Update README.md

README.md

@@ -28,6 +28,7 @@ Paris Noah's Ark Lab consists of 3 research teams that cover the following topic
 
    - [TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning](https://huggingface.co/papers/2502.15425): distributed multi-agent hierarchical reinforcement learning framework.
    - [AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting](https://arxiv.org/abs/2502.10235): simple yet powerful tricks to extend foundation models.
+   - [SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation](https://arxiv.org/abs/2407.11676): benchmark of shallow and deep domain adaptation method with realistic validation 
    - [Clustering Head: A Visual Case Study of the Training Dynamics in Transformers](https://arxiv.org/abs/2410.24050): visual and theoretical understanding of training dynamics in transformers.
    - [Large Language Models as Markov Chains](https://huggingface.co/papers/2410.02724):  theoretical insights on their generalization and convergence properties.
    - [A Systematic Study Comparing Hyperparameter Optimization Engines on Tabular Data](https://balazskegl.medium.com/navigating-the-maze-of-hyperparameter-optimization-insights-from-a-systematic-study-6019675ea96c): insights to navigate the maze of hyperopt techniques.