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Hybrid Event

13th - 14th July 2026 | Tokyo, Japan

International Conference on Probabilistic Approaches in Machine Learning (ICPAPML - 26)

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4

Days

4

Hrs

07

Min

02

Sec

Call For Paper

The (ICPAPML) is dedicated to advancing research excellence by bringing together leading scholars, scientists, and professionals from across the globe. It provides a platform for the dissemination of high-quality research and innovative methodologies.

With a strong focus on Probability Theory, the conference promotes research that contributes to academic depth, practical insights, and interdisciplinary knowledge integration.

Authors are invited to submit papers addressing, but not limited to, the following areas:

01
Probabilistic models in machine learning
02
Bayesian methods for machine learning
03
Stochastic processes in AI applications
04
Probabilistic graphical models in ML
05
Uncertainty quantification in machine learning
06
Applications of Bayesian networks
07
Probabilistic approaches to deep learning
08
Statistical learning theory and applications
09
Reinforcement learning with probabilistic models
10
Probabilistic methods for natural language processing
11
Machine learning for predictive analytics
12
Ensemble methods in probabilistic learning
13
Probabilistic models for time series analysis
14
Applications of Markov models in ML
15
Probabilistic reasoning in AI systems
16
Statistical methods for model evaluation
17
Machine learning with incomplete data
18
Probabilistic approaches to computer vision
19
Applications of probabilistic models in healthcare
20
Probabilistic methods for anomaly detection

Peer Review Process

All submissions evaluated through structured peer-review to ensure academic rigor. Accepted papers may be considered for high-quality journals.

Registration Details

Secure your participation early. Limited slots are allocated on a first-come, first-served basis.

Publication Opportunities

High-quality submissions prioritized for publication in recognized journals and proceedings.

Submit Your Paper Today Register Early
2026 UPDATE

Consistent Academic Support

Science Net ensures that research activities continue without interruption in the current global situation. Participants can engage through digital and hybrid conference formats.