no code implementations • 2 Feb 2024 • Su Jia, Peter Frazier, Nathan Kallus
Prior research on experimentation with interference has concentrated on the final output of a policy.
no code implementations • NeurIPS 2021 • Yunxiang Zhang, Xiangyu Zhang, Peter Frazier
Recent advances in computationally efficient non-myopic Bayesian optimization offer improved query efficiency over traditional myopic methods like expected improvement, with only a modest increase in computational cost.
2 code implementations • NeurIPS 2020 • Sait Cakmak, Raul Astudillo, Peter Frazier, Enlu Zhou
We consider Bayesian optimization of objective functions of the form $\rho[ F(x, W) ]$, where $F$ is a black-box expensive-to-evaluate function and $\rho$ denotes either the VaR or CVaR risk measure, computed with respect to the randomness induced by the environmental random variable $W$.
no code implementations • NeurIPS 2019 • Jian Wu, Peter Frazier
Expected improvement and other acquisition functions widely used in Bayesian optimization use a "one-step" assumption: they value objective function evaluations assuming no future evaluations will be performed.
no code implementations • 25 Apr 2016 • Tobias Schnabel, Adith Swaminathan, Peter Frazier, Thorsten Joachims
Eliciting relevance judgments for ranking evaluation is labor-intensive and costly, motivating careful selection of which documents to judge.
no code implementations • TACL 2015 • Bishan Yang, Claire Cardie, Peter Frazier
We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution.