arXiv research

This paper optimizes sampling policies for Bayesian optimization to improve exploration and exploitation.

Efficiently optimizes constrained problems with two-step lookahead BO.

NM-PPG optimizes adaptive feature acquisition in POMDPs for better predictions.

New method optimizes costly functions with unknown costs and budget constraints.

Efficiently reduces computational burden of rollout acquisition functions in Bayesian optimization.

PFNs4BO uses neural processes for flexible Bayesian Optimization.

Bayesian optimization surveys information-theoretic acquisition functions.

JES optimizes expensive functions by considering joint entropy over input and output spaces.

Lookahead, also known as non-myopic, Bayesian optimization (BO) aims to find optimal sampling policies through solving a dynamic program (DP) that maximizes a long-term reward over a rolling horizon. Though promising, lookahead BO faces the risk of error propagation through its increased dependence on a possibly mis-sp…

Study optimal portfolio strategy with sporadic bankruptcy for isoelastic utility.

We propose a general-purpose approach to discovering active learning (AL) strategies from data. These strategies are transferable from one domain to another and can be used in conjunction with many machine learning models. To this end, we formalize the annotation process as a Markov decision process, design universal s…

Bayesian optimization is a sample-efficient approach to solving global optimization problems. Along with a surrogate model, this approach relies on theoretically motivated value heuristics (acquisition functions) to guide the search process. Maximizing acquisition functions yields the best performance; unfortunately, t…

Evidence acquisition costs influence disclosure behavior and preference.

A new mechanism reduces expert belief regret in online forecasting.

AFA evaluates AI feature acquisition strategies in domains with high costs.

Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process. Fully maximizing acquisition functions produces the Bayes' decision rule, but this ideal is difficult to achieve since these functions …

Inexact acquisition solutions in BO lead to sublinear cumulative regret.

A2MT learns agents to select which modalities to acquire at test time.

A simple method improves batch active learning without high compute.

This paper explores optimising acquisition functions in Bayesian optimisation.

This paper optimizes Bayesian acquisition functions in Gaussian Processes for better optimization.

Bayesian methods improve drug discovery experiment design.

Optimizes information acquisition to reduce estimation risk and maximize utility.

Optimizes data acquisition in high-dimensional Bayesian optimization.

Boundary effects inflate variance in Gaussian processes, leading to acquisition bias.

Unified framework connects EI and information-theoretic acquisition functions.

Proposes a new acquisition function for batched Bayesian optimization.

We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next function value; the non-myopic approaches that do exist are able to consider only a handful of futu…

Dynamic acquisition of features improves predictions with limited data.

New acquisition function improves batch Bayesian active learning.

We consider the problem of active feature acquisition, where we sequentially select the subset of features in order to achieve the maximum prediction performance in the most cost-effective way. In this work, we formulate this active feature acquisition problem as a reinforcement learning problem, and provide a novel fr…

We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the task of deep Bayesian active learning. BatchBALD is a greedy linear-time $1 - \frac{1}{e}$-approximate a…

New method selects features for sequential decision making.

Study on optimal information acquisition in Kyle model with entropy cost.

We present Acquisition Thompson Sampling (ATS), a novel technique for batch Bayesian Optimization (BO) based on the idea of sampling multiple acquisition functions from a stochastic process. We define this process through the dependency of the acquisition functions on a set of model hyper-parameters. ATS is conceptuall…

This paper optimizes kernel and acquisition functions for high-dimensional Bayesian Optimization.

Bayesian optimization is a sample-efficient method for finding a global optimum of an expensive-to-evaluate black-box function. A global solution is found by accumulating a pair of query point and its function value, repeating these two procedures: (i) modeling a surrogate function; (ii) maximizing an acquisition funct…

An adaptive dropout approach improves high-dimensional Bayesian optimization.

GOIMDA selects inputs to maximize expected influence on a goal functional, reducing data acquisition needs.

POLAR learns efficient data acquisition policies using pretrained belief representations.

Unsupervised framework captures acquisition variability in structural connectomes.

Proposes qPO, a new acquisition strategy for batched Bayesian optimization that maximizes the probability of including the optimum.

Proposes efficient data acquisition for personalized treatment effects from observational data.

BOOST automates kernel and acquisition function selection in Bayesian optimization.

We propose a novel, theoretically-grounded, acquisition function for Batch Bayesian optimization informed by insights from distributionally ambiguous optimization. Our acquisition function is a lower bound on the well-known Expected Improvement function, which requires evaluation of a Gaussian Expectation over a multiv…

qEUBO optimizes decision-making with noisy feedback.

Bayesian optimization methods improved for min max optimization problems.

New method tackles constrained optimization in multi-fidelity Bayesian optimization.