Research talk: Can causal learning improve the privacy of ML models?
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)
- Track:
- Causal Machine Learning
- Date:
- Speakers:
- Shruti Tople
- Affiliation:
- Microsoft Research
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Shruti Tople
Principal Researcher
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Causal Machine Learning
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Opening remarks: Causal Machine Learning
Speakers:- Cheng Zhang
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Research talk: Causal ML and business
Speakers:- Jacob LaRiviere
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Panel: Challenges and opportunities of causality
Speakers:- Susan Athey,
- Yoshua Bengio,
- Judea Pearl
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Research talk: Causal ML and fairness
Speakers:- Allison Koenecke
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Panel: Causal ML Research at Microsoft
Speakers:- Daniel McDuff,
- Javier González,
- Justin Ding
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Research talk: Post-contextual-bandit inference
Speakers:- Nathan Kallus
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Panel: Causal ML in industry
Speakers:- Ya Xu,
- Totte Harinen,
- Dawen Liang
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