Learning Representation and Clustering (RLC)

In conjunction with  PRICAI 2026 November. Nov 17-20, 2026, PRICAI 2026 Guangzhou, China

Workshop Overview

Data clustering and representation learning play an indispensable role in data science. They are very useful to explore massive data in many fields, including information retrieval, natural language processing, bioinformatics, recommender systems and computer vision. Despite their success, most existing clustering methods are severely challenged by the data generated by modern applications, which are typically high dimensional, noisy, heterogeneous and sparse or even collected from multiple sources or represented by multiple views where each describes a perspective of the data. This has driven many researchers to investigate new deep clustering models to overcome these difficulties. One promising category of such models relies on representation learning. Indeed learning a good data representation is crucial for clustering algorithms and combining the two tasks is a common way of exploring this type of data. The idea is to embed the original data into a low dimensional latent space and then perform clustering on this new space. Both tasks can be carried out sequentially or jointly; combining the two tasks is a common way of exploring this type of data.

Hence, one main goal of the workshop is to bring together the leading researchers who work on state-of-the-art deep unsupervised feature extraction and clustering models, and also the practitioners who seek novel applications. In summary, this workshop is an opportunity to:

• Present the recent advances in representation learning and clustering methods including multi-view clustering and semi-supervised learning which are not explored well.

• Outline potential applications that could inspire new deep approaches.

• Explore benchmark data to better evaluate and study deep clustering models.

• Evaluate the effectiveness of deep clustering models compared to classical approaches in terms of  interpretability of clusters and Scalability .

The workshop is co-located with the  PRICAI2026 Pacific Rim International Conjerence on Artificial Intelligence - Nov 17-20, 2026).

Important Dates

• Workshop paper submissions: July 20, 2026, 25, September 2026

• Workshop paper notifications:  October 05, 2026

• Camera-ready deadline and copyright forms: October 10, 2026

• Early bird registration finishes: TBA

• Conference dates: November. 17 - 20, 2026

**All deadlines are 11:59 pm anywhere on earth**

Workshop Chairs

Workshop Organizers

• Lazhar Labiod: lazhar.labiod@u-paris.fr, Université de Paris Cité

• Mohamed Nadif: mohamed.nadif@u-paris.fr, Université de Paris Cité

Workshop Contact

• lazhar.labiod@u-paris.fr