{"id":29446,"url":"https://github.com/zbchern/awesome-machine-learning-reliability","name":"awesome-machine-learning-reliability","description":"A curated list of awesome resources regarding machine learning reliability.","projects_count":79,"last_synced_at":"2026-07-28T16:00:21.132Z","repository":{"id":101312927,"uuid":"157802551","full_name":"zbchern/awesome-machine-learning-reliability","owner":"zbchern","description":"A curated list of awesome resources regarding machine learning reliability.","archived":false,"fork":false,"pushed_at":"2021-04-26T08:54:20.000Z","size":1248,"stargazers_count":32,"open_issues_count":0,"forks_count":6,"subscribers_count":2,"default_branch":"master","last_synced_at":"2026-07-09T17:05:34.504Z","etag":null,"topics":["adversarial-examples","adversarial-machine-learning","machine-learning-testing","machine-leraning-reliability"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/zbchern.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2018-11-16T02:40:47.000Z","updated_at":"2026-05-21T20:03:40.000Z","dependencies_parsed_at":"2024-01-13T14:47:37.621Z","dependency_job_id":"1e592048-d061-4e43-948a-ab73e2d34991","html_url":"https://github.com/zbchern/awesome-machine-learning-reliability","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/zbchern/awesome-machine-learning-reliability","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zbchern%2Fawesome-machine-learning-reliability","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zbchern%2Fawesome-machine-learning-reliability/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zbchern%2Fawesome-machine-learning-reliability/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zbchern%2Fawesome-machine-learning-reliability/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zbchern","download_url":"https://codeload.github.com/zbchern/awesome-machine-learning-reliability/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zbchern%2Fawesome-machine-learning-reliability/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35999100,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-07-20T02:08:10.276Z","status":"online","status_checked_at":"2026-07-28T02:00:06.341Z","response_time":109,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-13T12:57:57.880Z","updated_at":"2026-07-28T16:00:21.132Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Conferences","Papers","Adversarial NLP and Speech","Survey","Blogs","Machine Learning Testing","Provable and Verifiable AI Robustness","Competitions","Empirical Study","Other Applications","Other Resources"],"sub_categories":["Machine Learning","Attack","Defense","Natural Language Processing","Security"],"readme":"# Awesome Machine Learning Reliability [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)\n![Awesome Machine Learning On Source Code](img/adversarial_example.png)\n\u003e ###### \u003cp align=\"right\"\u003e *Figure from \"[Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572)\" by Goodfellow et al. ICLR15*\u003c/p\u003e\n\nA curated list of awesome papers regarding machine learning reliability, inspired by [Awesome Machine Learning On Source Code](https://github.com/src-d/awesome-machine-learning-on-source-code) and [Awesome Adversarial Machine Learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning).\n\n## Contents\n- [Conferences](#conferences)\n- [Blogs](#blogs)\n- [Competitions](#competitions)\n- [Papers](#papers)\n    - [Adversarial Computer Vision](#adversarial-computer-vision)\n        - [Benchmarking](#benchmarking)\n        - [Attack](#attack)\n            - [White-box Attack](#white-box-attack)\n            - [Black-box Attack](#black-box-attack)\n            - [Real-world Attack](#real-world-attack)\n        - [Defense](#defense)\n            - [Adversarial Training](#adversarial-training)\n            - [Manifold Projections](#manifold-projections)\n            - [Adversarial Detection](#adversarial-detection)\n            - [Model Compression](#model-compression)\n            - [Manifold Projections](#manifold-projections)\n    - [Adversarial NLP and Speech](#adversarial-nlp-and-speech)\n    - [Provable and Verifiable AI Robustness](#provable-and-verifiable-ai-robustness)\n    - [Machine Learning Testing](#machine-learning-testing)\n    - [Survey](#survey)\n    - [Empirical Study](#empirical-study)\n    - [Other Applications](#other-applications)\n    - [Other Resources](#other-resources)\n- [License](#license)\n\n\n## Conferences\n### Security\n* [ACM Conference on Computer and Communications Security (CCS)](https://www.sigsac.org/ccs/CCS2018/papers/)\n* [IEEE Symposium on Security and Privacy (S\u0026P)](https://www.ieee-security.org/TC/SP2018/)\n* [Usenix Security Symposium (Usenix Security)](https://www.usenix.org/conference/usenixsecurity18)\n* [The Network and Distributed System Security Symposium (NDSS)](https://www.ndss-symposium.org/)\n\n### Machine Learning\n* [International Conference on Learning Representations (ICLR)](https://www.iclr.cc/)\n* [Annual Conference on Neural Information Processing Systems (NeurIPS)](https://neurips.cc/)\n* [International Conference on Machine Learning (ICML)](https://icml.cc/)\n\n### Natural Language Processing\n* [Conference on Empirical Methods in Natural Language Processing (EMNLP)](http://emnlp2018.org/)\n* [Annual Meeting of the Association for Computational Linguistics (ACL)](http://www.acl2019.org/EN/index.xhtml)\n\n###### *[Conference Deadlines](https://zbchern.github.io/conferences/)*\n\n## Blogs\n* [Cleverhans](http://www.cleverhans.io/)\n* [Adversarial Robustness - Theory and Practice](https://adversarial-ml-tutorial.org/)\n* [Gradient Science](https://gradientscience.org/)\n* [Attacking Machine Learning with Adversarial Examples (OpenAI)](https://blog.openai.com/adversarial-example-research/)\n\n## Competitions\n* [NeurIPS Adversarial Vision Challenge](https://www.crowdai.org/challenges/nips-2018-adversarial-vision-challenge)\n\n## Papers\n### Adversarial Computer Vision\n \n### Attack\n#### White-box Attack\n* [ICLR14] [Intriguing properties of neural networks](https://arxiv.org/abs/1312.6199) - Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus.\n* [ICLR15] [Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572) - Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy.\n* [S\u0026P17] [Towards Evaluating the Robustness of Neural Networks](https://nicholas.carlini.com/papers/2017_sp_nnrobustattacks.pdf) - Nicholas Carlini and David Wagner. [[code]](https://github.com/carlini/nn_robust_attacks) [[talk]](https://www.youtube.com/watch?v=yIXNL88JBWQ)\n* [ICML18] [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://nicholas.carlini.com/papers/2018_icml_obfuscatedgradients.pdf) - Anish Athalye, Nicholas Carlini, and David Wagner. [[code]](https://github.com/anishathalye/obfuscated-gradients) [[talk]](https://nicholas.carlini.com/talks/2018_icml_obfuscatedgradients.mp4)\n* [CVPR18] [Fooling Vision and Language Models Despite Localization and Attention Mechanism](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/3295.pdf) - Xiaojun Xu, Xinyun Chen, Chang Liu, Anna Rohrbach, Trevor Darrell, and Dawn Song.\n* [IJCAI17] [Tactics of Adversarial Attack on Deep Reinforcement Learning Agents](https://arxiv.org/abs/1703.06748) - Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, and Min Sun.\n* [S\u0026P16] [The Limitations of Deep Learning in Adversarial Settings](https://arxiv.org/abs/1511.07528) - Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami.\n* [CVPR16] [DeepFool: a simple and accurate method to fool deep neural networks](https://arxiv.org/abs/1511.04599) - Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard.\n\n#### Black-box Attack\n* [Arxiv16] [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://arxiv.org/abs/1605.07277) - Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow.\n* [AISec17] [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://arxiv.org/abs/1708.03999) - Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh.\n* [Arxiv17] [Query-Efficient Black-box Adversarial Examples](https://arxiv.org/abs/1712.07113) - Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin.\n\n#### Real-world Attack\n* [Arxiv19] [Natural Adversarial Examples](https://arxiv.org/abs/1907.07174) - Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song. [[dataset]](https://github.com/hendrycks/natural-adv-examples)\n* [CVPR18] [Robust Physical-World Attacks on Deep Learning Models](https://arxiv.org/abs/1707.08945) - Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song.\n* [ICML18] [Synthesizing Robust Adversarial Examples](https://arxiv.org/abs/1707.07397) - Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok.\n* [CVPR17 Workshop] [NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles](https://arxiv.org/abs/1707.03501) - Jiajun Lu, Hussein Sibai, Evan Fabry, and David Forsyth. [[slides]](http://www.jiajunlu.com/docs/AdversarialCar.pptx)\n* [ICLR17] [Adversarial Examples in the Physical World](https://arxiv.org/abs/1607.02533) - Alexey Kurakin, Ian Goodfellow, and Samy Bengio.\n\n#### Benchmarking\n* [ICLR19] [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://openreview.net/forum?id=HJz6tiCqYm) - Dan Hendrycks and Thomas Dietterich.\n\n### Defense\n\n#### Adversarial Training\n\n* [ICLR18] [Towards Deep Learning Models Resistant to Adversarial Attacks](https://arxiv.org/abs/1706.06083) - Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. [[code (mnist)]](https://github.com/MadryLab/mnist_challenge) [[code (cifar10)]](https://github.com/MadryLab/cifar10_challenge)\n* [NeurIPS17] [Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser](https://arxiv.org/abs/1712.02976) - Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu. [[code]](https://github.com/lfz/Guided-Denoise)\n* [Arxiv18] [Adversarial Logit Pairing](https://arxiv.org/abs/1803.06373) - Harini Kannan, Alexey Kurakin, and Ian Goodfellow. [[code]](https://github.com/tensorflow/models/tree/master/research/adversarial_logit_pairing)\n* [ICLR18] [Generating Natural Adversarial Examples](https://arxiv.org/abs/1710.11342) - Zhengli Zhao, Dheeru Dua, and Sameer Singh.\n\n#### Adversarial Detection\n\n* [AISec17] [Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods](https://arxiv.org/abs/1705.07263) - Nicholas Carlini and David Wagner.\n* [NDSS18] [Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks](https://arxiv.org/abs/1704.01155) - Weilin Xu, David Evans, and Yanjun Qi.\n* [NeurIPS18] [Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples](https://arxiv.org/abs/1810.11580) - Guanhong Tao, Shiqing Ma, Yingqi Liu, and Xiangyu Zhang.\n* [NDSS19] [NIC: Detecting Adversarial Samples with Neural Network Invariant Checking](https://www.ndss-symposium.org/wp-content/uploads/2019/02/ndss2019_03A-4_Ma_paper.pdf) - Shiqing Ma, Yingqi Liu, Guanhong Tao, Wen-Chuan Lee, and Xiangyu Zhang.\n\n#### Model Compression\n\n* [S\u0026P16] [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://arxiv.org/abs/1511.04508) - Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami.\n* [ICLR18] [Attacking Binarized Neural Networks](https://arxiv.org/abs/1711.00449) - Angus Galloway, Graham W. Taylor, and Medhat Moussa.\n* [ICLR19] [Defensive Quantization: When Efficiency Meets Robustness](https://openreview.net/forum?id=ryetZ20ctX) - Ji Lin, Chuang Gan, and Song Han.\n\n#### Manifold Projections\n* [CCS17] [MagNet: A Two-Pronged Defense against Adversarial Examples](https://dl.acm.org/citation.cfm?id=3134057) - Dongyu Meng and Hao Chen.\n\n## Adversarial NLP and Speech\n* [Arxiv18] [Identifying and Controlling Important Neurons in Neural Machine Translation](https://arxiv.org/abs/1811.01157) - Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, and James Glass.\n* [Arxiv18] [Robust Neural Machine Translation with Joint Textual and Phonetic Embedding](https://arxiv.org/abs/1810.06729) - Hairong Liu, Mingbo Ma, Liang Huang, Hao Xiong, and Zhongjun He.\n* [Arxiv18] [Improving the Robustness of Speech Translation](https://arxiv.org/abs/1811.00728) - Xiang Li, Haiyang Xue, Wei Chen, Yang Liu, Yang Feng, and Qun Liu.\n* [Arxiv18] [Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples](https://arxiv.org/pdf/1803.01128.pdf) - Minhao Cheng, Jinfeng Yi, Huan Zhang, Pin-Yu Chen, and Cho-Jui Hsieh.\n* [Arxiv18] [Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data](https://arxiv.org/pdf/1805.12316.pdf) - Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, and Michael I. Jordan.\n* [ICLR18] [Synthetic and Natural Noise Both Break Neural Machine Translation](https://arxiv.org/abs/1711.02173) - Yonatan Belinkov and Yonatan Bisk.\n* [ACL18] [Towards Robust Neural Machine Translation](http://aclweb.org/anthology/P18-1163) - Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, and Yang Liu.\n* [ACL18] [Did the Model Understand the Question?](https://arxiv.org/abs/1805.054923) - Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, and Kedar Dhamdhere.\n* [ACL18] [Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions [Student Research Workshop]](http://aclweb.org/anthology/P18-3018) - Eric Wallace and Jordan Boyd-Graber.\n* [EMNLP18] [Generating natural language adversarial examples](https://arxiv.org/abs/1804.07998) - Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang.\n* [NAACL18] [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](http://aclweb.org/anthology/N18-1170) - Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer.\n* [COLING18] [On Adversarial Examples for Character-Level Neural Machine Translation](http://aclweb.org/anthology/C18-1055) - Javid Ebrahimi, Daniel Lowd, and Dejing Dou.\n* [ICLR17] [Adversarial Training Methods for Semi-Supervised Text Classification](https://arxiv.org/abs/1605.07725) - Takeru Miyato, Andrew M. Dai, and Ian Goodfellow.\n* [EMNLP17] [Adversarial Examples for Evaluating Reading Comprehension Systems](https://arxiv.org/abs/1707.07328) - Robin Jia and Percy Liang.\n* [MILCOM16] [Crafting Adversarial Input Sequences for Recurrent Neural Networks](https://arxiv.org/abs/1604.08275) - Nicolas Papernot, Patrick McDaniel, Ananthram Swami, and Richard Harang.\n* [CSAW16] [Hidden Voice Commands](https://nicholas.carlini.com/papers/2016_usenix_hiddenvoicecommands.pdf) - Nicholas Carlini, Pratyush Mishra, Tavish Vaidya, Yuankai Zhang, Micah Sherr, Clay Shields, David Wagner, and Wenchao Zhou. [[talk]](https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/carlini)\n\n## Provable and Verifiable AI Robustness\n* [ICML18] [Differentiable Abstract Interpretation for Provably Robust Neural Networks](http://proceedings.mlr.press/v80/mirman18b/mirman18b.pdf) - Matthew Mirman, Timon Gehr, and Martin Vechev.\n* [ICML18] [Provable defenses against adversarial examples via the convex outer adversarial polytope](https://arxiv.org/abs/1711.00851) - Eric Wong and J. Zico Kolter. [[code]](https://github.com/locuslab/convex_adversarial)\n* [ICLR18] [Certified Defenses against Adversarial Examples](https://arxiv.org/abs/1801.09344) - Aditi Raghunathan, Jacob Steinhardt, and Percy Liang.\n* [Arxiv18] [On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models](https://arxiv.org/abs/1810.12715) - Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli.\n* [Arxiv18] [Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability](https://arxiv.org/abs/1809.03008) - Kai Y. Xiao, Vincent Tjeng, Nur Muhammad Shafiullah, and Aleksander Madry.\n\n\n## Machine Learning Testing\n* [Arxiv19] [Machine Learning Testing: Survey, Landscapes and Horizons](https://arxiv.org/abs/1906.10742) - Jie M. Zhang, Mark Harman, Lei Ma, and Yang Liu.\n* [FSE18] [MODE: Automated Neural Network Model Debugging via State Differential Analysis and Input Selection](https://www.cs.purdue.edu/homes/ma229/papers/FSE18.pdf) - Shiqing Ma, Yingqi Liu, Wen-Chuan Lee, Xiangyu Zhang, Ananth Grama.\n* [Arxiv18] [Testing Untestable Neural Machine Translation: An Industrial Case](https://arxiv.org/abs/1807.02340) - Wujie Zheng, Wenyu Wang, Dian Liu, Changrong Zhang, Qinsong Zeng, Yuetang Deng, Wei Yang, Pinjia He, Tao Xie.\n* [ASE18] [DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems](https://arxiv.org/abs/1803.07519) - Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, Jianjun Zhao, Yadong Wang.\n* [ICSE18] [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://arxiv.org/abs/1708.08559) - Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray.\n* [SOSP17] [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://arxiv.org/abs/1705.06640) - Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana.\n* [KDD16] [\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier](https://arxiv.org/abs/1602.04938) - Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. [[code]](https://github.com/marcotcr/lime), [[slides]](https://drive.google.com/file/d/0ByblrZgHugfYZ0ZCSWNPWFNONEU/view), [[video]](https://www.youtube.com/watch?v=hUnRCxnydCc)\n\n## Survey\n* [Arxiv17] [Adversarial Examples: Attacks and Defenses for Deep Learning](https://arxiv.org/abs/1712.07107) - Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li.\n* [Arxiv18] [Adversarial Examples - A Complete Characterisation of the Phenomenon](https://arxiv.org/abs/1810.01185) - Alexandru Constantin Serban and Erik Poll.\n* [Arxiv18] [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://arxiv.org/abs/1801.00553) - Naveed Akhtar and Ajmal Mian.\n* [Arxiv19] [Adversarial Examples: Opportunities and Challenges](https://arxiv.org/abs/1809.04790) - Jiliang Zhang and Chen Li.\n\n## Empirical Study\n* [ECCV18] [Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models](https://arxiv.org/abs/1808.01688) - Dong Su, Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, Yupeng Gao. [[code]](https://github.com/huanzhang12/Adversarial_Survey)\n\n## Other Applications\n* [Arxiv17] [Black-Box Attacks against RNN based Malware Detection Algorithms](https://arxiv.org/abs/1705.08131) - Weiwei Hu, Ying Tan\n\n## Other Resources\n* [Trustworthy Machine Learning](http://trustworthymachinelearning.org/) - A suite of tools for making machine learning secure and trustworthy\n\n## License\n\u003cp xmlns:dct=\"http://purl.org/dc/terms/\" xmlns:vcard=\"http://www.w3.org/2001/vcard-rdf/3.0#\"\u003e\n  \u003ca rel=\"license\"\n     href=\"http://creativecommons.org/publicdomain/zero/1.0/\"\u003e\n    \u003cimg src=\"http://i.creativecommons.org/p/zero/1.0/88x31.png\" style=\"border-style: none;\" alt=\"CC0\" /\u003e\n  \u003c/a\u003e\n  \u003cbr /\u003e\n  To the extent possible under law,\n  \u003ca rel=\"dct:publisher\"\n     href=\"https://github.com/zbchern/awesome_machine_learning_reliability\"\u003e\n    \u003cspan property=\"dct:title\"\u003eZhuangbin Chen\u003c/span\u003e\u003c/a\u003e\n  has waived all copyright and related or neighboring rights to\n  \u003cspan property=\"dct:title\"\u003eAwesome Machine Learning Reliability\u003c/span\u003e.\nThis work is published from:\n\u003cspan property=\"vcard:Country\" datatype=\"dct:ISO3166\"\n      content=\"CN\" about=\"https://github.com/zbchern/awesome_machine_learning_reliability\"\u003e\n  China\u003c/span\u003e.\n\u003c/p\u003e\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/zbchern%2Fawesome-machine-learning-reliability/projects"}