Research talk: Can causal learning improve the privacy of ML models?
- Shruti Tople | Microsoft Research
- Microsoft Research Summit 2021 | Causal Machine Learning
Ensuring privacy of data used to train machine learning models is important for safe and responsible deployment of these models. At the same time, models are required to generalize across different data distributions to enable widespread adoption. Balancing this privacy-utility trade-off has been a key challenge in designing privacy-preserving ML solutions.
In this talk, senior researcher Shruti Tople, from the Confidential Computing team at Microsoft Research Cambridge, will discuss well-known privacy attacks, such as membership inference, and show how causal learning techniques can play an important role in enhancing privacy guarantees of ML models.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
Causal Machine Learning
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Opening remarks: Causal Machine Learning
- Cheng Zhang
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Research talk: Challenges and opportunities in causal machine learning
- Amit Sharma,
- Cheng Zhang,
- Emre Kiciman
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Research talk: Causal ML and business
- Jacob LaRiviere
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Research talk: Causality for medical image analysis
- Daniel Coelho de Castro
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Research talk: Causal ML and fairness
- Allison Koenecke
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Panel: Causal ML Research at Microsoft
- Adith Swaminathan,
- Javier González Hernández,
- Justin Ding
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Research talk: Post-contextual-bandit inference
- Nathan Kallus
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Demo: Enabling end-to-end causal inference at scale
- Eleanor Dillon,
- Amit Sharma
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Panel: Causal ML at Microsoft
- Juan Lavista Ferres,
- Mingqi Wu,
- Sonia Jaffe
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Panel: Causal ML in industry
- Greg Lewis,
- Ya Xu,
- Totte Harinen
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Closing remarks: Causal Machine Learning
- Emre Kiciman
