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1.Online Learning 3: Adversarial bandit learning with catastrophes
post by Ryan Carey 860 days ago | Vadim Kosoy and Patrick LaVictoire like this | discuss

Note: This describes an idea of Jessica Taylor’s.

In order to better understand how machine learning systems might avoid catastrophic behavior, we are interested in modeling this as an adversarial learning problem.

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2.Online Learning 2: Exploration-only bandit learning with catastrophes
post by Ryan Carey 876 days ago | 5 comments

Note: This describes an idea of Jessica Taylor’s.

The usual training procedures for machine learning models are not always well-equipped to avoid rare catastrophes. In order to maintain the safety of powerful AI systems, it will be important to have training procedures that can efficiently learn from such events. [1]

We can model this situation with the problem of exploration-only online bandit learning. We will show that if agents allocate more of their attention to risky inputs, they can more efficiently achieve a low regret on this problem.

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3.Equilibria in adversarial supervised learning
post by Ryan Carey 889 days ago | Vadim Kosoy, Nate Soares and Patrick LaVictoire like this | discuss

Note: This describes an idea of Jessica Taylor’s.

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4.Online Learning 1: Bias-detecting online learners
post by Ryan Carey 898 days ago | Vadim Kosoy, Jessica Taylor, Nate Soares and Paul Christiano like this | 7 comments

Note: This describes an idea of Jessica Taylor’s, and is the first of several posts about aspects of online learning.

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