arXiv research

A new method estimates uncertainty without explicit prediction models.

In most real-world settings such as recommender systems, finance, and healthcare, collecting useful information is costly and requires an active choice on the part of the decision maker. The decision-maker needs to learn simultaneously what observations to make and what actions to take. This paper incorporates the info…

Evidence acquisition costs influence disclosure behavior and preference.

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

Meta-learning improves model performance by optimizing data acquisition.

Study improves feature acquisition for static settings in AFAPE.

Active learning selects samples for labeling to build accurate models with minimal labeled data.

New methods prioritize acquiring confounding features for efficient treatment effect estimation.

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

BOOST automates kernel and acquisition function selection in Bayesian optimization.

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

We introduce a novel apprenticeship learning algorithm to learn an expert's underlying reward structure in off-policy model-free \emph{batch} settings. Unlike existing methods that require a dynamics model or additional data acquisition for on-policy evaluation, our algorithm requires only the batch data of observed ex…

Simplifies decision-making during medical exams with cost-efficient feature acquisition.

Active learning selects both observations and annotation precision for Gaussian Processes.

Bayesian optimization is a powerful global optimization technique for expensive black-box functions. One of its shortcomings is that it requires auxiliary optimization of an acquisition function at each iteration. This auxiliary optimization can be costly and very hard to carry out in practice. Moreover, it creates ser…

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

Parallelizes active learning for Bayesian inference using Nested Sampler.

The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine …

As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition …

DARTS optimizes covariate selection in trials with limited data.

Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest. Nevertheless, the high cost involved hamper reliable and exhaustive simulations. Very often such codes incorporate heuristics that ironical…

Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant infor…

Active learning optimizes correlation clustering by querying the most informative pairwise comparisons.

This paper introduces a more efficient method for estimating level sets with a stopping criterion.

Securely evaluates the benefits of merging datasets for causal estimation.

Model captures decision-making under bounded rationality with prior beliefs and market feedback.

A review of statistical SSL methods showing improved classifier performance.

In this paper we introduce the ice-start problem, i.e., the challenge of deploying machine learning models when only little or no training data is initially available, and acquiring each feature element of data is associated with costs. This setting is representative for the real-world machine learning applications. Fo…

This paper studies a composite problem involving the decision making of the optimal entry time and dynamic consumption afterwards. In stage-1, the investor has access to full market information subjecting to some information costs and needs to choose an optimal stopping time to initiate stage-2; in stage-2, the investo…

Optimizes data acquisition in high-dimensional Bayesian optimization.

Supervised machine learning methods usually require a large set of labeled examples for model training. However, in many real applications, there are plentiful unlabeled data but limited labeled data; and the acquisition of labels is costly. Active learning (AL) reduces the labeling cost by iteratively selecting the mo…

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

In recent years, intelligent condition-based monitor-ing of rotary machinery systems has become a major researchfocus of machine fault diagnosis. In condition-based monitoring,it is challenging to form a large-scale well-annotated datasetdue to the expense of data acquisition and costly annotation.The generated data ha…

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…

Dynamic acquisition of features improves predictions with limited data.

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

Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample. Particularly, for affect estimation in the 3D space of valence, arousal an…

Algorithm optimizes measurement sequence to minimize data acquisition.

POLAR learns efficient data acquisition policies using pretrained belief representations.

Machine learning automates digitization of historical data.

This study evaluates Bayesian optimization algorithms on a wide range of problems.

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

AE-LSVI identifies near-optimal policies in complex systems with minimal data.

A simple method improves batch active learning without high compute.

New method optimizes Bayesian optimization for high-dimensional posterior samples.

OLPA optimizes online user-centric selection with probing, achieving near-optimal regret bounds.

Active inference framework improves $U$-statistic estimation efficiency.

The paper develops a method to learn cost-optimal sequential testing policies from retrospective data.