Asserting the winners of the Pattern-Environment friendly Sequential Bayesian Choice Making request for proposals

In February 2021, Facebook launched a call for proposals (RFP) for Bayesian randomized sequential decision making. Today we announce the winners of this award.
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In a question-and-answer session on the RFP, Core data science Researchers said they were interested in learning more about the great research that is being done in the field of Bayesian optimization. Eytan Bakshy and Max Balandat, Members of the RFP team also talked 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 in sample-efficient sequential Bayesian decision making .

The team has reviewed 89 high quality proposals and is happy 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

Research Award Winner Research

Accelerate the simulation of infectious diseases with interactive neural processes
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 the co-optimization of the hardware and software configuration
Michael Carbin, Hank Hoffmann, Yi Ding (Massachusetts Institute of Technology)

Bayesian optimization of images for hostile 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)

Integration of BoTorch in a closed, autonomous empirical research
Michael J. Frank, Sebastian Musslick (Brown University)

Careful exploration in high dimensions with performance-weighted sampling
Paris Perdikaris, Yibo Yang (University of Pennsylvania)

Fast performance estimator for AutoML
Yarin Gal, Binxin Ru (Oxford University)

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