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I Can’t Believe It’s Not Better! Bridging the gap between theory and empiricism in probabilistic machine learning
Jessica Forde · Francisco Ruiz · Melanie Fernandez Pradier · Aaron Schein · Finale Doshi-Velez · Isabel Valera · David Blei · Hanna Wallach

Sat Dec 12 04:45 AM -- 02:45 PM (PST) @
Event URL: https://i-cant-believe-its-not-better.github.io/ »

We’ve all been there. A creative spark leads to a beautiful idea. We love the idea, we nurture it, and name it. The idea is elegant: all who hear it fawn over it. The idea is justified: all of the literature we have read supports it. But, lo and behold: once we sit down to implement the idea, it doesn’t work. We check our code for software bugs. We rederive our derivations. We try again and still, it doesn’t work. We Can’t Believe It’s Not Better [1].

In this workshop, we will encourage probabilistic machine learning researchers who Can’t Believe It’s Not Better to share their beautiful idea, tell us why it should work, and hypothesize why it does not in practice. We also welcome work that highlights pathologies or unexpected behaviors in well-established practices. This workshop will stress the quality and thoroughness of the scientific procedure, promoting transparency, deeper understanding, and more principled science.

Focusing on the probabilistic machine learning community will facilitate this endeavor, not only by gathering experts that speak the same language, but also by exploiting the modularity of probabilistic framework. Probabilistic machine learning separates modeling assumptions, inference, and model checking into distinct phases [2]; this facilitates criticism when the final outcome does not meet prior expectations. We aim to create an open-minded and diverse space for researchers to share unexpected or negative results and help one another improve their ideas.

Author Information

Jessica Forde (Brown University)
Francisco Ruiz (DeepMind)
Melanie Fernandez Pradier (Microsoft Research)
Aaron Schein (Columbia University)
Finale Doshi-Velez (Harvard)
Isabel Valera (Saarland University & MPI for Intelligent Systems)
David Blei (Columbia University)

David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data. David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), and ACM-Infosys Foundation Award (2013). He is a fellow of the ACM.

Hanna Wallach (Microsoft)

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