In February 2021, Facebook launched a request for proposals (RFP) on sample-efficient sequential Bayesian decision-making. Today, we’re announcing the winners of this award.
In a Q&A about the RFP, Core Data Science researchers said they are keen to learn more about all the great research that is going on in the area of Bayesian optimization. Eytan Bakshy and Max Balandat, members of the team behind the RFP, also spoke about sharing a number of really interesting real-world use cases that they hope can help inspire additional applied research and increase interest and research activity into sample-efficient sequential Bayesian decision-making.
The team reviewed 89 high-quality proposals and are pleased to announce the two winning proposals below, as well as the 10 finalists. Thank you to everyone who took the time to submit a proposal, and congratulations to the winners.
Accelerate infectious disease simulation with interactive neural process
Rose Yu, Yian Ma (University of California San Diego)
Bridging Bayesian optimization and differentiable physical simulation
Jeannette Bohg, Krishna Murthy Jatavallabhula, Rika Antonova (Stanford University)
Adaptive experimentation to discover effective drug combinations in cancer
Wesley Tansey, Eduard Reznik, Karuna Ganesh (Memorial Sloan Kettering Cancer Center)
Bayesian learning for optimal vaccine allocation
Stefan Wager, Han Wu (Stanford University)
Bayesian optimization for hardware & software configuration co-optimization
Michael Carbin, Hank Hoffmann, Yi Ding (Massachusetts Institute of Technology)
Bayesian optimization of images for adversarial attacks
Marc Deisenroth (University College London)
Efficient Bayesian optimization for high-dimensional integer solution space
Eunhye Song (Pennsylvania State University)
Human-computer cooperative Bayesian optimization for scientific discovery
Roman Garnett, Alvitta Ottley (Washington University in St. Louis)
Inference after adaptive experimentation
Kuang Xu, David A. Hirshberg (Stanford University)
Integrating BoTorch into closed-loop autonomous empirical research
Michael J. Frank, Sebastian Musslick (Brown University)
Judicious exploration in high-dimensions with output-weighted sampling
Paris Perdikaris, Yibo Yang (University of Pennsylvania)
Speedy performance estimator for AutoML
Yarin Gal, Binxin Ru (Oxford University)
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