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A professional list of Tutorials and Surveys on DL, ML, DM, CV, NLP, Speech in top AI conferences and journals.
https://github.com/qingsongedu/awesome-AI-tutorials-surveys

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A professional list of Tutorials and Surveys on DL, ML, DM, CV, NLP, Speech in top AI conferences and journals.

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# Recent AI Advances: Tutorials and Surveys in Top AI Conferences and Journals



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A professionally curated list of tutorials (keynote, invited talk, etc.) and surveys of recent AI advances, including Deep Learning, Machine Learning, Data Mining, Computer Vision (CV), Natural Language Processing (NLP), Speech, etc., at the **Top AI Conferences and Journals**, which is **updated ASAP (the earliest time)** once the accepted tutorials and surveys are announced in the corresponding top AI conferences/journals. Hope this list would be helpful for researchers and engineers who are interested in various AI areas.



The top conferences including:

- Machine Learning: NeurIPS, ICML, ICLR

- Computer Vision: CVPR, ICCV, ACMMM

- NLP and Speech: ACL, EMNLP

- Speech: ICASSP, INTERSPEECH

- Artificial Intelligence: AAAI, IJCAI

- Data Mining: KDD, WWW 

- Data Management: SIGMOD, VLDB, ICDE

- Misc (selected): AISTAT, CIKM, ICDM, WSDM, SIGIR, etc.



The top journals including:

CACM, PIEEE, TPAMI, TKDE, TNNLS, TITS, TIST, TSP, TASLP, TIP, TACL, SPM, IJCV, JMLR, JAIR, CSUR, DMKD, KAIS, arXiv(selected), etc.



If you found any missed resources (paper/code) or errors, please feel free to open an issue or make a pull request.



## Table of Contents

- [AI Tutorials](#AI-Tutorials)

  * [Tutorials 2023](#Tutorials-2022)

  * [Tutorials 2022](#Tutorials-2022)

  * [Tutorials 2021](#Tutorials-2021)

  * [Tutorials 2020](#Tutorials-2020)

  * [Tutorials 201X](#Tutorials-201X)

 

- [AI Surveys](#AI-Surveys)

  * [General](#General)

  * [Transformer and Attention](#Transformer-and-Attention)

  * [Self-Supervised Learning](#Self-Supervised-Learning)

  * [Graph Neural Networks](#Graph-Neural-Networks)

  * [Federated Learning](#Federated-Learning)

  * [XAI](#XAI)

  * [AutoML](#AutoML)

  * [Deep Generative Models](#Deep-Generative-Models)

  * [N-Shot Learning](#N-Shot-Learning)

  * [Anomaly Detection and OOD](#Anomaly-Detection-and-OOD)

  * [Label-noise Learning](#Label-noise-Learning)

  * [Imbalanced-data Learning](#Imbalanced-data-Learning)

  * [Deep Reinforcement Learning](#Deep-Reinforcement-Learning)

  * [Domain Adaptation](#Domain-Adaptation)

  * [Others](#Others)



## AI Tutorials

### Tutorials 2023

* Everything You Need to Know about Transformers: Architectures, Optimization, Applications, and Interpretation, *AAAI* 2023. [\[Link\]](https://transformer-tutorial.github.io/aaai2023/)

* On Explainable AI: From Theory to Motivation, Industrial Applications, XAI Coding & Engineering Practices, *AAAI* 2023. [\[Link\]](https://xaitutorial2023.github.io/)



### Tutorials 2022

* Causality and deep learning: synergies, challenges& opportunities for research, *ICML* 2022. [\[Link TBD\]]()

* Bridging Learning and Decision Making, *ICML* 2022. [\[Link TBD\]]()

* Facilitating a smoother transition to Renewable Energy with AI (AI4Renewables), *ICLR* 2022 Social. [\[Link\]](https://www.ai4renewables.org/) [\[slides\]](https://iclr.cc/media/iclr-2022/Slides/8733_OpISyMy.pdf) 

* Optimization in ML and DL - A discussion on theory and practice, *ICLR* 2022 Social. [\[slides\]](https://iclr.cc/media/iclr-2022/Slides/8739_DhSLTHw.pdf)

* Beyond Convolutional Neural Networks, *CVPR* 2022. [\[Link\]](https://sites.google.com/view/cvpr-2022-beyond-cnn)

* Evaluating Models Beyond the Textbook: Out-of-distribution and Without Labels, *CVPR* 2022. [\[Link\]](https://sites.google.com/view/evalmodel/home)

* Sparsity Learning in Neural Networks and Robust Statistical Analysis, *CVPR* 2022. [\[Link\]](https://sparse-learning.github.io/)

* Denoising Diffusion-based Generative Modeling: Foundations and Applications, *CVPR* 2022. [\[Link\]](https://cvpr2022-tutorial-diffusion-models.github.io/)

* On Explainable AI: From Theory to Motivation, Industrial Applications, XAI Coding & Engineering Practices, *AAAI* 2022. [\[Link\]](https://xaitutorial2022.github.io/)

* Deep Learning on Graphs for Natural Language Processing, *AAAI* 2022. [\[Link\]](https://dlg4nlp.github.io/tutorial_Deep%20Learning%20on%20Graphs%20for%20Natural%20Language%20Processing%20AAAI%202022.html)

* Bayesian Optimization: From Foundations to Advanced Topics, *AAAI* 2022. [\[Link\]](https://bayesopt-tutorial.github.io/syllabus/)



### Tutorials 2021

* The Art of Gaussian Processes: Classic and Contemporary, *NeurIPS* 2021. [\[Link\]](https://github.com/GAMES-UChile/The_Art_of_Gaussian_Processes) [\[slides\]](https://nips.cc/media/neurips-2021/Slides/21890_AZNeRaA.pdf)

* Self-Supervised Learning: Self-Prediction and Contrastive Learning, , *NeurIPS* 2021. [\[slides\]](https://nips.cc/media/neurips-2021/Slides/21895.pdf) [\[vedio\]](https://www.youtube.com/watch?v=7l6fttRJzeU)

* Self-Attention for Computer Vision, *ICML* 2021. [\[Link\]](https://icml.cc/virtual/2021/tutorial/10842)

* Continual Learning with Deep Architectures, *ICML* 2021. [\[Link\]](https://icml.cc/virtual/2021/tutorial/10833)

* Responsible AI in Industry: Practical Challenges and Lessons Learned, *ICML* 2021. [\[Link\]](https://icml.cc/virtual/2021/tutorial/10841)

* Self-Supervision for Learning from the Bottom Up, *ICLR* 2021 Talk. [\[Link\]](https://iclr.cc/virtual/2021/invited-talk/3720)

* Geometric Deep Learning: the Erlangen Programme of ML, *ICLR* 2021 Talk. [\[Link\]](https://iclr.cc/virtual/2021/invited-talk/3717)

* Moving beyond the fairness rhetoric in machine learning, *ICLR* 2021 Talk. [\[Link\]](https://iclr.cc/virtual/2021/invited-talk/3718)

* Is My Dataset Biased, *ICLR* 2021 Talk. [\[Link\]](https://iclr.cc/virtual/2021/invited-talk/3721)

* Interpretability with skeptical and user-centric mind, *ICLR* 2021 Talk. [\[Link\]](https://iclr.cc/ExpoConferences/2021/talk%20panel/4381)

* AutoML: A Perspective where Industry Meets Academy, *KDD* 2021. [\[Link\]](https://joneswong.github.io/KDD21AutoMLTutorial/)

* Automated Machine Learning on Graph, *KDD* 2021. [\[Link\]](http://mn.cs.tsinghua.edu.cn/xinwang/kdd2021Tutorial.htm)

* Toward Explainable Deep Anomaly Detection, *KDD* 2021. [\[Link\]](https://sites.google.com/site/gspangsite/kdd21_tutorial)

* Fairness and Explanation in Clustering and Outlier Detection, *KDD* 2021. [\[Link\]](https://www.cs.ucdavis.edu/~davidson/KDD2021/overview.htm)

* Real-time Event Detection for Emergency Response, *KDD* 2021. [\[Link\]](https://www.cs.rochester.edu/~tetreaul/kdd2021-tutorial.html)

* Machine Learning Explainability and Robustness: Connected at the Hip, *KDD* 2021. [\[Link\]](https://sites.google.com/andrew.cmu.edu/kdd-2021-tutorial-expl-robust/)

* Machine Learning Robustness, Fairness, and their Convergence, *KDD* 2021. [\[Link\]](https://kdd21tutorial-robust-fair-learning.github.io/)

* Counterfactual Explanations in Explainable AI: A Tutorial, *KDD* 2021. [\[Link\]](https://sites.google.com/view/kdd-2021-counterfactual)

* Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber, *KDD* 2021. [\[Link\]](https://causal-machine-learning.github.io/kdd2021-tutorial/)

* Normalization Techniques in Deep Learning: Methods, Analyses, and Applications, *CVPR* 2021. [\[Link\]](https://normalization-dnn.github.io/)

* Normalizing Flows and Invertible Neural Networks in Computer Vision, *CVPR* 2021. [\[Link\]](https://mbrubake.github.io/cvpr2021-nf_in_cv-tutorial/)

* Theory and Application of Energy-Based Generative Models, *CVPR* 2021. [\[Link\]](https://energy-based-models.github.io/)

* Adversarial Machine Learning in Computer Vision, *CVPR* 2021. [\[Link\]](https://advmlincv.github.io/cvpr21-tutorial/)

* Practical Adversarial Robustness in Deep Learning: Problems and Solutions, *CVPR* 2021. [\[Link\]](https://sites.google.com/view/par-2021)

* Leave those nets alone: advances in self-supervised learning, *CVPR* 2021. [\[Link\]](https://gidariss.github.io/self-supervised-learning-cvpr2021/)

* Interpretable Machine Learning for Computer Vision, *CVPR* 2021. [\[Link\]](https://interpretablevision.github.io/)

* Learning Representations via Graph-structured Networks, *CVPR* 2021. [\[Link\]](https://xiaolonw.github.io/graphnnv3/)

* Reviewing the Review Process, *ICCV* 2021. [\[Link\]](https://sites.google.com/view/reviewing-the-review-process/)

* Meta Learning and Its Applications to Natural Language Processing, *ACL* 2021. [\[Link\]](https://ai.ntu.edu.tw/mlss2021/wp-content/uploads/2021/08/0812-Thang-Vu-Shang-Wen-Li.pdf)

* Deep generative modeling of sequential data with dynamical variational autoencoders, *ICASSP* 2021. [\[Link\]](https://dynamicalvae.github.io/)



### Tutorials 2020

* Deep Implicit Layers - Neural ODEs, Deep Equilibirum Models, and Beyond, *NeurIPS* 2020. [\[Link\]](http://implicit-layers-tutorial.org/)

* Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning, *NeurIPS* 2020. [\[Link\]](https://nips.cc/virtual/2020/public/tutorial_0f190e6e164eafe66f011073b4486975.html)

* Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities, *NeurIPS* 2020. [\[Link\]](https://nips.cc/virtual/2020/public/tutorial_59e711d152de7bec7304a8c2ecaf9f0f.html)

* Advances in Approximate Inference, *NeurIPS* 2020. [\[Link\]](https://nips.cc/virtual/2020/public/tutorial_b5a5e2e8958e765c2822d5cf7c60df7d.html)

* There and Back Again: A Tale of Slopes and Expectations, *NeurIPS* 2020. [\[Link\]](https://nips.cc/virtual/2020/public/tutorial_880c6de112a048b0fc4ddb0a8b513e17.html)

* Federated Learning and Analytics: Industry Meets Academia, *NeurIPS* 2020. [\[Link\]](https://nips.cc/virtual/2020/public/tutorial_f31c147335274c56d801f833d3c26a70.html)

* Machine Learning with Signal Processing, *ICML* 2020. [\[Link\]](https://users.aalto.fi/~asolin/teaching/#tutorials)

* Bayesian Deep Learning and a Probabilistic Perspective of Model Construction, *ICML* 2020. [\[slides\]](https://cims.nyu.edu/~andrewgw/bayesdlicml2020.pdf) [\[video\]](https://www.youtube.com/watch?v=E1qhGw8QxqY)

* Representation Learning Without Labels, *ICML* 2020. [\[slides\]](https://danilorezende.com/wp-content/uploads/2020/07/ICML-2020-Tutorial-Slides.pdf) [\[video\]](https://www.youtube.com/watch?v=_9rGTWfpo_4)

* Recent Advances in High-Dimensional Robust Statistics, *ICML* 2020. [\[Link\]](http://www.iliasdiakonikolas.org/icml-robust-tutorial.html)

* Submodular Optimization: From Discrete to Continuous and Back, *ICML* 2020. [\[Link\]](http://iid.yale.edu/icml/icml-20.md/)

* Deep Learning for Anomaly Detection, in *KDD* 2020. [\[Link\]](https://sites.google.com/view/kdd2020deepeye/home) [\[video\]](https://www.youtube.com/watch?v=Fn0qDbKL3UI&list=PLn0nrSd4xjja7AD3aY9Jxmr820gx59EQC&index=67)

* Learning with Small Data, in *KDD* 2020. [\[Link\]](https://sites.psu.edu/kdd20tutorial/2020/06/01/kdd-2020-tutorial-learning-with-small-data/)



### Tutorials 201X

* Adversarial Machine Learning, ICLR 2019 Keynote. [\[slides\]](https://www.iangoodfellow.com/slides/2019-05-07.pdf)

* Introduction to GANs, CVPR 2018. [\[slides\]](https://www.iangoodfellow.com/slides/2018-06-22-gan_tutorial.pdf)

* Which Anomaly Detector should I use,	ICDM 2018. [\[slides\]](https://federation.edu.au/__data/assets/pdf_file/0011/443666/ICDM2018-Tutorial-Final.pdf)



 

## AI Surveys



### General

* Deep learning, in *Nature* 2015. [\[paper\]](https://www.nature.com/articles/nature14539)

* Deep learning in neural networks: An overview, in *Neural networks* 2015. [\[paper\]](https://www.sciencedirect.com/science/article/abs/pii/S0893608014002135)



### Transformer and Attention

* A survey on visual transformer, in *IEEE TPAMI* 2022. [\[paper\]](https://arxiv.org/abs/2012.12556)

* Transformers in vision: A survey, in *ACM Computing Surveys* 2021. [\[paper\]](https://arxiv.org/abs/2101.01169)

* Efficient transformers: A survey, in *arXiv* 2022. [\[paper\]](https://arxiv.org/abs/2009.06732)

* A General Survey on Attention Mechanisms in Deep Learning, in *IEEE TKDE* 2022. [\[paper\]](https://personal.eur.nl/frasincar/papers/TKDE2022/tkde2022.pdf)

* Attention, please! A survey of neural attention models in deep learning, in *Artificial Intelligence Review* 2022. [\[paper\]](https://link.springer.com/article/10.1007/s10462-022-10148-x)

* An attentive survey of attention models, in *ACM TIST* 2021. [\[paper\]](https://arxiv.org/abs/1904.02874)

* Attention in natural language processing, in *IEEE TNNLS* 2020. [\[paper\]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9194070)



### Self-Supervised Learning

* Self-supervised visual feature learning with deep neural networks: A survey, in *IEEE TPAMI* 2020. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/9086055)

* Self-supervised Learning: Generative or Contrastive, TKDE'21. [\[paper\]](https://arxiv.org/abs/2006.08218)

* Self-Supervised Representation Learning: Introduction, advances, and challenges, SPM'22. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/9770283/)



### Graph Neural Networks 

* A comprehensive survey on graph neural networks, TNNLS'20. [\[paper\]](https://arxiv.org/abs/1901.00596)

* Deep learning on graphs: A survey, TKDE'20. [\[paper\]](https://arxiv.org/abs/1812.04202)

* Graph neural networks: A review of methods and applications, AI Open'20. [\[paper\]](https://www.sciencedirect.com/science/article/pii/S2666651021000012)

* Self-Supervised Learning of Graph Neural Networks: A Unified Review, TPAMI'22. [\[paper\]](https://arxiv.org/abs/2102.10757)

* Graph Self-Supervised Learning: A Survey, TKDE'22. [\[paper\]](https://arxiv.org/abs/2103.00111)

* Self-supervised learning on graphs: Contrastive, generative, or predictive, TKDE'21. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/9632431)



### Federated Learning

* Federated machine learning: Concept and applications, TIST'19. [\[paper\]](https://arxiv.org/abs/1902.04885)

* Advances and open problems in federated learning, now'21. [\[paper\]](https://www.nowpublishers.com/article/Details/MAL-083)

* A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection, TKDE'21. [\[paper\]](https://arxiv.org/abs/1907.09693)

* A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions, CSUR'21. [\[paper\]](https://dl.acm.org/doi/pdf/10.1145/3460427)

* A survey on federated learning, Knowledge-Based Systems'21. [\[paper\]](https://www.sciencedirect.com/science/article/abs/pii/S0950705121000381)

* A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond, JIOT'20. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/9220780)

* Federated learning: Challenges, methods, and future directions, SPM'20. [\[paper\]](https://ieeexplore.ieee.org/document/9084352)



### XAI

* Explaining deep neural networks and beyond: A review of methods and applications, PIEEE'21. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/9369420)

* Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, Information Fusion'20. [\[paper\]](https://www.sciencedirect.com/science/article/abs/pii/S1566253519308103)

* A survey on the explainability of supervised machine learning, JAIR'21. [\[paper\]](https://www.jair.org/index.php/jair/article/download/12228/26647)

* Techniques for Interpretable Machine Learning, CACM'19. [\[paper\]](https://arxiv.org/abs/1808.00033) 



### AutoML

* AutoML: A survey of the state-of-the-art, Knowledge-Based Systems'21. [\[paper\]](https://www.sciencedirect.com/science/article/abs/pii/S0950705120307516)  

* Benchmark and survey of automated machine learning frameworks, JAIR'21. [\[paper\]](https://www.jair.org/index.php/jair/article/view/11854)

* AutoML to Date and Beyond: Challenges and Opportunities, CSUR'22. [\[paper\]](https://dl.acm.org/doi/abs/10.1145/3470918)

* Automated Machine Learning on Graphs: A Survey, IJCAI'21. [\[paper\]](https://www.ijcai.org/proceedings/2021/637)

* Others: awesome-automl-papers. [\[repo\]](https://github.com/hibayesian/awesome-automl-papers)



### Deep Generative Models 

* NIPS 2016 Tutorial: Generative Adversarial Networks, arXiv'17. [\[paper\]](https://arxiv.org/pdf/1701.00160.pdf)

* Generative adversarial networks: An overview, SPM'18. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/8253599)

* A review on generative adversarial networks: Algorithms, theory, and applications, TKDE'21. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/9625798)

* A survey on generative adversarial networks: Variants, applications, and training, CSUR'22. [\[paper\]](https://dl.acm.org/doi/abs/10.1145/3463475)

* An Introduction to Variational Autoencoders, now'19. [\[paper\]](https://arxiv.org/abs/1906.02691)

* Dynamical Variational Autoencoders: A Comprehensive Review, now'21. [\[paper\]](https://arxiv.org/abs/2008.12595)

* Advances in variational inference, TPAMI'19. [\[paper\]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8588399)

* Normalizing flows: An introduction and review of current methods, TPAMI'20. [\[paper\]](https://arxiv.org/abs/1908.09257)

* Normalizing Flows for Probabilistic Modeling and Inference, JMLR'21. [\[paper\]](https://www.jmlr.org/papers/volume22/19-1028/19-1028.pdf)



### N-Shot Learning

* A survey of zero-shot learning: Settings, methods, and applications, in *TIST* 2019. [\[paper\]](https://dl.acm.org/doi/abs/10.1145/3293318)

* Generalizing from a few examples: A survey on few-shot learning, in *CSUR* 2020. [\[paper\]](https://arxiv.org/abs/1904.05046) [\[Link\]](https://github.com/tata1661/FSL-Mate) 

* What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured Data, in *TIST* 2022. [\[paper\]](https://dl.acm.org/doi/abs/10.1145/3510030)

* A Survey of Few-Shot Learning: An Effective Method for Intrusion Detection, in *SCN* 2022. [\[paper\]](https://www.hindawi.com/journals/scn/2021/4259629/)

* Few-Shot Learning on Graphs: A Survey, in *arXiv* 2022. [\[paper\]](https://arxiv.org/abs/2203.09308)

* A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities, in *arXiv* 2022. [\[paper\]](https://arxiv.org/abs/2205.06743)



### Anomaly Detection and OOD

* A unifying review of deep and shallow anomaly detection, PIEEE'21. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/9347460) 

* Deep learning for anomaly detection: A review, CSUR'20. [\[paper\]](https://arxiv.org/abs/2007.02500) 

* A Comprehensive Survey on Graph Anomaly Detection with Deep Learning, TKDE'21. [\[paper\]](https://arxiv.org/abs/2106.07178)  

* Graph based anomaly detection and description: a survey, DMKD'15. [\[paper\]](https://arxiv.org/abs/1404.4679)

* Anomaly detection in dynamic networks: a survey, WICS'15. [\[paper\]](https://wires.onlinelibrary.wiley.com/doi/pdf/10.1002/wics.1347) 

* Anomaly detection: A survey, CSUR'09. [\[paper\]](https://www.profsandhu.com/cs5323_s17/a15-chandola.pdf) 

* A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges, arXiv'21. [\[paper\]](https://arxiv.org/abs/2110.14051)

* Self-Supervised Anomaly Detection: A Survey and Outlook, arXiv'21. [\[paper\]](https://arxiv.org/abs/2205.05173)



###  Label-noise Learning

* A Survey of Label-noise Representation Learning: Past, Present and Future, arXiv'21. [\[paper\]](https://arxiv.org/abs/2011.04406) [\[link\]](https://github.com/bhanML/label-noise-papers)

* Learning from Noisy Labels with Deep Neural Networks: A Survey, TNNLS'22. [\[paper\]](https://arxiv.org/abs/2007.08199) [\[link\]](https://github.com/songhwanjun/Awesome-Noisy-Labels)

* Classification in the presence of label noise: a survey, TNNLS'13. [\[paper\]](https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Frenay%20-%20Classification%20in%20the%20Presence%20of%20Label%20Noise%20-%202014.pdf)



### Imbalanced-data Learning

* Learning from imbalanced data, TKDE'09. [\[paper\]](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.728.3478&rep=rep1&type=pdf)

* A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions, CSUR'20. [\[paper\]](https://dl.acm.org/doi/abs/10.1145/3343440)

* Imbalance problems in object detection: A review, TPAMI'20. [\[paper\]](https://arxiv.org/abs/1909.00169)



### Deep Reinforcement Learning



### Domain Adaptation

* Generalizing to unseen domains: A survey on domain generalization, TKDE'22. [\[paper\]](https://arxiv.org/abs/2103.03097)

* A survey of unsupervised deep domain adaptation, TIST'21. [\[paper\]](https://dl.acm.org/doi/pdf/10.1145/3400066)

* A review of domain adaptation without target labels, TPAMI'19. [\[paper\]](https://arxiv.org/abs/1901.05335)



### Others

* A continual learning survey: Defying forgetting in classification tasks, in *IEEE TPAMI* 2021. [\[paper\]](https://arxiv.org/abs/1909.08383)

* Learning under concept drift: A review, in *IEEE TKDE* 2018. [\[paper\]](https://arxiv.org/abs/2004.05785)

* Learning in nonstationary environments: A survey, MCI'15. [\[paper\]](https://arxiv.org/abs/2004.05785)

* Online learning: A comprehensive survey, Neucom'21. [\[paper\]](https://www.sciencedirect.com/science/article/abs/pii/S0925231221006706)

* A survey on transfer learning, TKDE'09. [\[paper\]](http://home.cse.ust.hk/~qyang/Docs/2009/tkde_transfer_learning.pdf)

* A Comprehensive Survey on Transfer Learning, PIEEE'21. [\[paper\]](https://arxiv.org/abs/1911.02685)

* A survey on multi-task learning, TKDE'21. [\[paper\]](https://arxiv.org/abs/1707.08114)

* Bayesian statistics and modelling, Nature Reviews Methods Primers'21. [\[paper\]](https://osf.io/wdtmc/download)

* Meta-learning in neural networks: A survey, arXiv'21. [\[paper\]](https://arxiv.org/abs/2004.05439)

* Deep Long-Tailed Learning: A Survey, arXiv'21. [\[paper\]](https://arxiv.org/abs/2110.04596) [\[link\]](https://github.com/Vanint/Awesome-LongTailed-Learning)   

* Learning to optimize: A primer and a benchmark, arXiv'21. [\[paper\]](https://arxiv.org/abs/2103.12828)