System: Based on the search results given, can you provide a concise overview of the main hypothesis or research question addressed in the article, along with the key methodologies used by the researchers to investigate it? Additionally, could you summarize the main findings obtained from the experiments and discuss their implications for advancing state-of-the-art research in the field? Lastly, please identify any limitations of the study and suggest potential areas for future research, while also evaluating the strengths and weaknesses of the presented conclusions. The answer should be well structured and between 300 and 400 words.

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[Page no. 3] "Reliability. We conduct comprehensive evaluations of various algorithms in LAMDA- SSL using multiple benchmark datasets, including CIFAR-10, Cora, Boston, among others. The ex- perimental results are accessible on the homepage. Most of the reproduced algorithms exhibit com- parable or improved performance compared to the results reported in the original papers. • Openness. LAMDA-SSL is distributed un- der the MIT license. Contributions from the com- munity are strongly welcome and easy enough be- cause the documentation provides numerous ex- amples showing how to customize modules of LAMDA-SSL. Conclusion. In this paper, we present an easy- to-use, powerful and open-source toolkit in Python for SSL with comprehensive functionality, simple interfaces, complete documentation, and the best support for algorithms, data types, and tasks com- pared with related toolkits. Our aim is to facilitate SSL research and applications by addressing the challenge of limited labeled data. Going forward, we plan to incorporate advanced algorithms into LAMDA-SSL and expand its application scope in open environments [9]. Acknowledgements This research was supported by the National Key R&D Program of China (2022ZD0114803), the National Science Foundation of China (62176118). References 1 Guo L Z, Zhang Z Y, Jiang Y, et al. Safe deep semi- supervised learning for unseen-class unlabeled data. In Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, 2020. 3897— 3906 2 Li Y F, Guo L Z, Zhou Z H. Towards safe weakly supervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(1): 334– 346 3 Guo L Z, Zhang Y G, Wu Z F, et al. Robust Semi- Supervised Learning when Not All Classes have La- bels. In: Koyejo S, Mohamed S, Agarwal A, et al. eds. Advances in Neural Information Processing Sys- tems, New Orleans, Los Angeles, United States, 2022. 3305–3317 4 Pedregosa F, Varoquaux G, Gramfort A,"

[Page no. 3] "et al. Scikit- learn: Machine learning in python. Journal of Machine Learning Research, 2011, 12(85): 2825–2830 5 Wang Y D, Chen H, Fan Y, et al. USB: A Uniﬁed Semi-supervised Learning Benchmark for Classiﬁca- tion. In: Koyejo S, Mohamed S, Agarwal A, et al. eds. Advances in Neural Information Processing Sys- tems Datasets and Benchmarks Track, New Orleans, Los Angeles, United States, 2022 6 Paszke A, Gross S, Massa F, et al. Pytorch: An im- perative style, high-performance deep learning library. In: Wallach H M, Larochelle H, Beygelzimer A, et al. eds. Advances in Neural Information Processing Sys- tems, Vancouver, BC, Canada, 2019. 8024–8035 7 Chapelle O, Scholkopf B, Zien A. Semi-Supervised Learning. The MIT Press, 2006 8 Oliver A, Odena A, Raﬀel C A, et al. Realistic evalu- ation of deep semi-supervised learning algorithms. In: Bengio S, Wallach H M, Larochelle H, et al. eds. Advances in Neural Information Processing Systems, Montr´eal, Canada, 2018. 3239–3250 9 Zhou Z H. Open environment machine learning. Na- tional Science Review, 2022, 9(8)"

[Page no. 2] "classiﬁcation include: • Generative methods: SSGMM. • Semi-supervised SVMs: TSVM, LapSVM. • Graph-based methods: Label Propagation, Label Spreading. • Disagreement-based methods: Co-Training, Tri-Training. • Ensemble methods: SemiBoost, Assemble. The algorithms implemented for regression include CoReg and for clustering include Constrained K- Means and Constrained Seed K-Means. For deep SSL [8], the algorithms implemented for classiﬁcation include: • Consistency methods: Ladder Network, Π- Model, Temporal Ensembling, Mean Teacher, VAT, UDA. • Pseudo-label methods: PL, S4L. • Hybrid methods: ICT, MixMatch, ReMix- Match, FixMatch, FlexMatch. • Deep generative methods: Improved GAN, SSVAE. • Deep graph methods: SDNE, GCN, GAT. We also implemented Π-Model Reg, Mean Teacher Reg and ICT Reg for the regression task. In addition to its diverse range of algorithms, LAMDA-SSL oﬀers 45 data transformation meth- ods and 16 metrics for model evaluation. It also provides ﬂexible interfaces for component replace- ment and customization, tailored for professional users. Speciﬁcally, for deep SSL, LAMDA-SSL al- lows users to easily replace and customize modules such as Dataset, Dataloader, Sampler, Aug- mentation, Network, Optimizer and Sched- uler, without aﬀecting other modules. This em- powers users to achieve low-code implementation for customized deep SSL algorithms by lever- aging LAMDA-SSL’s DeepModelMixin compo- nent, which provides default processing functions for deep SSL. Furthermore, LAMDA-SSL is de- signed to be compatible with popular toolkits such as “scikit-learn” and “Pytorch”, inheriting their mechanisms and functions. Similar to “scikit- learn”, LAMDA-SSL supports the pipeline mech- anism and includes hyper-parameter search func- tionality. And like “Pytorch”, LAMDA-SSL takes advantage of GPU acceleration for training and supports distributed learning. Simple Interface. The APIs of LAMDA-SSL refer to “scikit-learn” and all the learners have two basic methods: ﬁt() and predict(). The only diﬀerence from the APIs of “scikit-learn” is that the ﬁt() method of LAMDA-SSL needs three data items of X, y"

[Page no. 3] "and unlabeled X to be in- put. For deep SSL algorithms, LAMDA-SSL uses DeepModelMixin component to make the APIs of deep SSL algorithms and statistical SSL algo- rithms uniﬁed. Lots of elaborate examples can be found in the online documentation and the Exam- ple module of the source code. Extensive Documentation LAMDA-SSL is open-sourced on GitHub and its detailed docu- mentation is already available at https://ygzwqzd. github.io/LAMDA-SSL/. This documentation provides a detailed overview of LAMDA-SSL from various perspectives, organized into four parts. The ﬁrst part covers the mechanism, features, and functions of LAMDA-SSL. The second part of- fers abundant examples to illustrate the usage of LAMDA-SSL in detail. The third part introduces  Lin-Han Jia, et al. Sci China Inf Sci 3 Table 1 The comparison of LAMDA-SSL with related SSL toolkits. Toolkit scikit-learn USB LAMDA-SSL # deep SSL algorithms 0 14 18 # statistical SSL algorithms 3 0 12 Types of data Tabular Image Text Image Text Audio Tabular Image Text Graph Types of task Classiﬁcation Regression Clustering Classiﬁcation Classiﬁcation Regression Clustering Hyper-parameters search ✓ × ✓ GPU acceleration × ✓ ✓ Distributed learning × ✓ ✓ Documentation ✓ ✓ ✓ all the implemented SSL algorithms in LAMDA- SSL, facilitating users in understanding and select- ing the right algorithm. The fourth part showcases the APIs of LAMDA-SSL. This thorough docu- mentation signiﬁcantly reduces the learning diﬃ- culty for users to familiarize themselves with the LAMDA-SSL toolkit and SSL algorithms. We also evaluate LAMDA-SSL according to sev- eral quality standards of open source software. • Availability. LAMDA-SSL depends only on standard open-source toolkits and is usable un- der many operating systems such as Linux, Ma- cOSX, and Windows. The toolkit can be acquired via Pypi easily using “pip install LAMDA-SSL”. The source code of LAMDA-SSL is available at https://github.com/YGZWQZD/LAMDA-SSL. •"

[Page no. 2] "module, LAMDA-SSL facilitates data man- agement and transformation for 4 types of data, such as tabular, image, text, and graph. In the model module, LAMDA-SSL supports model ap- plication and deployment for 3 tasks such as clas- siﬁcation, regression, and clustering. LAMDA- SSL is designed to be user-friendly, providing sim- ple interfaces and well-tuned default parameters for entry-level users, while also oﬀering ﬂexibility for component replacement and customization for professional users. We conducted a comparison between LAMDA- SSL, the SSL module of “scikit-learn,” and “USB” (Table 1). To the best of our knowledge, LAMDA- SSL is the ﬁrst SSL toolkit that seamlessly in- tegrates statistical SSL and deep SSL algorithms within a uniﬁed framework. LAMDA-SSL demon- strates superiority in both statistical SSL and deep SSL domains. In the realm of statistical SSL, LAMDA-SSL is more suitable for SSL applications compared to “scikit-learn.” In the realm of deep SSL, LAMDA-SSL outperforms “USB” in various aspects, including the number of algorithms, data types, task types, and functionality. The key advantages of LAMDA-SSL include comprehensive functionality, simple interfaces, and extensive documentation. Comprehensive Functionality. At present, LAMDA-SSL has implemented 30 SSL algorithms, including 12 statistical SSL algorithms and 18 deep SSL algorithms. * Corresponding author (email: <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>, <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>) arXiv:2208.04610v2 [cs.LG] 22 May 2023  Lin-Han Jia, et al. Sci China Inf Sci 2 Data Management Data Transformation Model Application Model Deployment Data Module Model Module Statistical SSL Algorithms Deep SSL Algorithms Data Processing Data Augmentation Labeled Dataset Unlabeled Dataset Model Evaluation Hyperparameters Search Train Dataset Labeled Dataset Valid Dataset Labeled Dataset Test Dataset Dataset for SSL Dataloader for Deep SSL Tabular Image Text Graph Types of Data Types of Tasks Classification Regression Clustering Figure 1 An overview of the design, functions and contents of LAMDA-SSL For statistical SSL [7], the algorithms imple- mented for"

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