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Have you seen Mykel Kochenderfer, Tim Wheeler, and Kyle Wray's new textbook on decision making under uncertainty yet? This @StanfordHAI blog post has you covered:

Check out the full textbook at https://algorithmsbook.com.

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"What must we do to advance the organizing power of mankind alongside the technological advances of our extraordinary age?" @robreich quoting Einstein at the panel on Foundation Models at @StanfordHAI livestream: https://stanford.io/3v7V8gS

Pretraining is ≈SoTA for domain adaptation: just do contrastive learning on *all* unlabeled data + finetune on source labels. Features are NOT domain-invariant, but disentangle class & domain info to enable transfer. Theory & exps: http://arxiv.org/abs/2204.00570 http://arxiv.org/abs/2204.02683

Unhappy with existing tooling for evaluating ML models? Look no further than our latest blog post from @EyubogluSabri introducing Domino: an approach for automatically discovering systematic errors in ML models. https://ai.stanford.edu/blog/domino/

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We Are Pleased to Welcome New Members of Our Faculty

Sanmi Koyejo who focuses on Trustworthy Machine Learning for Healthcare and Neuroscience

Carlos Guestrin who focuses on Machine Learning, Explainability, Fairness and ML Systems.

 

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Sanmi KoyejoTrustworthy Machine Learning for Healthcare and Neuroscience
Carlos GuestrinMachine Learning, Explainability, Fairness and ML Systems

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Stanford AI Lab faculty and students enjoy chances to understand and solve the not-yet-doable pain points of industry. Get a chance to support and interact with SAIL’s brightest minds.

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