Search · four archives
Search · four archives
21 papers · ranked by Valyu relevance
Tavor Z. Baharav, Gary J. Cheng, Mert Pilancı, David Tse
We study the problem of estimating the value of a known smooth function f at an unknown point µ ∈ R n, where each component µi can be sampled via a noisy oracle. Sampling more frequently components of µ corresponding to directions of the function with larger directional derivatives is more sample-efficient. However, as…
Albert S. Berahas, Raghu Bollapragada, Baoyu Zhou
This paper presents a methodology for using varying sample sizes in sequential quadratic programming (SQP) methods for solving equality constrained stochastic optimization problems. The first part of the paper deals with the delicate issue of dynamic sample selection in the evaluation of the gradient in conjunction…
Sandra Pieraccini, Tommaso Vanzan
Adaptive sampling algorithms are modern and efficient methods that dynamically adjust the sample size throughout the optimization process. However, they may encounter difficulties in risk-averse settings, particularly due to the challenge of accurately sampling from the tails of the underlying distribution of random…
Suryateja Ravutla, Andrew Bai, Matthew J. Realff, Fani Boukouvala
Hybridization and Adaptive Sampling for Simulation-Based Optimization Authors: ['Suryateja Ravutla' 'Andrew Bai' 'Matthew J. Realff' 'Fani Boukouvala'] Process simulators are essential for modeling of complex processes; however, optimization of expensive models remains challenging due to lack of equations, simulation…
Finlay Clark, Graeme Robb, Daniel Cole, Julien Michel
Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a…
Félix Mercier, Nizar Bouhlel, Angelina El Ghaziri, Joseph Ly Vu + 2 more
Digital phenotyping is rapidly advancing, generating increasing amounts of data, particularly in the case of temporal monitoring. We propose an adaptive sampling method that optimizes sampling, thereby reducing costs associated with data production, processing, and storage. The proposed method is based on Bayesian…
Guillermo Gallego, Danny Segev
In the adaptive ProbeMax problem, given a collection of mutually-independent random variables X1, . . . , Xn, our goal is to design an adaptive probing policy for sequentially sampling at most k of these variables, with the objective of maximizing the expected maximum value sampled. In spite of its stylized…
Mateu Sbert, László Szirmay-Kalos, Chun-Hung Liu, Jwo-Yuh Wu + 1 more
'Peter Y. Hong'] Multiple Importance Sampling (MIS) combines the probability density functions (pdf) of several sampling techniques. The combination weights depend on the proportion of samples used for the particular techniques. Weights can be found by optimization of the variance, but this approach is costly and…
Shengyi He, Guangxin Jiang, Henry Lam, Michael C. Fu
In solving simulation-based stochastic root-finding or optimization problems that involve rare events, such as in extreme quantile estimation, running crude Monte Carlo can be prohibitively inefficient. To address this issue, importance sampling can be employed to drive down the sampling error to a desirable level.…
Sina Dabiri, Eric R. Cole, Robert E. Gross
Brain stimulation has become an important treatment option for a variety of neurological and psychiatric diseases. A key challenge in improving brain stimulation is selecting the optimal set of stimulation parameters for each patient, as parameter spaces are too large for brute-force search and their induced effects…
Fulvia Mecatti, Charalambos Sismanidis, Emanuela Furfaro, Pier Luigi Conti
'Pier Luigi Conti'] A new class of sampling strategies is proposed that can be applied to population-based surveys targeting a rare trait that is unevenly spread over an area of interest. Our proposal is characterised by the ability to tailor the data collection to specific features and challenges of the survey at…
Pariyakorn Maneekul, Zelda B. Zabinsky, Giulia Pedrielli
Global optimization of black-box functions is challenging in high dimensions. We introduce a conceptual adaptive random search framework, Branching Adaptive Surrogate Search Optimization (BASSO), that combines partitioning and surrogate modeling for subregion sampling. We present a finite-time analysis of BASSO, and…
Authors not listed
Accurate and efficient calculation of alchemical free energies is a critical challenge in computational chemistry, frequently hindered by the inherent limitations of conventional Thermodynamic Integration (TI) methods. These limitations include poor phasespace overlap between discrete alchemical states, inefficient…
Sumedh S Nagrale, Alik S Widge
The use of Deep Brain Stimulation (DBS) on the ventral capsule/ventral striatum (VCVS) has therapeutic potential for patients with refractory psychiatric disorders, but clinical success is impeded by the need for a time-consuming and trial-and-error process when setting the parameters, this process relying on…
Javier Garcia-Barcos, Ruben Martinez-Cantin, Eduardo C. Garrido-Merchán
'Eduardo C. Garrido-Merchán'] Optimizing complex systems usually involves costly and time-consuming experiments, where selecting the experiments to perform is fundamental. Bayesian optimization (BO) has proved to be a suitable optimization method in these situations thanks to its sample efficiency and principled way of…
Rabea Turon, Lars C. Reining, Philipp A. Hummel, Lynn Schmittwilken + 5 more
Behavioral experiments are often infeasible when stimulus spaces have many dimensions or when testing time is limited. One way to address this challenge is adaptive stimulus selection, where informative stimuli are chosen dynamically based on participants’ responses. However, in high-dimensional spaces, identifying…
Authors not listed
For applications in gas sensing, purification, and capture, we often wish to search a large set of metal-organic frameworks (MOFs) for the top-K in terms of their Henry coefficient of an adsorbate. A molecular simulation to predict the Henry coefficient of a MOF constitutes a Monte Carlo integration where each sample…
Joan Saurina-i-Ricos, Daniel Mas Montserrat, Alexander G. Ioannidis
Estimating genetic clusters from sequencing data is a fundamental task in population and medical genetics, enabling demographic inference and adjustment for population structure in association studies. ADMIXTURE, a widely used model-based clustering method, employs an accelerated Expectation–Maximization (EM) algorithm…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Eric R. Cole, Mark J. Connolly, Mihir Ghetiya, Mohammad E. S. Sendi + 3 more
To treat neurological and psychiatric diseases with deep brain stimulation, a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient…