Conference Information

MLTEC 2026: International Conference on Machine Learning Techniques

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Submission Date:
2026-10-11 Due today Extended
Notification Date:
2026-11-17
Conference Date:
2026-12-19
Location:
Sydney, Australia
Years:
Organizer:
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MLTEC
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Conference Partner Index (CP-I)

50.1 / 100
Ranked #1,598 of 5,695 conferences · Top 29%

#156 of 741 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%)
56
Community attention (10%)
31
Public record completeness (15%)
55

Inputs used: Editions on record: 7 · Researchers following it here: 4 · Researchers who opened this page in the past 24 months: 7

Missing from the public record: Historical acceptance rates (+4.5) · 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 2026-10-10

Call For Papers

MLTEC 2026 (International Conference on Machine Learning Techniques) is an academic conference held in Sydney, Australia on 2026-12-19. The paper submission deadline is 2026-10-11 (extended). Acceptance notifications are sent on 2026-11-17.

7th International Conference on Machine Learning Techniques (MLTEC 2026) December 19 ~ 20, 2026, Sydney, Australia https://mltec.org/ Scope & Topics 7th International Conference on Machine Learning Techniques (MLTEC 2026) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning Techniques. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Machine Learning Techniques. Topics of interest include, but are not limited to, the following • Advanced ML Systems, MLOps and Deployment • Adversarial Robustness and Secure ML • AI‑Hardware Co‑Design • AutoML and Hyperparameter Optimization • Causal Machine Learning • Climate Modeling and Environmental ML • Computer Vision and Visual Learning • Continual and Lifelong Learning • Data Mining and Knowledge Discovery • Diffusion Models for Non‑Vision Modalities • Edge ML, TinyML and On‑Device Learning • Embodied AI and Interactive Learning • Evaluation, Benchmarking and Safety of Foundation Models • Explainable and Interpretable ML • Fairness, Accountability and Responsible ML • Federated and Distributed Learning • Fine‑Tuning, Alignment and Adaptation of Foundation Models • Foundation Models: Architectures, Training and Adaptation • Fraud Detection and Risk Modeling • Generative AI: Diffusion, GANs and Autoregressive Models • Graph Foundation Models • Graph Machine Learning and GNNs • Knowledge Graphs and Reasoning • LLM Agents and Autonomous Decision‑Making • LLM‑Based Software Engineering • Machine Learning Applications • Machine Learning for NLP • Machine Translation • Meta‑Learning and Few‑Shot Learning • ML for Biology and Protein Modeling • ML for Chemistry and Drug Discovery • ML for Code and Program Synthesis • ML for Cybersecurity and Threat Detection • ML for Healthcare, Finance, Robotics and Science • ML‑Accelerated Compilers • Model‑Based RL and Generative Simulation • Multi‑Agent Reinforcement Learning • Multimodal Learning and Fusion • Multimodal LLMs (Vision‑Language‑Audio) • Neural Networks and Deep Learning • Neuro‑Symbolic Learning • Planning‑Enabled and Memory‑Augmented Agents • Privacy‑Preserving ML and Differential Privacy • Quantum Machine Learning • Recommender Systems • Reinforcement Learning and RLHF • Representation Learning • Responsible Foundation Models: Fairness, Safety and Governance • Scalable FM Training, Distributed Compute and ML Infrastructure • Scalable ML and Distributed Training • Self‑Supervised and Contrastive Learning • Sustainable AI Systems • Temporal Transformers and Sequence Modeling • Time‑Series Foundation Models • Tool‑Using AI Systems • Transfer Learning and Domain Adaptation • Ubiquitous and Pervasive ML • Vision‑Language‑Action Models (VLAMs) • World Models and Predictive Simulation Learning • XML, Databases and Structured Data Learning Paper Submission Authors are invited to submit papers through the conference Submission System by October 11, 2026. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed). Selected papers from MLTEC 2026, after further revisions, will be published in the special issues of the following journals. • International Journal of Artificial Intelligence & Applications (IJAIA) • Machine Learning and Applications: An International Journal (MLAIJ) • International Journal of Ubiquitous Computing (IJU) • Advances in Vision Computing: An International Journal (AVC) Important Dates • Submission Deadline: October 11, 2026 • Authors Notification: November 17, 2026 • Registration & Camera-Ready Paper Due: November 24, 2026 Contact Us Here's where you can reach us: [email protected] (or) [email protected] For more details, please visit: https://mltec.org/ Paper Submission Link: https://csea2026.org/submission/index.php
Last updated by Kristi Jencks on

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