ARRL 2026 workshop

For years, machine learning has advanced artificial intelligence (AI) by enabling the development of systems that generate models from various databases without explicit instruction. The growing availability of data across various fields has led to the proliferation of learning-enabled systems, which embed machine learning components in the core, that have become increasingly powerful and integral to industry and everyday life. Data mining techniques allow such systems to examine vast quantities of data, identifying subtle features that often elude human capabilities. However, these techniques frequently rely on oversimplified learning objectives and data that may be biased, incomplete, or even hazardous. The transition from learning-enabled systems into real-world decision-making contexts thus can pose risks, primarily due to their limited adaptability, reliability, and responsibility in dealing with unfamiliar or unknown circumstances.

The 4th International Workshop on Adaptable, Reliable, and Responsible Learning (ARRL) aims to gather researchers and practitioners to present recent advancements in addressing the three key aspects of learning within the context of data-driven and data-centric systems: adaptability, reliability, and responsibility. The workshop will explore theoretical foundations, algorithm designs, and frameworks that ensure future learning-enabled systems are

1) *Adaptable*, by exhibiting evolvability with changes in the environment, societal dynamics, and task objectives or requirements, ensuring that the system remains relevant and effective in addressing diverse and dynamic challenges while maintaining high-performance standards;

2) *Reliable*, by demonstrating robustness and stability in the presence of uncertainty, variability, and unknown unknowns, ensuring system safety and performance consistency across diverse conditions and high-stakes operating environments; and

3) *Responsible*, by promoting sustainability, fairness, explainability, and trustworthiness in learning processes and outcomes, addressing ethical and privacy concerns and championing technology use for positive societal impact including solutions for affordable clean energy and climate action.

This workshop cordially invites submissions that showcase cutting-edge advances in research and development of adaptable, reliable, and responsible (ARR) learning algorithms and designs, as well as late-breaking research that introduces published work or software that address ARR challenges and provide significant value to the community.

Past Events: Held on ARRL 2023 (Shanghai, China), ARRL 2024 (Abu Dhabi, UAE) and ARRL 2025 (Washington DC, USA).

IMPORTANT DATES

August 20^th, 2026	Papers Submission
September 18^th, 2026	Author Notification
October 5^th, 2026	Camera-ready and Copyright forms
November 15^th, 2026	Workshop Date

All deadlines are due 11:59 pm Anywhere on Earth (AOE).

TOPICS

Theory, methodology, and resource papers are welcome from any of the following areas, including but not limited to:

Adaptable Learning

Online/Incremental Learning
Transfer Learning and Domain Adaptation
Lifelong/Continual/Meta Learning
Learning from Heterogeneous and Multi-Modal Data
Knowledge Discovery from Multiple Databases
Learning with Rejection/Abstention
Cross-Domain Data Mining
Evolving Data Stream Mining
Ensemble Learning in Dynamic Environments

Reliable Learning

Robustness and Generalization in Data Mining
Trustworthiness in Learning-enabled Systems
Noise Handling and Outlier/Anomaly Detection
Data Wrangling and Munging for Reliable Preprocessing
Data Quality Assessment and Assurance
Robustness in Graph and Network Mining
Uncertainty Quantification and Confidence Estimation in Learning-enabled Systems
Learning with Very Few Examples
Open-World Learning (Learning in Unexpected/Unknown Environments)

Responsible Learning

Explainable Learning Modules and Architectures
Interpretability of Learning Results
Algorithmic Fairness in Data Mining
Discrimination-aware Data Mining
Privacy-Preserving Data Mining
Ethical Data Mining and Data Usage
Socio-technical Aspects of Data Mining
Bias Detection and Mitigation in Learning-enabled Systems
AI for Environmental and Social Sustainability
Data Mining for Energy Efficiency and Climate Action

SUBMISSION AND PUBLICATION

Paper submission link: International Workshop on Adaptable, Reliable, and Responsible Learning (ARRL).

Paper submissions should be limited to a maximum of 8 pages, and follow the IEEE ICDM format. More detailed information is available in the IEEE ICDM 2026 Submission Guidelines.

All accepted papers will be included in the ICDM'26 Workshop Proceedings (ICDMW 2026) published by the IEEE Computer Society Press. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.

All accepted papers, including workshops, must have at least one "FULL" registration. A full registration is either a "member" or "non-member" registration. Student registrations are not considered full registrations. All authors are required to register by 15th October 2026.

For registration queries please contact: registration@computer.org

PROGRAM

Sunday, November 15, 2026 — Workshop W29 (ARRL)

Venue: Berlin 1

Time (UTC+8)	Title	Presenter/Author
13:30–13:35	Opening Remarks	—
13:35–14:05	Keynote Talk (Title TBA)	Speaker TBA
14:05–14:20	When Do Label-Free LLM Divergence Claims Reproduce? A Reliability Audit over Domain Taxonomies [Paper DM1289]	Yinan LIU, Tengfei Ma, Weimin Lyu, Xinyu Dong, Xiaojie Fan, Rachel Wong, Richard Rosenthal, Henry Lam, and Fusheng Wang
14:20–14:35	Does Model Uncertainty Track Human Ambiguity? Evidence from Multi-Annotator Vision Benchmarks [Paper S16213]	Manya Singh and Arjun Pakrashi
14:35–14:50	Divergence Aware Isolation Forest via Branch Occupancy Distributions [Paper S16211]	Albert Rachwał, Alicja Rachwał, and Paweł Karczmarek
14:50–15:05	Leakage-Safe Block-Granular Mining of Energy-Drink Survey Data:Stable Behavioural Signatures in a High-Dimensional Questionnaire [Paper DM991]	Adam Kiersztyn, Paweł Krukow, Krystyna Kiersztyn, Natalia Kupis-Posiej, Kacper Romaniuk, and Andrzej Cudo
15:05–15:20	Dynamic Regret for Unconstrained Online Regression via the Follow-the-Leading-History Algorithm with Vovk--Azoury--Warmuth Forecasters [Paper S16205]	Viacheslav Potapov and Dmitry Rokhlin
15:20–15:30	Transition / Buffer	—
15:30–15:50	Coffee Break	—
15:50–16:05	One-Shot Domain Calibration of Mixture-of-Experts Routers via Gradient Bias Steering [Paper S16207]	Marinа Frolova, Ivan Shchukin, Evgeny Bessonnitsyn, Konstantin Pakulnevich, Timur Gabbasov, Aleksandr Ustinov, and Sergey Muravyov
16:05–16:20	CLIP-CVAE: Attribute-Conditioned Feature Synthesis for Generalized Zero-Shot Learning [Paper S16214]	Muqaddas Gull, Mubashir Ahmad, Huma Naj, and Samee Ullah Khan
16:20–16:35	TRACE-LLM: A Replayable and Certificate-Driven Transformation Framework for Domain-Specialized Large Language Models [Paper S16216]	Kamal Taha
16:35–16:50	SCOF: Causal Risk Mining and Cost-Aware Intervention for AI Agent Safety [Paper S16215]	Satyam Mishra, Phung Thao Vi, Anchit Bijalwan, Tan Duc Tran, and Vishwanath Bijalwan
16:50–17:05	Mining Multi-Model Failure Patterns for Reliability Auditing of 3D Lung Nodule Malignancy Predictors [Paper DM1153]	Xin Meng, Xiaopeng Li, Huixin Yu, Yuhong Zhao, and Qing Chang
17:05–17:20	Interpretable Neuro-Symbolic Learning for Robust Tennis Biomechanical Error Detection under Distribution Shift [Paper M2209]	Chunchen Lin, Linhai Zhu, and Chunfeng Lin
17:20–17:35	Adaptive Reasoning with Recursive Decomposition [Paper S16212]	Jingjun Tang, Limin Yan, Lixing Yu, Kun Yue, and Zhiwen Tang
17:35–17:50	KARD: A Knowledge-Anchored Reasoning Distillation Framework for Domain-Specific Small-Scale LLMs via Attraction Basin Regularization [Paper M1472]	Gen Li, Zhichao Yang, Yuanze Hu, Ye Qiu, Xinyu Wang, Yifan Sun, Zhaoxin Fan, Faguo Wu, Hongwei Zheng, and Wenjun Wu
17:50–17:55	Buffer	—
17:55–18:00	Closing Remarks	—