I Can't Believe It's Not Better! Workshop
ICBINB@NeurIPS 2020 - Bridging the gap between theory and empiricism in probabilistic machine learning
LIVE STREAM is here.
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 phases2; 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.
In conjunction with NeurIPS, the workshop will be held virtually on Saturday 12th, December 2020. Please see our schedule for details.
|Michael C. Hughes
University of Toronto
How do I submit?
We accept submissions through our OpenReview workshop site.
October 14th October 16th 23:59 Anywhere on Earth. Accept/reject notification will be sent out by October 31st.
Check out our CFP for detailed submission guidelines.
- Jessica Zosa Forde, Brown University
- Francisco Ruiz, DeepMind
- Melanie F. Pradier, Harvard University
- Aaron Schein, Columbia University
- Finale Doshi-Velez, Harvard University
- Isabel Valera, MPI for Intelligent System
- David Blei, Columbia University
- Hanna Wallach, Microsoft Research
For any question or suggestion, please contact us at: email@example.com