black-box function
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization
Bayesian optimization (BO) conventionally relies on handcrafted acquisition functions (AFs) to sequentially determine the sample points. However, it has been widely observed in practice that the best-performing AF in terms of regret can vary significantly under different types of black-box functions. It has remained a challenge to design one AF that can attain the best performance over a wide variety of black-box functions. This paper aims to attack this challenge through the perspective of reinforced few-shot AF learning (FSAF). Specifically, we first connect the notion of AFs with Q-functions and view a deep Q-network (DQN) as a surrogate differentiable AF. While it serves as a natural idea to combine DQN and an existing few-shot learning method, we identify that such a direct combination does not perform well due to severe overfitting, which is particularly critical in BO due to the need of a versatile sampling policy. To address this, we present a Bayesian variant of DQN with the following three features: (i) It learns a distribution of Q-networks as AFs based on the Kullback-Leibler regularization framework. This inherently provides the uncertainty required in sampling for BO and mitigates overfitting.
Optimizing Conditional Value-At-Risk of Black-Box Functions
This paper presents two Bayesian optimization (BO) algorithms with theoretical performance guarantee to maximize the conditional value-at-risk (CVaR) of a black-box function: CV-UCB and CV-TS which are based on the well-established principle of optimism in the face of uncertainty and Thompson sampling, respectively. To achieve this, we develop an upper confidence bound of CVaR and prove the no-regret guarantee of CV-UCB by utilizing an interesting connection between CVaR and value-at-risk (VaR). For CV-TS, though it is straightforwardly performed with Thompson sampling, bounding its Bayesian regret is non-trivial because it requires a tail expectation bound for the distribution of CVaR of a black-box function, which has not been shown in the literature. The performances of both CV-UCB and CV-TS are empirically evaluated in optimizing CVaR of synthetic benchmark functions and simulated real-world optimization problems.
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation
Shivapratap Gopakumar, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh
Neural Information Processing Systems http://nips.cc/
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation
Shivapratap Gopakumar, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh
Neural Information Processing Systems http://nips.cc/
EfficientHighDimensionalBayesianOptimization withAdditivityandQuadratureFourierFeatures
Bayesian Optimization (BO) is a versatile method for global optimization of a black-box function using noisy point-wise observations.
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cedric Archambeau, Rodolphe Jenatton
Neural Information Processing Systems http://nips.cc/
Scalable Hyperparameter Transfer Learning
Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cedric Archambeau
In this work, we aim to warm-start BO in the context of hyperparameter optimization (HPO).
Distributionally Robust Bayesian Optimization with ϕ-divergences
As with all data-driven approaches, BO is prone to cases where the given data shifts from the data of interest.
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization
Bayesian optimization (BO) conventionally relies on handcrafted acquisition functions (AFs) to sequentially determine the sample points. However, it has been widely observed in practice that the best-performing AF in terms of regret can vary significantly under different types of black-box functions. It has remained a challenge to design one AF that can attain the best performance over a wide variety of black-box functions.