Robust Machine Learning

Rachid Guerraoui is a professor of computer science at EPFL, where he leads the Distributed Computing Laboratory. He has previously worked at the Ecole des Mines de Paris, CEA Saclay, HP Labs in Palo Alto, and MIT. ACM fellow and professor of the College de France, he was awarded a Senior ERC Grant and a Google Focused Award. He has co-authored several popular books on distributed computing, including Reliable and Secure Distributed Programming, and Algorithms for Concurrent Systems.Nirupam Gupta is a computer science research associate at EPFL. He has previously worked as a postdoc in the department of computer science at Georgetown University. He has served on the program committees of the dependable and secure machine learning workshops at the IEEE DSN conference and the symposium on reliable distributed systems (SRDS), and currently serves as a reviewer for leading control systems and optimization journals, including Elsevier Automatica, IEEE TAC and IEEE CONES. He received his PhD from the University of Maryland College Park, and his bachelor’s degree from the Indian Institute of Technology Delhi.Rafael Pinot is a junior professor in the department of mathematics at Sorbonne Université, where he holds a chair on the mathematical foundation of computer and data science within the LPSM research unit. He previously worked as a computer science research associate at EPFL and received his PhD from PSL Research University. In 2018, he was awarded a JSPS summer fellowship to join Kyoto University as a visiting researcher. He also received the Dauphine Foundation’s Young Researcher Award (2020) and the Postdoctoral Research Award from EPFL’s Ecocloud Research Center (2021).

• Chapter 1. Context & Motivation.- Chapter 2. Basics of Machine Learning.- Chapter 3. Federated Machine Learning.- Chapter 4. Fundamentals of Robust Machine Learning.- Chapter 5. Optimal Robustness.- Chapter 6. Practical Robustness.