Name
Abstract | ||
|---|---|---|
Information-theoretic Bayesian optimisation techniques have demonstrated state-of-the-art performance in tackling important global optimisation problems. However, current information-theoretic approaches: require many approximations in implementation; limit the choice of kernels available to model the objective; and introduce often-prohibitive computational overhead. We develop a fast information-theoretic Bayesian Optimisation method, FITBO, that circumvents the need for sampling the global minimiser, thus significantly reducing computational overhead. Moreover, in comparison with existing approaches, our method faces fewer constraints on kernel choice and enjoys the merits of dealing with the output space. We demonstrate empirically that FITBO inherits the performance associated with information-theoretic Bayesian optimisation, while being even faster than simpler Bayesian optimisation approaches, such as Expected Improvement. |
Year | Venue | Field |
|---|---|---|
2018 | ICML | Kernel (linear algebra),Overhead (computing),Mathematical optimization,Sampling (statistics),Artificial intelligence,Mathematics,Machine learning,Bayesian probability |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Xin Ru | 1 | 1 | 4.42 |
| Mark McLeod | 2 | 1 | 0.70 |
| Diego Granziol | 3 | 6 | 2.15 |
| Michael Osborne | 4 | 250 | 33.49 |