Frontiers in Machine Learning: Big Ideas in Causality and Machine Learning

Channel:
Microsoft Research
Subscribers:
351,000
Published on ● Video Link: https://www.youtube.com/watch?v=wYVptiGkmQM


Duration: 1:35:59
14,001 views
318

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/



Other Videos By Microsoft Research


2020-07-30Towards a Practical Virtual Office for Mobile Knowledge Workers
2020-07-30Challenges and Gratitude of Software Developers During COVID-19 Working From Home
2020-07-30Remote Work and Well-Being
2020-07-30Early Indicators of the Effect of the Global Shift to Remote Work on People with Disabilities
2020-07-29Hope Speech and Help Speech: Surfacing Positivity Amidst Hate
2020-07-28Frontiers in Machine Learning: Security and Machine Learning
2020-07-28Frontiers in Machine Learning: Climate Impact of Machine Learning
2020-07-28Frontiers in ML: Learning from Limited Labeled Data: Challenges and Opportunities for NLP
2020-07-28Frontiers in Machine Learning: Saving Lives with Interpretable ML
2020-07-28Frontiers in Machine Learning: Machine Learning Reliability and Robustness
2020-07-28Frontiers in Machine Learning: Big Ideas in Causality and Machine Learning
2020-07-28Frontiers in Machine Learning: Beyond Fairness: Pushing ML Frontiers for Social Equity [Panel]
2020-07-28Frontiers in Machine Learning: Accelerating Machine Learning with Confidential Computing
2020-07-28Frontiers in Machine Learning: Machine Learning Conversations
2020-07-28Frontiers in Machine Learning: Fireside Chat
2020-07-23Optics for the Cloud PhD Event 2020 - Day 2
2020-07-23Optics for the Cloud PhD Event 2020 - Day 1
2020-07-23Abstraction in Reinforcement Learning
2020-07-21Blue-Pencil: modeless program synthesis
2020-07-20ICSE20 Gulwani MIP Speech
2020-07-20Fireside Chat with Chris Bishop and Peter Lee


Tags:
Frontiers in Machine Learning 2020
Microsoft Research
causality and machine learning
Amit Sharma
Susan Athey
Elias Bareinboim
Cheng Zhang
AI
neural networks