Active Learning

ICML 2024arXiv:2404.14367

#2

Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data

Fahim Tajwar, Anikait Singh, Archit Sharma et al.

preference fine-tuninglarge language modelson-policy reinforcement learningcontrastive learning+4

179

ICLR 2024arXiv:2308.07074

#3

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

Keming Lu, Hongyi Yuan, Zheng Yuan et al.

109

ICML 2024arXiv:2401.12926

#4

DsDm: Model-Aware Dataset Selection with Datamodels

Logan Engstrom

dataset selectionlanguage model trainingdata optimizationmodel performance+1

94

ICLR 2024arXiv:2307.12375

#5

In-Context Learning Learns Label Relationships but Is Not Conventional Learning

Jannik Kossen, Yarin Gal, Tom Rainforth

56

ICLR 2024arXiv:2203.09410

#6

A Framework and Benchmark for Deep Batch Active Learning for Regression

David Holzmüller, Viktor Zaverkin, Johannes Kästner et al.

52

ICML 2024arXiv:2402.08114

#7

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

46

ICLR 2025arXiv:2410.13788

#8

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

37

ICML 2025arXiv:2403.07008

#9

AutoEval Done Right: Using Synthetic Data for Model Evaluation

Pierre Boyeau, Anastasios Angelopoulos, Tianle Li et al.

36

CVPR 2024arXiv:2403.04272

#10

Active Generalized Category Discovery

Shijie Ma, Fei Zhu, Zhun Zhong et al.

34

CVPR 2025arXiv:2501.09781

#11

VideoWorld: Exploring Knowledge Learning from Unlabeled Videos

Zhongwei Ren, Yunchao Wei, Xun Guo et al.

33

AAAI 2024arXiv:2312.09505

#12

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

33

ICLR 2024arXiv:2405.02774

#13

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

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

32

ICML 2024arXiv:2403.03208

#14

Active Statistical Inference

Tijana Zrnic, Emmanuel J Candes

active learningstatistical inferenceconfidence intervalshypothesis tests+4

31

AAAI 2024arXiv:2312.14126

#15

Entropic Open-Set Active Learning

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

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

29

ICLR 2024arXiv:2310.10873

#16

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

Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang et al.

28

ECCV 2024arXiv:2312.05328

#17

Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding

Talfan Evans, Shreya Pathak, Hamza Merzic et al.

active learningdata efficiencyvisual understandingmultimodal models+4

25

CVPR 2024arXiv:2311.11178

#18

Active Prompt Learning in Vision Language Models

Jihwan Bang, Sumyeong Ahn, Jae-Gil Lee

22

ICML 2024arXiv:2406.10502

#19

Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data

Jiahan Zhang, Qi Wei, Feng Liu et al.

vision-language modelspseudolabeling strategyprompt tuningzero-shot performance+3

22

ECCV 2024arXiv:2407.08966

#20

LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Models

Yabin Zhang, Wenjie Zhu, Chenhang He et al.

19

ICLR 2025arXiv:2410.11215

#21

A CLIP-Powered Framework for Robust and Generalizable Data Selection

Suorong Yang, Peng Ye, Wanli Ouyang et al.

data selectionmultimodal informationclip frameworksample scoring+4

17

AAAI 2024arXiv:2312.07169

#22

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

17

ICML 2024arXiv:2402.17327

#23

Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond

Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger et al.

data selection problemsensitivity samplingk-means clusteringfoundation model fine-tuning+4

16

AAAI 2024arXiv:2305.04203

#24

Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning

Wenhai Wan, Shao-Yuan Li, Xinrui Wang et al.

15

ECCV 2024arXiv:2407.02778

#25

Foster Adaptivity and Balance in Learning with Noisy Labels

Mengmeng Sheng, Zeren Sun, Tao Chen et al.

noisy label learningsample selection strategylabel noise correctionclass balance+4

14

CVPR 2024arXiv:2202.04291

#26

L2B: Learning to Bootstrap Robust Models for Combating Label Noise

Yuyin Zhou, Xianhang li, Fengze Liu et al.

14

AAAI 2024arXiv:2305.13764

#27

Mitigating Label Noise through Data Ambiguation

Julian Lienen, Eyke Hüllermeier

13

ICML 2025arXiv:2410.05952

#28

Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning

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

Active Evaluation Acquisition for Efficient LLM Benchmarking

Yang Li, Jie Ma, Miguel Ballesteros et al.

13

ICML 2025arXiv:2503.00808

#30

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

KaShun SHUM, Yuzhen Huang, Hongjian Zou et al.

12

ECCV 2024arXiv:2407.07268

#31

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

Pingting Hao, Kunpeng Liu, Wanfu Gao

Dataset Quantization with Active Learning based Adaptive Sampling

Zhenghao Zhao, Yuzhang Shang, Junyi Wu et al.

11

ICML 2024arXiv:2311.15502

#33

Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical

Wei Wang, Takashi Ishida, Yu-Jie Zhang et al.

complementary-label learningweakly supervised learningpositive-unlabeled learningrisk estimator+4

11

ICML 2025arXiv:2505.16225

#34

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

Zihan Chen, Song Wang, Zhen Tan et al.

11

CVPR 2024arXiv:2303.10365

#35

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

Shiyu Tian, Hongxin Wei, Yiqun Wang et al.

partial-label learningweakly supervised learninglabel ambiguitypseudo label generation+2

10

ECCV 2024arXiv:2407.18899

#36

Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence

Mengyao Lyu, Tianxiang Hao, Xinhao Xu et al.

domain adaptationsource-free adaptationactive learningcontrastive sampling+4

10

AAAI 2024arXiv:2312.12237

#37

Roll with the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning

Yue Duan, Zhen Zhao, Lei Qi et al.

9

ECCV 2024arXiv:2407.13254

#38

Make a Strong Teacher with Label Assistance: A Novel Knowledge Distillation Approach for Semantic Segmentation

Shoumeng Qiu, Jie Chen, Xinrun Li et al.

knowledge distillationsemantic segmentationlabel noise injectiondual-path consistency+3

9

CVPR 2024arXiv:2403.11674

#39

Towards Generalizing to Unseen Domains with Few Labels

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

semi-supervised domain generalizationdomain-generalizable featuresfeature-based conformityposterior distribution matching+3

ICLR 2024arXiv:2401.09787

#40

Querying Easily Flip-flopped Samples for Deep Active Learning

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

CVPR 2025arXiv:2503.02491

#41

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

Sebastian Schmidt, Leonard Schenk, Leo Schwinn et al.

active learningout-of-distribution detectioncategory discoverydata filtering+2

AAAI 2024arXiv:2312.14369

#42

Quality-Diversity Generative Sampling for Learning with Synthetic Data

Allen Chang, Matthew C. Fontaine, Serena Booth et al.

ICLR 2025arXiv:2406.04273

#43

ELFS: Label-Free Coreset Selection with Proxy Training Dynamics

Haizhong Zheng, Elisa Tsai, Yifu Lu et al.

CVPR 2024arXiv:2403.12236

#44

Improving Generalization via Meta-Learning on Hard Samples

Nishant Jain, Arun Suggala, Pradeep Shenoy

CVPR 2025arXiv:2503.07591

#45

Filter Images First, Generate Instructions Later: Pre-Instruction Data Selection for Visual Instruction Tuning

Bardia Safaei, Faizan Siddiqui, Jiacong Xu et al.

AAAI 2024arXiv:2401.04923

#46

Inconsistency-Based Data-Centric Active Open-Set Annotation

Ruiyu Mao, Ouyang Xu, Yunhui Guo

active learningopen-set annotationdata-centric methodsunknown class identification+3

ICML 2024arXiv:2403.01942

#47

Mitigating Label Noise on Graphs via Topological Sample Selection

Yuhao Wu, Jiangchao Yao, Xiaobo Xia et al.

label noise mitigationgraph neural networkssample selectionnon-iid graph data+4

ICML 2025arXiv:2411.17284

#48

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

Alexander Capstick, Rahul G. Krishnan, Payam Barnaghi

ICML 2025arXiv:2312.09196

#49

Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation

Shyam Nuggehalli, Jifan Zhang, Lalit Jain et al.

ICML 2024arXiv:2310.15047

#50

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

Dmitrii Krasheninnikov, Egor Krasheninnikov, Bruno Mlodozeniec et al.

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

temporal action segmentationactive learningcontrastive learningaction prototypes+3

#51

Two-Stage Active Learning for Efficient Temporal Action Segmentation

Yuhao Su, Ehsan Elhamifar

ECCV 2024

ICLR 2025arXiv:2410.10636

#52

Adapt-$\infty$: Scalable Continual Multimodal Instruction Tuning via Dynamic Data Selection

Adyasha Maharana, Jaehong Yoon, Tianlong Chen et al.

ECCV 2024arXiv:2402.15198

#53

Partial Label Learning with a Partner

Chongjie Si, Zekun Jiang, Xuehui Wang et al.

Bidirectional Uncertainty-Based Active Learning for Open-Set Annotation

ChenChen Zong, Ye-Wen Wang, Kun-Peng Ning et al.

active learningopen set recognitionuncertainty samplingunknown class detection+3

ICLR 2025arXiv:2412.18303

#55

Efficient and Context-Aware Label Propagation for Zero-/Few-Shot Training-Free Adaptation of Vision-Language Model

Yushu Li, Yongyi Su, Adam Goodge et al.

ICCV 2025arXiv:2506.05312

#56

Do It Yourself: Learning Semantic Correspondence from Pseudo-Labels

Olaf Dünkel, Thomas Wimmer, Christian Theobalt et al.

AAAI 2025arXiv:2503.17017

#57

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

Aoting Zhang, Dongbao Yang, Chang Liu et al.

multi-label classificationclass-incremental learningcatastrophic forgettingdynamic feature purification+2

ICLR 2024arXiv:2310.07838

#58

Towards the Fundamental Limits of Knowledge Transfer over Finite Domains

Qingyue Zhao, Banghua Zhu

ECCV 2024arXiv:2305.19486

#59

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

ICLR 2025arXiv:2501.14278

#60

Active Learning for Continual Learning: Keeping the Past Alive in the Present

Jaehyun Park, Dongmin Park, Jae-Gil Lee

ICML 2025arXiv:2506.08607

#61

Sample Efficient Demonstration Selection for In-Context Learning

Kiran Purohit, Venktesh V, Sourangshu Bhattacharya et al.

AAAI 2025arXiv:2501.15201

#62

A Training-free Synthetic Data Selection Method for Semantic Segmentation

Hao Tang, Siyue Yu, Jian Pang et al.

ECCV 2024arXiv:2407.19675

#63

Semi-Supervised Teacher-Reference-Student Architecture for Action Quality Assessment

Wulian Yun, Mengshi Qi, Fei Peng et al.

NEURIPS 2025arXiv:2505.19051

#64

Efficient Data Selection at Scale via Influence Distillation

Mahdi Nikdan, Vincent Cohen-Addad, Dan Alistarh et al.

ICML 2024arXiv:2403.10820

#65

Active Label Correction for Semantic Segmentation with Foundation Models

Hoyoung Kim, SEHYUN HWANG, Suha Kwak et al.

semantic segmentationfoundation modelsactive label correctionzero-shot predictions+3

ICCV 2025arXiv:2412.06293

#66

Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness

Qifan Yu, Zhebei Shen, Zhongqi Yue et al.

ECCV 2024arXiv:2406.04898

#67

Labeled Data Selection for Category Discovery

Bingchen Zhao, Nico Lang, Serge Belongie et al.

CVPR 2025arXiv:2502.19691

#68

Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach

Chen-Chen Zong, Sheng-Jun Huang

AAAI 2025arXiv:2504.17474

#69

Enhancing Multi-Label Classification via Dynamic Label-Order Learning

Jiangnan Li, Yice ZHANG, Shiwei Chen et al.

CoLAL: Co-learning Active Learning for Text Classification

Linh Le, Genghong Zhao, Xia Zhang et al.

Enhanced Sample Selection with Confidence Tracking: Identifying Correctly Labeled Yet Hard-to-Learn Samples in Noisy Data

Weiran Pan, Wei Wei, Feida Zhu et al.

ICLR 2024arXiv:2310.08056

#72

Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation

Shreyas Havaldar, Navodita Sharma, Shubhi Sareen et al.

ECCV 2024arXiv:2407.16802

#73

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

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

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

#74

Selective Label Enhancement Learning for Test-Time Adaptation

Yihao Hu, Congyu Qiao, Xin Geng et al.

ICLR 2025

CVPR 2025arXiv:2503.14564

#75

Effortless Active Labeling for Long-Term Test-Time Adaptation

Guowei Wang, Changxing Ding

ICCV 2025arXiv:2411.13317

#76

Teaching VLMs to Localize Specific Objects from In-context Examples

Sivan Doveh, Nimrod Shabtay, Eli Schwartz et al.

vision-language modelsfew-shot localizationpersonalized object localizationin-context learning+4

ICLR 2025arXiv:2410.02268

#77

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

Tianchi Xie, Jiangning Zhu, Guozu Ma et al.

sample selectionstructural entropygraph decompositionshapley value+4

ICML 2025arXiv:2505.05355

#78

Nearly Optimal Sample Complexity for Learning with Label Proportions

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

ICLR 2025arXiv:2501.07886

#79

Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision

Yaowen Ye, Cassidy Laidlaw, Jacob Steinhardt

ICML 2024arXiv:2406.03944

#80

Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples

Dake Bu, Wei Huang, Taiji Suzuki et al.

active learninguncertainty-based samplingdiversity-based samplingfeature learning+4

AAAI 2025arXiv:2412.15276

#81

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

Gaozheng Pei, Shaojie Lyu, Ke Ma et al.

ICML 2025arXiv:2505.19680

#82

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

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

ECCV 2024arXiv:2407.07673

#83

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

Feixiang Zhou, Bryan M. Williams, Hossein Rahmani

semi-supervised learningtemporal action localizationpseudo-label learninglabel quality assessment+2

ICCV 2025arXiv:2507.12998

#84

Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning

Zihua Zhao, Feng Hong, Mengxi Chen et al.

ICCV 2025arXiv:2507.10225

#85

Synthesizing Near-Boundary OOD Samples for Out-of-Distribution Detection

Jinglun Li, Kaixun Jiang, Zhaoyu Chen et al.

CVPR 2025arXiv:2506.09473

#86

Provoking Multi-modal Few-Shot LVLM via Exploration-Exploitation In-Context Learning

Cheng Chen, Yunpeng Zhai, Yifan Zhao et al.

in-context learninglarge vision-language modelsmulti-modal demonstration selectionexploration-exploitation reinforcement learning+3

ICLR 2025arXiv:2410.19631

#87

Efficient Biological Data Acquisition through Inference Set Design

Ihor Neporozhnii, Julien Roy, Emmanuel Bengio et al.

sequential subset selectionactive learningdrug discoveryhigh-throughput screening+3

ECCV 2024arXiv:2306.11911

#88

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

Qingzheng Huang, Xilin He, Xiaole Xian et al.

LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge Integration

Siqi Wang, Bryan Plummer

learning with noisy labelsnoise source knowledgelabel noise correctionrobust model training+2

ICML 2025arXiv:2306.07566

#90

Learning with Selectively Labeled Data from Multiple Decision-makers

Jian Chen, Zhehao Li, Xiaojie Mao

ICML 2025arXiv:2503.04706

#91

Sample-Optimal Agnostic Boosting with Unlabeled Data

Udaya Ghai, Karan Singh

CVPR 2024arXiv:2403.14430

#92

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

Tianming Liang, Chaolei Tan, Beihao Xia et al.

open-ended video qamulti-label classificationranking distillationinsufficient labeling+3

ICCV 2025arXiv:2507.23771

#93

Consensus-Driven Active Model Selection

Justin Kay, Grant Horn, Subhransu Maji et al.

active model selectionprobabilistic frameworkbayesian inferencelabel acquisition+4

AAAI 2024arXiv:2301.02761

#94

Active Learning Guided by Efficient Surrogate Learners

Yunpyo An, Suyeong Park, Kwang In Kim

active learningbatch-based samplinggaussian process surrogatesurrogate learners+3

NEURIPS 2025arXiv:2502.08227

#95

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.

Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples

Suqin Yuan, Lei Feng, Bo Han et al.

ECCV 2024arXiv:2403.06289

#97

Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning

Zijun Long, Lipeng Zhuang, George W Killick et al.

AAAI 2025arXiv:2412.11620

#98

Combating Semantic Contamination in Learning with Label Noise

Wenxiao Fan, Kan Li

label noisesemantic contaminationlabel refurbishmentsemi-supervised learning+2

semi-supervised learningactive learningpseudo-labelingfeature alignment+3

#99

Towards Cost-Effective Learning: A Synergy of Semi-Supervised and Active Learning

Tianxiang Yin, Ningzhong Liu, Han Sun

CVPR 2025

CVPR 2025arXiv:2512.16202

#100

Open Ad-hoc Categorization with Contextualized Feature Learning

Zilin Wang, Sangwoo Mo, Stella X. Yu et al.

ad-hoc categorizationcontextualized feature learningsemantic extensionvisual clustering+4