[Jan/2023] The talks of our NeurIPS 2022 workshop are now online. Watch them here!
[Nov/2022] We have launched the ICBINB Repository of Unexpected Negative Results. Feedback and suggestions are welcomed.
What is ICBINB?
The ICBINB initiative is a movement within the ML community for well-executed meaningful research beyond bold numbers. The goals of the initiative are to crack open the research process, to re-value unexpected negative results, question well-established default practices, and advance the understanding, elegance, and diversity of the field as opposed to focusing solely on the outcome and just rewarding approaches that beat previous works on a given benchmark.
The three pillars of such an initiative include:
- Shed light on the research process: sharing stories about how research is really done behind the curtains, encouraging transparency and reproducibility.
- Provide a platform for high-quality but under-valued ML research: showcase, disseminate, and support valuable work that is currently under-represented given publication incentives. This includes negative results/failed attempts, simple approaches that work well in practice, and applied work.
- Develop an inclusive and welcoming community of researchers and practitioners that share the same values to support and help each other to conduct deep high-impact research. Encourage meta-dialog on how we should be conducting top-tier ML research.
Who we are
Here our wonderful team of volunteers! None of this would be possible without their help.
University of Cambridge
University of Oxford
University of Edinburgh
Criteo AI Lab
Carnegie Mellon University
City University of Hong Kong
Imperial College London
Cornell University / MSKCC
Dpt of Brain & Cognitive Sciences, MIT
Amsterdam University & MSR
Melanie F. Pradier
Francisco J.R. Ruiz
What we believe
- Process over outcome
- Deep understanding of experimental results
- Intellectual and methodological transparency
- Depth over breadth
- Collaboration and peer support
Ongoing and future events