MicAugment transfers audio style from few seconds of input to match target conditions.
problem Audio model robustness to diverse acquisition conditions.
method Identifies and applies transformations learned from target audio to input audio.
result MicAugment significantly improves model robustness in downstream tasks.
New method optimizes costly functions with unknown costs and budget constraints.
problem Optimizing functions with unknown and heterogeneous evaluation costs under a budget constraint.
method Budgeted multi-step expected improvement acquisition function.
result Our method outperforms existing approaches in various synthetic and real problems.
HH-VAEM improves imputation and acquisition of missing data using hierarchical models and Hamiltonian Monte Carlo.
problem Imputation and acquisition of missing heterogeneous data.
method Hierarchical VAE model with Hamiltonian Monte Carlo and automatic hyper-parameter tuning.
result HH-VAEM outperforms existing methods in imputation and supervised learning tasks.
GAN normalizes CT scans for consistent radiomic feature values.
problem Variations in dose levels and slice thickness affect radiomic features sensitivity.
method Used a 3D generative adversarial network (GAN) to normalize reduced dose, thick slice images to normal dose, thinner slice images.
result GAN-based approach led to significantly smaller error in radiomic features.
Study uses LLMs for financial sentiment analysis without fine-tuning.
problem Lack of prescriptive knowledge to leverage generative models in FSA.
method Proposes a design framework with heterogeneous LLM agents based on Minsky's theory.
result Framework yields better accuracies, especially with substantial discussions.
Paper proposes a transfer learning framework for tensor Gaussian graphical models.
problem Pooling heterogeneous tensor data for improved estimation and variable selection.
method Transfer learning framework that uses data-adaptive weights from auxiliary domains.
result Significant improvement in estimation errors and variable selection consistency.
Learning by integrating multiple heterogeneous data sources is a common requirement in many tasks. Collective Matrix Factorization (CMF) is a technique to learn shared latent representations from arbitrary collections of matrices. It can be used to simultaneously complete one or more matrices, for predicting the unknow…
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.
problem How evidence acquisition costs affect disclosure behavior and preference.
method Analyzes sender-receiver interactions with covert and overt evidence acquisition, varying certification costs.
result Equilibria converge to the Pareto-worst free-learning equilibrium as costs vanish, and receivers prefer covert to overt acquisition.
Securely evaluates the benefits of merging datasets for causal estimation.
problem Challenges in assessing the value of merging datasets for causal treatment effect estimation.
method Cryptographically secure multi-party computation to evaluate Expected Information Gain (EIG) while ensuring privacy.
result Demonstrates the first privacy-preserving method for dataset acquisition tailored to causal estimation.
AFA evaluates AI feature acquisition strategies in domains with high costs.
problem Evaluate AI feature acquisition strategies in domains with high costs.
method Apply missing data methods and offline reinforcement learning under NDE and NUC assumptions.
result Propose a novel semi-offline reinforcement learning framework with three new estimators.
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.
problem Inexact maximization of acquisition functions in Bayesian optimization.
method Define inaccuracy measure, establish cumulative regret bounds for GP-UCB and GP-TS.
result Inexact BO algorithms can achieve sublinear cumulative regret under appropriate inaccuracy conditions.
A2MT learns agents to select which modalities to acquire at test time.
problem Learning agents to select modalities for multimodal temporal data acquisition.
method Perceiver IO architecture for active acquisition of multimodal temporal data.
result Agents successfully learn cost-reactive acquisition behavior on real-world datasets.
A simple method improves batch active learning without high compute.
problem Efficient batch active learning in machine learning.
method Adapting standard single-point acquisition strategies to batch.
result Simple strategy performs as well as advanced batch methods.
This paper explores optimising acquisition functions in Bayesian optimisation.
problem Optimising acquisition functions in Bayesian optimisation is challenging due to their non-convex nature.
method The authors derive compositional forms for acquisition functions and use them to recast maximisation as a compositional optimisation problem.
result The compositional approach to maximising acquisition functions shows empirical advantages across various tasks.
This paper optimizes Bayesian acquisition functions in Gaussian Processes for better optimization.
problem Improving the efficiency of Bayesian optimization methods.
method Analysis of different acquisition functions and optimizers for optimizing Bayesian acquisition functions.
result Optimization of acquisition functions leads to faster and more accurate sampling points.
New method combines regional HIV prevention trial data without sharing individual patient info.
problem Regional differences in HIV prevention efficacy, privacy concerns, and data sharing limitations.
method Federated learning approach that combines site-specific estimators via L1-regularization.
result Improved precision in estimating region-specific survival curves.
Optimizes information acquisition to reduce estimation risk and maximize utility.
problem Estimation risk in investor decision-making.
method Derives closed-form value functions using CARA and CRRA utility functions, employs variational methods to explore optimal acquisition.
result Acquiring information earlier is more valuable in reducing estimation risk and achieving higher utility.
Optimizes data acquisition in high-dimensional Bayesian optimization.
problem Suboptimal data acquisition in high-dimensional Bayesian optimization tasks.
method Utility-calibrated variational inference to align approximations with BO goals.
result Optimal data acquisition decisions under a limited computational budget.
Efficiently reduces computational burden of rollout acquisition functions in Bayesian optimization.
problem Expensive computation of rollout acquisition functions in Bayesian optimization.
method Combines quasi-Monte Carlo, common random numbers, and control variates to reduce computational burden. Formulates a policy-search approach to eliminate the need to optimize the rollout acquisition function.
result Significant reduction in computational burden of rollout acquisition functions.
NM-PPG optimizes adaptive feature acquisition in POMDPs for better predictions.
problem Optimizing adaptive feature acquisition in prediction problems with costly features.
method Non-myopic pathwise policy gradients (NM-PPG) with continuous relaxation and straight-through rollout.
result NM-PPG outperforms state-of-the-art AFA methods on synthetic and real-world datasets.
Boundary effects inflate variance in Gaussian processes, leading to acquisition bias.
problem Boundary-induced acquisition bias in Gaussian processes.
method Traced root cause to geometric mechanism of kernel truncation at domain boundaries.
result Boundary effects create distortion that worsens with dimensionality, affecting acquisition behavior.
Today's densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data providing temporal characterization of an individual's behaviors. Is it possible to efficiently couple such rich sensor data with predictive modeling techniques to provide contextual, and…
Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock ma…
Unified framework connects EI and information-theoretic acquisition functions.
problem Distinguish between Expected Improvement and information-theoretic acquisition functions.
method Introduces Variational Entropy Search (VES) to unify EI and information-theoretic approaches.
result EI can be seen as a variational inference approximation of Max-value Entropy Search (MES).
Proposes a new acquisition function for batched Bayesian optimization.
problem Intractability of acquisition functions for batched Bayesian optimization.
method Statistical physics inspired acquisition function for Gaussian processes.
result Demonstrates competitive performance on various problems.
Dynamic acquisition of features improves predictions with limited data.
problem Limited or uncertain data requires additional relevant information for accurate assessments.
method Proposes models that dynamically acquire new features using conditional mutual information and arbitrary conditional flow.
result Demonstrates superior performance over baselines in multiple settings.
This paper optimizes sampling policies for Bayesian optimization to improve exploration and exploitation.
problem Improving the balance between exploration and exploitation in Bayesian optimization.
method Developed efficient methods to estimate and optimize non-myopic acquisition functions using rollout policies and stochastic gradient optimization.
result Efficient optimization of sampling policies leads to better performance in Bayesian optimization.
New acquisition function improves batch Bayesian active learning.
problem BatchBALD conflates epistemic and aleatoric uncertainty, leading to suboptimal performance.
method Focus on predictive probabilities to separate epistemic uncertainty, leading to better performance and faster evaluation.
result The new acquisition function performs better and allows for larger batches.
With the rapid development of high-throughput technologies, parallel acquisition of large-scale drug-informatics data provides huge opportunities to improve pharmaceutical research and development. One significant application is the purpose prediction of small molecule compounds, aiming to specify therapeutic propertie…
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−e1-approximate a…
New method selects features for sequential decision making.
problem Dynamic feature selection for instance-wise decisions.
method Latent variable model trained in a supervised manner; reasoning across stochastic latent space.
result Outperforms existing methods on various datasets.
Study on optimal information acquisition in Kyle model with entropy cost.
problem Optimal information acquisition in Kyle model with entropy cost.
method Continuous signals are optimal, and any signal with a logit posterior distribution yields the same ex-ante value.
result Posterior expected payoff becomes normally distributed as information acquisition cost increases.
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.
problem Bayesian Optimization struggles with high-dimensional problems due to computational inefficiency.
method The paper leverages the additionality of the objective function to map kernel and acquisition functions in lower-dimensional subspaces, improving efficiency.
result Efficient optimization of acquisition function in high-dimensional problems.
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.
problem High-dimensional black-box optimization problems.
method Adaptive dropout of variables in the acquisition function.
result AdaDropout effectively tackles high-dimensional challenges and improves solution quality.
GOIMDA selects inputs to maximize expected influence on a goal functional, reducing data acquisition needs.
problem Challenges in active data acquisition for learning and optimization tasks in deep neural networks.
method GOIMDA uses inverse curvature and goal gradient to select inputs maximizing expected influence on a specified goal functional.
result GOIMDA achieves target performance with fewer labeled samples or function evaluations compared to baselines.
POLAR learns efficient data acquisition policies using pretrained belief representations.
problem Challenges in learning effective policies for adaptive data acquisition.
method POLAR decouples representation learning from policy learning by leveraging pretrained predictive foundation models as belief-state encoders.
result POLAR outperforms state-of-the-art methods across diverse tasks while requiring fewer training samples.
Unsupervised framework captures acquisition variability in structural connectomes.
problem Acquisition differences across sites, scanners, and protocols complicate structural connectome analysis.
method An unsupervised framework using architectural annealing to balance discrete and continuous latent variables.
result Architectural annealing produces stronger site learning than baseline models.
Proposes qPO, a new acquisition strategy for batched Bayesian optimization that maximizes the probability of including the optimum.
problem Efficiently identifying top-performing compounds from a large chemical library.
method qPO (multipoint Probability of Optimality) acquisition strategy that maximizes the probability of including the true optimum.
result Empirical evidence shows that qPO is competitive with and complements other state-of-the-art methods in batched Bayesian optimization.
Proposes efficient data acquisition for personalized treatment effects from observational data.
problem Efficiently acquiring outcomes for personalized treatment effects in observational studies.
method Introduces causal, Bayesian acquisition functions to select points with overlapping support.
result Demonstrates improved sample efficiency and accuracy in learning personalized treatment effects.
BOOST automates kernel and acquisition function selection in Bayesian optimization.
problem Inappropriate kernel and acquisition function combinations lead to poor performance in Bayesian optimization.
method BOOST uses offline evaluation to predict and select the best kernel-acquisition function pair.
result BOOST consistently improves over fixed-hyperparameter BO and is competitive with state-of-the-art adaptive methods.
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…
Real estate appraisal is a complex and important task, that can be made more precise and faster with the help of automated valuation tools. Usually the value of some property is determined by taking into account both structural and geographical characteristics. However, while geographical information is easily found, o…
Bayesian optimization surveys information-theoretic acquisition functions.
problem Optimizing noisy, expensive, non-convex functions with unknown gradients.
method Bayesian optimization using Gaussian process surrogate models and information-theoretic acquisition functions.
result Information-theoretic acquisition functions outperform others in real scenarios.