Tutorial: Safe and Reliable Machine Learning
This document serves as a brief overview of the "Safe and Reliable Machine Learning" tutorial given at the 2019 ACM Conference on Fairness, Accountability, and Transparency (FAT* 2019). The talk slides can be found here: https://bit.ly/2Gfsukp, while a video of the talk is available here: https://youtu.be/FGLOCkC4KmE, and a complete list of references for the tutorial here: https://bit.ly/2GdLPme.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningFairnessSimilar Papers 제목 키워드 기반
Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees
This tutorial focuses on efficient methods to predictive monitoring (PM), the problem of detecting at runtime future violations of a given requirement from the current state of a system. While performing model checking a…
Conformal PredictionUncertainty QuantificationTutorial on Using Machine Learning and Deep Learning Models for Mental Illness Detection
Social media has become an important source for understanding mental health, providing researchers with a way to detect conditions like depression from user-generated posts. This tutorial provides practical guidance to a…
A tutorial to set safety stock under guaranteed-service time by dynamic programming
In this paper, we provide a tutorial to solve the problem of minimising the safety stock levels under guaranteed-service time over a supply chain (SC) using the dynamic programming (DP) algorithm proposed by Graves and W…
Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial
Research on decision support applications in healthcare, such as those related to diagnosis, prediction, treatment planning, etc., have seen enormously increased interest recently. This development is thanks to the incre…
BIG-bench Machine LearningSafe Physics-Informed Machine Learning for Dynamics and Control
This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learn…
Autonomous VehiclesDecision MakingPhysics-informed machine learningUncertainty Quantification