### BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

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 algorithm amenable to dynamic programming and efficient caching. We compare BatchBALD to the commonly used approach for batch data acquisition and find that the current approach acquires similar and redundant points, sometimes performing worse than randomly acquiring data. We finish by showing that, using BatchBALD to consider dependencies within an acquisition batch, we achieve new state of the art performance on standard benchmarks, providing substantial data efficiency improvements in batch acquisition.

### Talent Acquisition Tech with Artificial Intelligence

Earlier I wrote about different ways in which HR and recruiting can use artificial intelligence to help bare some of the administrative burden that seems to take up so much of our time. In this article, I mentioned that artificial intelligence is defined as "an ideal'intelligent' machine [that] is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal." It's a branch of computer science that uses machine learning algorithms that mimic cognitive functions; making machines more human-like. I am a firm believer that A.I. technologies can allow HR as well as talent acquisition to be more strategic in our jobs. A.I. is great for pattern matching and prediction, however, I still have many questions especially surrounding whether A.I. is ethical at using these growing number of tools to use as part of your diversity recruiting efforts.

### Talent Acquisition Tech with Artificial Intelligence

Earlier I wrote about different ways in which HR and recruiting can use artificial intelligence to help bare some of the administrative burden that seems to take up so much of our time. In this article, I mentioned that artificial intelligence is defined as "an ideal'intelligent' machine [that] is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal." It's a branch of computer science that uses machine learning algorithms that mimic cognitive functions; making machines more human-like. I am a firm believer that A.I. technologies can allow HR as well as talent acquisition to be more strategic in our jobs. A.I. is great for pattern matching and prediction, however, I still have many questions especially surrounding whether A.I. is ethical at using these growing number of tools to use as part of your diversity recruiting efforts.

### VOILA: Efficient Feature-value Acquisition for Classification

We address the problem of efficient feature-value acquisition for classification in domains in which there are varying costs associated with both feature acquisition and misclassification. The objective is to minimize the sum of the information acquisition cost and misclassification cost. Any decision theoretic strategy tackling this problem needs to compute value of information for sets of features. Having calculated this information, different acquisition strategies are possible (acquiring one feature at time, acquiring features in sets, etc.). However, because the value of information calculation for arbitrary subsets of features is computationally intractable, most traditional approaches have been greedy, computing values of features one at a time.

### Nutrition and Health Data for Cost-Sensitive Learning

Traditionally, machine learning algorithms have been focused on modeling dynamics of a certain dataset at hand for which all features are available for free. However, there are many concerns such as monetary data collection costs, patient discomfort in medical procedures, and privacy impacts of data collection that require careful consideration in any health analytics system. An efficient solution would only acquire a subset of features based on the value it provides whilst considering acquisition costs. Moreover, datasets that provide feature costs are very limited, especially in healthcare. In this paper, we provide a health dataset as well as a method for assigning feature costs based on the total level of inconvenience asking for each feature entails. Furthermore, based on the suggested dataset, we provide a comparison of recent and state-of-the-art approaches to cost-sensitive feature acquisition and learning. Specifically, we analyze the performance of major sensitivity-based and reinforcement learning based methods in the literature on three different problems in the health domain, including diabetes, heart disease, and hypertension classification.