Transformer-based models have facilitated numerous applications with superior performance. A key challenge in transformers is the quadratic dependency of its training time complexity on the length of the input sequence. A recent popular solution is using ...

Despite the increasing success of Machine Learning (ML) techniques in real-world applications, their maintenance over time remains challenging. In particular, the prediction accuracy of deployed ML models can suffer due to significant changes between ...

We are witnessing an increasing availability of streaming data that may contain valuable information on the underlying processes. It is thus attractive to be able to deploy machine learning models, e.g., for classification, on edge devices near sensors ...

Over the past decade, millions of smart meters have been installed by electricity suppliers worldwide, allowing them to collect a large amount of electricity consumption data, albeit sampled at a low frequency (one point every 30min). One of the ...

Formatting is an important property in tables for visualization, presentation, and analysis. Spreadsheet software allows users to automatically format their tables by writing data-dependent conditional formatting (CF) rules. Writing such rules is often ...

Machine learning models are everywhere now; but only few of them are transparent in how they work. To remedy this, local explanations aim to show users how and why learned models produce a certain output for a given input (data sample). However, most ...

Effective supervised training of modern machine learning models often requires large labelled training datasets, which could be prohibitively costly to acquire for many practical applications. Research addressing this problem has sought ways to leverage ...

At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud, an explainable fraud transaction prediction framework which is mainly ...

We present DivExplorer, a tool that enables users to explore datasets and find subgroups of data for which a classifier behaves in an anomalous manner. These subgroups, denoted as divergent subgroups, may exhibit, for example, higher-than-normal false ...

The increasing complexity of data analysis tasks makes it dependent on human expertise and challenging for non-experts. One of the major challenges faced in data analysis is the selection of the proper algorithm for given tasks and data sets. Motivated ...

Deep learning (DL) has widespread applications and has revolutionized many industries. Although automated machine learning (AutoML) can help us away from coding for DL models, the acquisition of lots of high-quality data for model training remains a ...

High-quality labels are expensive to obtain for many machine learning tasks, such as medical image classification tasks. Therefore, probabilistic (weak) labels produced by weak supervision tools are used to seed a process in which influential samples ...

Given a data set D containing millions of data points and a data consumer who is willing to pay for $X to train a machine learning (ML) model over D, how should we distribute this $X to each data point to reflect its "value"? In this paper, we define the ...

Machine learning (ML) and advanced statistics are important tools for drawing insights from large datasets. However, these techniques often require human intervention to steer computation towards meaningful results. In this demo, we present Vizdom, a new ...

Crowd-sourcing has become a popular means of acquiring labeled data for many tasks where humans are more accurate than computers, such as image tagging, entity resolution, and sentiment analysis. However, due to the time and cost of human labor, ...

Sensors on mobile phones and wearables, and in general sensors on IoT (Internet of Things), bring forth a couple of new challenges to big data research. First, the power consumption for analyzing sensor data must be low, since most wearables and ...

Almost by definition, optimization is a source of a tremendous power for automatically improving processes, decisions, products and services. But its potential is still largely unexploited in most real-world contexts. One of the main reasons blocking its ...

Tabular data is an abundant source of information on the Web, but remains mostly isolated from the latter's interconnections since tables lack links and computer-accessible descriptions of their structure. In other words, the schemas of these tables -- ...

The proliferation of imprecise data has motivated both researchers and the database industry to push statistical techniques into relational database management systems (RDBMSes). We study strategies to maintain model-based views for a popular statistical ...

We address the problem of publishing a Naïve Bayesian Classifier (NBC) or, equivalently, publishing the necessary views for building an NBC, while protecting privacy of the individuals who provided the training data. Our approach completely preserves ...