Percy Liang on Machine Learning Robustness, Foundation Models, and Reproducibility

In interview 21 of The Gradient Podcast, we talk to Percy Liang, an Associate Professor of Computer Science at Stanford University and the director of the Center for Research on Foundation Models.

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Percy Liang’s research spans many topics in machine learning and natural language processing, including robustness, interpretability, semantics, and reasoning.  He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets.  His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), a Microsoft Research Faculty Fellowship (2014), and multiple paper awards at ACL, EMNLP, ICML, and COLT.

Sections:

(00:00) Intro
(01:21) Start in AI
(06:52) Interest in Language
(10:17) Start of PhD
(12:22) Semantic Parsing
(17:49) Focus on ML robustness
(22:30) Foundation Models, model robustness
(28:55) Foundation Model bias
(34:48) Foundation Model research by academia
(37:13) Current research interests
(39:40) Surprising robustness results
(44:24) Reproducibility and CodaLab
(50:17) Outro

Papers / Topics discussed:

• On the Opportunities and Risks of Foundation Models

• Reflections on Foundation Models

• Removing spurious features can hurt accuracy and affect groups disproportionately.

• Selective classification can magnify disparities across groups

• Just train twice: improving group robustness without training group information

• LILA: language-informed latent actions

• CodaLab