architecture based on the Kronecker Delta Rule, a novel update rule for associative matrix memories. KronLSTM extends the xLSTM family with a memory-revision mechanism that corrects targeted associati…
- Decoupling Exploration from Optimization in RLVRSaif Punjwani, Micah Goldblum · arXiv · Oct 7, 2026
Modern language models undergo reinforcement learning with verifiable rewards (RLVR) on top of already-trained checkpoints. A key promise of RLVR is the discovery of new reasoning strategies. In principle, a model can sample novel ideas abs…
- IdeaLens: Detecting AI Ideas in Long-form WritingRishanth Rajendhran, Minjoon Choi, Jenna Russell, Ramya Namuduri et al. · arXiv · Oct 5, 2026
While modern AI detectors identify who wrote the words, emerging policies on AI use increasingly hinge on a different question: who came up with the ideas? We introduce IdeaLens, a detector that identifies whether a document's ideas came fr…
- hyperbolix: Hyperbolic Deep Learning in JAXTimo Klein, Thomas Lang, Yllka Velaj, Sebastian Tschiatschek · arXiv · Sep 23, 2026
We present hyperbolix, an open-source library for hyperbolic deep learning in JAX, built on Flax NNX. To our knowledge, it is the first comprehensive, general-purpose hyperbolic deep learning library in JAX. It includes six manifolds with a…
- Label-Efficient Learning for Ground-Based Sky-Image Classification: A Benchmark of Transfer Learning, Active Learning, and Pseudo-Labeling on GCDEsther Bou Dagher, Viktoriya Bu-Dager, Boguslaw Zegarlinski · arXiv · Sep 22, 2026
Accurate ground-based cloud classification is important for atmospheric monitoring, solar-energy forecasting, aviation weather assessment, and climate observation systems. However, reliable sky-image annotation is time-consuming, especially…
- Subspace Inference Enables Efficient Active Reward Learning from PreferencesYutai Zhou, Erdem Bıyık · arXiv · Sep 3, 2026
Reinforcement learning from human feedback (RLHF) has emerged as a powerful yet sample-inefficient approach for learning reward models from human preferences, making active learning a critical component in synthesizing informative preferenc…
- LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style UpdatesDmitrii Andriianov, Andrey Veprikov, Aleksandr Beznosikov · arXiv · Sep 2, 2026
Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight change it induces. We introduce LoRA-TSD, an optimizer that…
- Enhancing Bayesian Optimization and Active Learning Through Kernel DiversityHeng Zhang, Haotian Xiang, Qin Lu, Konstantinos D. Polyzos et al. · arXiv · Aug 25, 2026
Hyperparameter selection remains a key challenge in Bayesian optimization (BO) and Bayesian active learning (AL), as model misspecification can lead to suboptimal performance, while more accurate fully Bayesian treatments typically rely on …
- Loss-Based Active Learning for Neural Abstractive SummarizationMichail Ioannou, Tatiana Passali, Grigorios Tsoumakas · Greeks in AI 2026 Poster · Jun 2, 2026
Fine-tuning abstractive summarization models requires high-quality annotated data. However, obtaining such corpora is expensive and time-consuming, as it requires human annotators to read and comprehend long documents to create accurate sum…
- Active learning framework leveraging transcriptomics identifies modulators of disease phenotypes.Benjamin DeMeo, Charlotte Nesbitt, S. Miller, Daniel B. Burkhardt et al. · Science · Oct 23, 2025
Phenotypic drug screening remains constrained by the vastness of chemical space and technical challenges scaling experimental workflows. To overcome these barriers, computational methods have been developed to prioritize compounds, but they…
- Active learning accelerates electrolyte solvent screening for anode-free lithium metal batteriesP. Ma, Ritesh Kumar, Ke-Hsin Wang, Chibueze V. Amanchukwu · Nature Communications · Sep 25, 2025
Anode-free or ‘zero-excess’ lithium metal batteries offer high energy density compared to current lithium-ion batteries but require electrolyte innovation to extend cycle life. Due to the lack of universal design principles, electrolyte dev…
- Human-GenAI interaction for active learning in STEM education: State-of-the-art and future directionsSofie Otto, Rea Lavi, L. Bertel · Comput. Educ. · Aug 1, 2025
- IoT-Driven Skin Cancer Detection: Active Learning and Hyperparameter Optimization for Enhanced Accuracy.Jing Yang, Hao-Shen Qin, Jin-Li Wang, Por Lip Yee et al. · IEEE journal of biomedical and health informatics · Jun 10, 2025
Skin cancer, one of the most prevalent and lethal cancer types, poses significant challenges for early diagnosis due to the diversity in lesion size, shape, color, and surface reflections. The Internet of Things (IoT) has revolutionized hea…
- AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational settingGregory Kestin, Kelly Miller, Anna Klales, Timothy Milbourne et al. · Scientific Reports · Jun 3, 2025
Advances in generative artificial intelligence show great potential for improving education. Yet little is known about how this new technology should be used and how effective it can be compared to current best practices. Here we report a r…
- InvDesFlow-AL: active learning-based workflow for inverse design of functional materialsXiao-Qi Han, Peng-Jie Guo, Ze-Feng Gao, Hao Sun et al. · npj Computational Materials · May 14, 2025
Developing inverse design methods for functional materials with specific properties is critical to advancing fields like renewable energy, catalysis, energy storage, and carbon capture. Generative models based on diffusion principles can di…
- Designing embodied generative artificial intelligence in mixed reality for active learning in higher educationAndy Nguyen, Faaiz Gul, Belle Dang, Luna Huynh et al. · Innovations in Education & Teaching International · Apr 29, 2025
ABSTRACT Generative Artificial Intelligence (GenAI) technologies have introduced significant changes to higher education, but the role of Embodied GenAI Agents in Mixed Reality (MR) environments is still relatively unexplored. This study wa…
- Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalystsRui Ding, Jian-Guo Liu, Kang Hua, Xue-Bin Wang et al. · Science Advances · Apr 4, 2025
Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study presents a multistage machine learning (ML) approach to streamline the discovery and optimization of complex…
- Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLMs-Powered AssistanceBo Yuan, Yulin Chen, Yin Zhang, Wei Jiang · Annual Meeting of the Association for Computational Linguistics · Apr 3, 2025
Learning from noisy labels (LNL) is a challenge that arises in many real-world scenarios where collected training data can contain incorrect or corrupted labels. Most existing solutions identify noisy labels and adopt active learning to que…
- Promoting online teaching through active learning strategies: applications and innovationsA. Rakha · Frontiers in Education · Mar 28, 2025
Online teaching extends beyond the mere creation of educational materials and the broadcasting of lectures. It should be enjoyable, engaging, collaborative, and comfortable, which can be achieved through the implementation of active learnin…
- Digital Twin-Based Active Learning for Industrial Process Control and Supervision in Industry 4.0Jessica S. Ortiz, Evelin K. Quishpe, Grace X. Sailema, Nathaly S. Guamán · Italian National Conference on Sensors · Mar 26, 2025
The integration of Digital Twin technology into active learning environments has been established as an innovative strategy to optimize engineering education. This study focuses on the development and evaluation of a learning tool based on …
- Image steganalysis using active learning and hyperparameter optimizationBohang Li, Ningxin Li, Jing Yang, Osama Alfarraj et al. · Scientific Reports · Mar 1, 2025
Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To ad…
- From Selection to Generation: A Survey of LLM-based Active LearningYu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Jun-Da Wu et al. · Annual Meeting of the Association for Computational Linguistics · Feb 17, 2025
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent active learning frameworks, Large Language Models (LLMs) hav…
- Active learning framework to optimize process parameters for additive-manufactured Ti-6Al-4V with high strength and ductilityJeong Ah Lee, Jaejung Park, ManJae SaGong, S. Y. Ahn et al. · Nature Communications · Jan 22, 2025
Optimizing process and heat-treatment parameters of laser powder bed fusion for producing Ti-6Al-4V alloys with high strength and ductility is crucial to meet performance demands in various applications. Nevertheless, inherent trade-offs be…
- Active Learning Strategies: A Mini Review of Evidence-Based ApproachesMaria Eugenia Martinez, Valeria Gomez · Acta Pedagogia Asiana · Jan 17, 2025
Active learning strategies such as think-pair-share (TPS), problem-based learning (PBL), flipped classrooms, and collaborative projects are essential for promoting student engagement, critical thinking, and academic success. This review bri…
- Implementing active learning approach to promote motivation, reduce anxiety, and shape positive attitudes: A case study of EFL learners.Afsheen Rezai, R. Ahmadi, Parisa Ashkani, Gholam Hossein Hosseini · Acta Psychologica · Jan 16, 2025
Active Learning (AL) represents a transformative instructional approach that departs from traditional methods by immersing students in experiential learning activities such as problem-solving, discussions, role-plays, interactive engagement…
- YOLO-ALDS: an instance segmentation framework for tomato defect segmentation and grading based on active learning and improved YOLO11Sheng Xue, Zhen-Ye Li, Dong-Yi Wang, Ting-Ting Zhu et al. · Computers and Electronics in Agriculture · Jan 1, 2025
- DIRECT: Deep Active Learning under Imbalance and Label NoiseShyam Nuggehalli, Jifan Zhang, Lalit K Jain, Robert D Nowak · ICLR 2025 Conference Withdrawn Submission · Sep 27, 2024
Class imbalance is a prevalent issue in real world machine learning applications, often leading to poor performance in rare and minority classes. With an abundance of wild unlabeled data, active learning is perhaps the most effective techni…
- Universal Rates for Active LearningSteve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas · NeurIPS 2024 poster · Sep 25, 2024
In this work we study the problem of actively learning binary classifiers from a given concept class, i.e., learning by utilizing unlabeled data and submitting targeted queries about their labels to a domain expert. We evaluate the q…
- Learnability Matters: Active Learning for Video CaptioningYiqian Zhang, Buyu Liu, Jun Bao, Qiang Huang et al. · NeurIPS 2024 poster · Sep 25, 2024
This work focuses on the active learning in video captioning. In particular, we propose to address the learnability problem in active learning, which has been brought up by collective outliers in video captioning and neglected in the litera…
- Fair Active Learning in Low-Data RegimesRomain Camilleri, Andrew Wagenmaker, Kevin Jamieson, Lalit K Jain et al. · UAI 2024 poster · Apr 26, 2024
In critical machine learning applications, ensuring fairness is essential to avoid perpetuating social inequities. In this work, we address the challenges of reducing bias and improving accuracy in data-scarce environments, where the cost o…