Conference Information

ALIS 2026: Adaptive Learning and Intelligent Systems Conference

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Submission Date:
2025-12-19 Extended
Notification Date:
2026-01-21
Conference Date:
2026-02-12
Location:
Melbourne, Australia
Years:
1
Viewed: 768   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

37.5 / 100
Ranked #5,537 of 5,682 conferences · Top 98%

#712 of 739 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50
Submission selectivity (20%) No data - scored at the neutral baseline of 50
Editions held (20%)
19
Community attention (10%)
10
Public record completeness (15%)
35

Inputs used: Editions on record: 1 · Researchers who opened this page in the past 24 months: 3

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-20

Call For Papers

ALIS 2026 (Adaptive Learning and Intelligent Systems Conference) is an academic conference held in Melbourne, Australia on 2026-02-12. The paper submission deadline is 2025-12-19 (extended). Acceptance notifications are sent on 2026-01-21.

Scope and Topics: We invite high-quality submissions on all areas of AI, with particular emphasis on learning-based and adaptive systems. Topics of interest include, but are not limited to: Generative AI: foundation models, multimodal generative systems, controllability, evaluation, safety Deep Learning Advances: architectures, training methods, interpretability, scalability Graph Neural Networks- based architectures, representations and applications Reinforcement Learning & Control: single-agent, multi-agent, hierarchical RL, safe and explainable RL Large Language Models (LLMs): reasoning, alignment, fine-tuning, efficient deployment, societal impact Quantum Machine Learning, variational quantum algorithms, quantum-enhanced optimization, hybrid quantum–classical models Adaptive Systems: online learning, continual learning, meta-learning, self-improving AI Cognitive & Neurosymbolic AI: hybrid approaches, reasoning and learning integration Trustworthy AI: fairness, robustness, explainability, human-centered design, responsible deployment Applications: healthcare, robotics, education, finance, climate, creative industries
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