Active Learning

ICLR 2025arXiv:2410.13788

#2

Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questions

Michael Zhang, W. Bradley Knox, Eunsol Choi

clarifying question generationambiguous user requestspreference labeling methodsfuture conversation modeling+4

32

AAAI 2024arXiv:2312.07169

#3

VideoWorld: Exploring Knowledge Learning from Unlabeled Videos

Zhongwei Ren, Yunchao Wei, Xun Guo et al.

MLLM-as-a-Judge for Image Safety without Human Labeling

Zhenting Wang, Shuming Hu, Shiyu Zhao et al.

Semi-supervised Active Learning for Video Action Detection

Ayush Singh, Aayush J Rana, Akash Kumar et al.

semi-supervised active learningvideo action detectionspatio-temporal localizationinformative sample selection+3

16

CVPR 2024arXiv:2405.05714

#6

Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning

Rui Zhao, Bin Shi, Jianfei Ruan et al.

14

ICML 2025arXiv:2410.05952

#7

Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning

Dong-Dong Wu, Deng-Bao Wang, Min-Ling Zhang

Double-Layer Hybrid-Label Identification Feature Selection for Multi-View Multi-Label Learning

Pingting Hao, Kunpeng Liu, Wanfu Gao

Active Evaluation Acquisition for Efficient LLM Benchmarking

Yang Li, Jie Ma, Miguel Ballesteros et al.

12

CVPR 2024arXiv:2405.19902

#10

Dataset Quantization with Active Learning based Adaptive Sampling

Zhenghao Zhao, Yuzhang Shang, Junyi Wu et al.

Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection

Suyeon Kim, Dongha Lee, SeongKu Kang et al.

8

ICLR 2024arXiv:2401.09787

#12

Querying Easily Flip-flopped Samples for Deep Active Learning

Seong Jin Cho, Gwangsu Kim, Junghyun Lee et al.

8

ICML 2025arXiv:2411.17284

#13

AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling

Alexander Capstick, Rahul G. Krishnan, Payam Barnaghi

7

CVPR 2025arXiv:2503.02491

#14

Joint Out-of-Distribution Filtering and Data Discovery Active Learning

Sebastian Schmidt, Leonard Schenk, Leo Schwinn et al.

6

temporal action segmentationactive learningcontrastive learningaction prototypes+3

#15

Two-Stage Active Learning for Efficient Temporal Action Segmentation

Yuhao Su, Ehsan Elhamifar

ECCV 2024

6

AAAI 2025arXiv:2503.17017

#16

Noisy Label Calibration for Multi-View Classification

Shilin Xu, Yuan Sun, Xingfeng Li et al.

Partial Label Learning with a Partner

Chongjie Si, Zekun Jiang, Xuehui Wang et al.

Specifying What You Know or Not for Multi-Label Class-Incremental Learning

Aoting Zhang, Dongbao Yang, Chang Liu et al.

5

AAAI 2025arXiv:2501.15201

#19

Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization

Yan Yan, Yuhong Guo

A Training-free Synthetic Data Selection Method for Semantic Segmentation

Hao Tang, Siyue Yu, Jian Pang et al.

AAAI 2025arXiv:2408.12161

#21

Rebalancing Multi-Label Class-Incremental Learning

Kaile Du, Yifan Zhou, Fan Lyu et al.

AAAI 2025arXiv:2409.12218

#22

ARTICLE: Annotator Reliability Through In-Context Learning

Sujan Dutta, Deepak Pandita, Tharindu Cyril Weerasooriya et al.

ECCV 2024arXiv:2305.19486

#23

Enhancing Multi-Label Classification via Dynamic Label-Order Learning

Jiangnan Li, Yice ZHANG, Shiwei Chen et al.

Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation

Arpit Garg, Cuong Cao Nguyen, RAFAEL FELIX et al.

noisy-label learninginstance-dependent noisenoise-rate estimationsample selection+3

CVPR 2025arXiv:2503.18406

#25

Instruct-CLIP: Improving Instruction-Guided Image Editing with Automated Data Refinement Using Contrastive Learning

Sherry X. Chen, Misha Sra, Pradeep Sen

ICCV 2025arXiv:2412.04769

#26

Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection

Lei Fan, Junjie Huang, Donglin Di et al.

ECCV 2024arXiv:2407.07673

#27

Towards Adaptive Pseudo-label Learning for Semi-Supervised Temporal Action Localization

Feixiang Zhou, Bryan M. Williams, Hossein Rahmani

ICCV 2025arXiv:2408.08201

#28

Heavy Labels Out! Dataset Distillation with Label Space Lightening

Ruonan Yu, Songhua Liu, Zigeng Chen et al.

ICCV 2025arXiv:2507.04051

#29

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery

Xiao Liu, Nan Pu, Haiyang Zheng et al.

on-the-fly category discoveryfine-grained recognitiondiffusion modelsattribute-composition generation+4

AAAI 2025arXiv:2502.04394

#30

Distribution-Aware Robust Learning from Long-Tailed Data with Noisy Labels

Jae Soon Baik, In Young Yoon, Kun Hoon Kim et al.

DECT: Harnessing LLM-assisted Fine-Grained Linguistic Knowledge and Label-Switched and Label-Preserved Data Generation for Diagnosis of Alzheimer’s Disease

Tingyu Mo, Jacqueline C. K. Lam, Victor O. K. Li et al.

AAAI 2025arXiv:2503.15908

#32

Implicit Relative Labeling-Importance Aware Multi-Label Metric Learning

Jun-Xiang Mao, Yong Rui, Min-Ling Zhang

Enhancing Close-up Novel View Synthesis via Pseudo-labeling

Jiatong Xia, Libo Sun, Lingqiao Liu

ICML 2025arXiv:2505.19680

#34

Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning

Xinrui Wang, Shao-Yuan Li, Jiaqiang Zhang et al.

ICLR 2025arXiv:2410.02268

#35

Structural-Entropy-Based Sample Selection for Efficient and Effective Learning

Tianchi Xie, Jiangning Zhu, Guozu Ma et al.

2

ICCV 2025arXiv:2411.13317

#36

MTaDCS: Moving Trace and Feature Density-based Confidence Sample Selection under Label Noise

Qingzheng Huang, Xilin He, Xiaole Xian et al.

Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning

Zihua Zhao, Feng Hong, Mengxi Chen et al.

Distinguish Then Exploit: Source-free Open Set Domain Adaptation via Weight Barcode Estimation and Sparse Label Assignment

Weiming Liu, Jun Dan, Fan Wang et al.

Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning

Lei-Lei Ma, Shuo Xu, Ming-Kun Xie et al.

Teaching VLMs to Localize Specific Objects from In-context Examples

Sivan Doveh, Nimrod Shabtay, Eli Schwartz et al.

2

ECCV 2024arXiv:2311.02879

#41

Meta-Learning Dynamic Center Distance: Hard Sample Mining for Learning with Noisy Labels

Chenyu Mu, Yijun Qu, Jiexi Yan et al.

Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling

Wonho Bae, Jing Wang, Danica J. Sutherland

active learningmeta-learningcontext set labelinggaussian mixture models+1

AAAI 2025arXiv:2412.11620

#43

Combating Semantic Contamination in Learning with Label Noise

Wenxiao Fan, Kan Li

AAAI 2025arXiv:2412.18231

#44

Towards Macro-AUC Oriented Imbalanced Multi-Label Continual Learning

Yan Zhang, Guoqiang Wu, Bingzheng Wang et al.

AAAI 2024arXiv:2301.02761

#45

Active Learning Guided by Efficient Surrogate Learners

Yunpyo An, Suyeong Park, Kwang In Kim

active learningbatch-based samplinggaussian process surrogatesurrogate learners+3

CVPR 2024arXiv:2403.14430

#46

Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels

Tianming Liang, Chaolei Tan, Beihao Xia et al.

ICML 2025arXiv:2506.00816

#47

L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning

Xiang Zhang, Run He, Chen Jiao et al.

semantic segmentationfoundation modelsactive label correctionzero-shot predictions+3

#48

Active Label Correction for Semantic Segmentation with Foundation Models

Hoyoung Kim, SEHYUN HWANG, Suha Kwak et al.

ICML 2024

CVPR 2024arXiv:2404.10966

#49

Domain-Specific Block Selection and Paired-View Pseudo-Labeling for Online Test-Time Adaptation

Yeonguk Yu, Sungho Shin, Seunghyeok Back et al.

ICLR 2024arXiv:2203.09410

#50

Learning from True-False Labels via Multi-modal Prompt Retrieving

Zhongnian Li, Jinghao Xu, Peng Ying et al.

A Framework and Benchmark for Deep Batch Active Learning for Regression

David Holzmüller, Viktor Zaverkin, Johannes Kästner et al.

ICLR 2024arXiv:2310.08056

#52

Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation

Shreyas Havaldar, Navodita Sharma, Shubhi Sareen et al.

ICML 2025arXiv:2306.07566

#53

#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models

Keming Lu, Hongyi Yuan, Zheng Yuan et al.

AutoEval Done Right: Using Synthetic Data for Model Evaluation

Pierre Boyeau, Anastasios Angelopoulos, Tianle Li et al.

Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs

Feiyang Kang, Hoang Anh Just, Yifan Sun et al.

Learning with Selectively Labeled Data from Multiple Decision-makers

Jian Chen, Zhehao Li, Xiaojie Mao

ICML 2024arXiv:2401.12926

#57

DsDm: Model-Aware Dataset Selection with Datamodels

Logan Engstrom

dataset selectionlanguage model trainingdata optimizationmodel performance+1

active learninguncertainty-based samplingdiversity-based samplingfeature learning+4

#58

Active Reward Modeling: Adaptive Preference Labeling for Large Language Model Alignment

Yunyi Shen, Hao Sun, Jean-Francois Ton

Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples

Dake Bu, Wei Huang, Taiji Suzuki et al.

ICML 2024

implicit meta-learninglarge language modelsdocument usefulness indicatorsfine-tuning datasets+3

#60

Implicit meta-learning may lead language models to trust more reliable sources

Dmitrii Krasheninnikov, Egor Krasheninnikov, Bruno Mlodozeniec et al.

ICML 2024

CVPR 2024arXiv:2403.12236

#61

MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning

Zihan Chen, Song Wang, Zhen Tan et al.

Improving Generalization via Meta-Learning on Hard Samples

Nishant Jain, Arun Suggala, Pradeep Shenoy

ICML 2024arXiv:2402.08114

#63

Active Preference Learning for Large Language Models

William Muldrew, Peter Hayes, Mingtian Zhang et al.

preference learninglarge language modelsreinforcement learning from human feedbackdirect preference optimization+4

ICLR 2024arXiv:2310.10873

#64

IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models

Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang et al.

CVPR 2024arXiv:2403.11674

#65

Non-Stationary Predictions May Be More Informative: Exploring Pseudo-Labels with a Two-Phase Pattern of Training Dynamics

Hongbin Pei, Jingxin Hai, Yu Li et al.

The Human-AI Substitution game: active learning from a strategic labeler

Tom Yan, Chicheng Zhang

Towards Generalizing to Unseen Domains with Few Labels

Chamuditha Jayanga Galappaththige, Sanoojan Baliah, Malitha Gunawardhana et al.

ICML 2025arXiv:2506.08607

#68

Towards the Fundamental Limits of Knowledge Transfer over Finite Domains

Qingyue Zhao, Banghua Zhu

Sample Efficient Demonstration Selection for In-Context Learning

Kiran Purohit, Venktesh V, Sourangshu Bhattacharya et al.

ICML 2025arXiv:2503.04706

#70

Boosting Multi-Domain Fine-Tuning of Large Language Models through Evolving Interactions between Samples

Xize Liang, Lin Yang, Jie Wang et al.

Sample-Optimal Agnostic Boosting with Unlabeled Data

Udaya Ghai, Karan Singh

ICML 2025arXiv:2410.13853

#72

Weakly-Supervised Audio-Visual Video Parsing with Prototype-based Pseudo-Labeling

Kranthi Kumar Rachavarapu, Kalyan Ramakrishnan, A. N. Rajagopalan

Active Prompt Learning in Vision Language Models

Jihwan Bang, Sumyeong Ahn, Jae-Gil Lee

AutoAL: Automated Active Learning with Differentiable Query Strategy Search

Yifeng Wang, Xueying Zhan, Siyu Huang

CVPR 2024arXiv:2406.00510

#75

Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection

Jiaming Li, Jiacheng Zhang, Jichang Li et al.

ICML 2025arXiv:2503.00808

#76

Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation

Shyam Nuggehalli, Jifan Zhang, Lalit Jain et al.

Nearly Optimal Sample Complexity for Learning with Label Proportions

Robert Busa-Fekete, Travis Dick, Claudio Gentile et al.

Predictive Data Selection: The Data That Predicts Is the Data That Teaches

KaShun SHUM, Yuzhen Huang, Hongjian Zou et al.

CVPR 2024arXiv:2303.10365

#79

Positive-Unlabeled Learning by Latent Group-Aware Meta Disambiguation

Lin Long, Haobo Wang, Zhijie Jiang et al.

Learning with Structural Labels for Learning with Noisy Labels

Noo-ri Kim, Jin-Seop Lee, Jee-Hyong Lee

Navigating Real-World Partial Label Learning: Unveiling Fine-Grained Images with Attributes

CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning

Shiyu Tian, Hongxin Wei, Yiqun Wang et al.

AAAI 2024arXiv:2312.09505

#83

ELFS: Label-Free Coreset Selection with Proxy Training Dynamics

Haizhong Zheng, Elisa Tsai, Yifu Lu et al.

Enhancing Semi-supervised Domain Adaptation via Effective Target Labeling

Jiujun He, Bin Liu, Guosheng Yin

Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning

Mengmeng Sheng, Zeren Sun, Zhenhuang Cai et al.

noisy label learningpartial label learningnegative learninglabel disambiguation+3

AAAI 2025arXiv:2401.02329

#86

CoLAL: Co-learning Active Learning for Text Classification

Linh Le, Genghong Zhao, Xia Zhang et al.

Conformal Prediction for Partial Label Learning

Xiuwen Gong, Nitin Bisht, Guandong Xu

Improving Model Probability Calibration by Integration of Large Data Sources with Biased Labels

Renat Sergazinov, Richard Chen, Cheng Ji et al.

Optimizing Label Assignment for Weakly Supervised Person Search

Haiyang Zhu, Xi Yang, Nannan Wang

Exploring Vacant Classes in Label-Skewed Federated Learning

Kuangpu Guo, Yuhe Ding, Jian Liang et al.

AAAI 2025arXiv:2412.15276

#91

Exploring Query Efficient Data Generation Towards Data-Free Model Stealing in Hard Label Setting

Gaozheng Pei, Shaojie Lyu, Ke Ma et al.

ICLR 2025arXiv:2410.11215

#92

A CLIP-Powered Framework for Robust and Generalizable Data Selection

Suorong Yang, Peng Ye, Wanli Ouyang et al.

data selectionmultimodal informationclip frameworksample scoring+4

AAAI 2024arXiv:2312.14126

#93

Entropic Open-Set Active Learning

Bardia Safaei, Vibashan VS, Celso de Melo et al.

active learningopen-set recognitionentropy-based samplingunknown category detection+2

test-time adaptationpseudo-label refinementselective label enhancementdomain adaptation+3

#94

Learnware Specification via Label-Aware Neural Embedding

Wei Chen, Jun-Xiang Mao, Min-Ling Zhang

Gradient-Based Sample Selection for Black-Box Universal Domain Adaptation

Qiuyan He, Minghua Deng

Selective Label Enhancement Learning for Test-Time Adaptation

Yihao Hu, Congyu Qiao, Xin Geng et al.

ICLR 2025

#97

Disentangled Partial Label Learning

Wei-Xuan Bao, Yong Rui, Min-Ling Zhang

Can You Rely on Synthetic Labellers in Preference-Based Reinforcement Learning? It’s Complicated

Katherine Metcalf, Miguel Sarabia, Masha Fedzechkina et al.

Multi-Instance Multi-Label Classification from Crowdsourced Labels

Ziquan Wang, Mingxuan Xia, Xiangyu Ren et al.

Little Is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning

Amr Abourayya, Jens Kleesiek, Kanishka Rao et al.

AAAI 2025