Frontiers in Machine Learning: Big Ideas in Causality and Machine Learning
Causal relationships are stable across distribution shifts. Models based on causal knowledge have the potential to generalize to unseen domains and offer counterfactual predictions: how do outcomes change if a certain feature is changed in the real world. In recent years, machine learning methods based on causal reasoning have led to advances in out-of-domain generalization, fairness and explanation, and robustness to data selection biases. ¬ In this session, we discuss big ideas at the intersections of causal inference and machine learning towards building stable predictive models and discovering causal insights from data.
Special MSR India session
Session Lead: Amit Sharma, Microsoft
Speaker: Susan Athey, Stanford University
Talk Title: Causal Inference, Consumer Choice, and the Value of Data
Speaker: Elias Bareinboim, Columbia University
Talk Title: On the Causal Foundations of Artificial Intelligence (Explainability & Decision-Making)
Speaker: Cheng Zhang, Microsoft
Talk Title: A causal view on Robustness of Neural Networks
Q&A panel with all 3 speakers
See more on-demand sessions from Microsoft Research's Frontiers in Machine Learning 2020 virtual event: https://www.microsoft.com/en-us/research/event/frontiers-in-machine-learning-2020/
Видео Frontiers in Machine Learning: Big Ideas in Causality and Machine Learning канала Microsoft Research
Special MSR India session
Session Lead: Amit Sharma, Microsoft
Speaker: Susan Athey, Stanford University
Talk Title: Causal Inference, Consumer Choice, and the Value of Data
Speaker: Elias Bareinboim, Columbia University
Talk Title: On the Causal Foundations of Artificial Intelligence (Explainability & Decision-Making)
Speaker: Cheng Zhang, Microsoft
Talk Title: A causal view on Robustness of Neural Networks
Q&A panel with all 3 speakers
See more on-demand sessions from Microsoft Research's Frontiers in Machine Learning 2020 virtual event: https://www.microsoft.com/en-us/research/event/frontiers-in-machine-learning-2020/
Видео Frontiers in Machine Learning: Big Ideas in Causality and Machine Learning канала Microsoft Research
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Frontiers in Machine Learning: Machine Learning Reliability and RobustnessFireside Chat with David BleiMicrosoft Urban Futures Summer Workshop | Policy and Social Impact [Day 3]Reimagining Learning: Richard Culatta at TEDxBeaconStreetFrontiers in Machine Learning: Saving Lives with Interpretable MLResearch in Focus: Deep Learning Research and the Future of AIThe Speed of Light is NOT About LightArtificial intelligence & algorithms: pros & cons | DW Documentary (AI documentary)Can we make better software by using ML and AI techniques? With Chandra Madilla and Chetan BansalCausal Inference in Data Science From Prediction to Causation by Amit Sharma | DataEngConf NYC '16How Work From Home Affects Collaboration: Information Workers in a Natural Experiment During COVID19Machine Learning: Living in the Age of AI | A WIRED FilmKeynote Talk: Model Based Machine LearningApplications of Machine Learning in the Supply ChainMost Research in Deep Learning is a Total Waste of Time - Jeremy Howard | AI Podcast ClipsThe Future of Mathematics?Making data mean more through storytelling | Ben Wellington | TEDxBroadwayProfessional Stock Trading Course Lesson 1 of 10 by Adam KhooFrontiers in Machine Learning: Climate Impact of Machine Learning"I'm Fine" - Learning To Live With Depression | Jake Tyler | TEDxBrighton