215 citations · 888 across the 37 of their papers we have counts for
20 papers · 1 filter
CENTS: Generating synthetic electricity consumption time series for rare and unseen scenarios
Michael Fuest, Alfredo Cuesta, Kalyan Veeramachaneni
Recent breakthroughs in large-scale generative modeling have demonstrated the potential of foundation models in domains such as natural language, computer vision, and protein struc…
Large language models can be zero-shot anomaly detectors for time series?
Sarah Alnegheimish, Linh Nguyen, Laure Berti-Equille +1
Recent studies have shown the ability of large language models to perform a variety of tasks, including time series forecasting. The flexible nature of these models allows them to…
Pyreal: A Framework for Interpretable ML Explanations
Alexandra Zytek, Wei-En Wang, Dongyu Liu +2
Users in many domains use machine learning (ML) predictions to help them make decisions. Effective ML-based decision-making often requires explanations of ML models and their predi…
Lessons from Usable ML Deployments and Application to Wind Turbine Monitoring
Alexandra Zytek, Wei-En Wang, Sofia Koukoura +1
Through past experiences deploying what we call usable ML (one step beyond explainable ML, including both explanations and other augmenting information) to real-world domains, we h…
OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User
Sarah Alnegheimish, Laure Berti-Equille, Kalyan Veeramachaneni
Time series anomaly detection is a vital task in many domains, including patient monitoring in healthcare, forecasting in finance, and predictive maintenance in energy industries.…
AER: Auto-Encoder with Regression for Time Series Anomaly Detection
Lawrence Wong, Dongyu Liu, Laure Berti-Equille +2
Anomaly detection on time series data is increasingly common across various industrial domains that monitor metrics in order to prevent potential accidents and economic losses. How…
Sequential Models in the Synthetic Data Vault
Kevin Zhang, Neha Patki, Kalyan Veeramachaneni
The goal of this paper is to describe a system for generating synthetic sequential data within the Synthetic data vault. To achieve this, we present the Sequential model currently…
Sintel: A Machine Learning Framework to Extract Insights from Signals
Sarah Alnegheimish, Dongyu Liu, Carles Sala +2
The detection of anomalies in time series data is a critical task with many monitoring applications. Existing systems often fail to encompass an end-to-end detection process, to fa…
The Need for Interpretable Features: Motivation and Taxonomy
Alexandra Zytek, Ignacio Arnaldo, Dongyu Liu +2
Through extensive experience developing and explaining machine learning (ML) applications for real-world domains, we have learned that ML models are only as interpretable as their…
Enabling Collaborative Data Science Development with the Ballet Framework
Micah J. Smith, Jürgen Cito, Kelvin Lu +1
While the open-source software development model has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by in…
AutoML to Date and Beyond: Challenges and Opportunities
Shubhra Kanti Karmaker Santu, Md. Mahadi Hassan, Micah J. Smith +3
As big data becomes ubiquitous across domains, and more and more stakeholders aspire to make the most of their data, demand for machine learning tools has spurred researchers to ex…
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
Sarah Alnegheimish, Najat Alrashed, Faisal Aleissa +4
An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018. Despite the common workflow structure appearing in these publications, no trusted an…
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
Alexander Geiger, Dongyu Liu, Sarah Alnegheimish +2
Time series anomalies can offer information relevant to critical situations facing various fields, from finance and aerospace to the IT, security, and medical domains. However, det…
Towards Reducing Biases in Combining Multiple Experts Online
Yi Sun, Ivan Ramirez, Alfredo Cuesta-Infante +1
In many real life situations, including job and loan applications, gatekeepers must make justified and fair real-time decisions about a person's fitness for a particular opportunit…
Modeling Tabular data using Conditional GAN
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante +1
Modeling the probability distribution of rows in tabular data and generating realistic synthetic data is a non-trivial task. Tabular data usually contains a mix of discrete and con…
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data
Lei Xu, Shubhra Kanti Karmaker Santu, Kalyan Veeramachaneni
Most automation in machine learning focuses on model selection and hyper parameter tuning, and many overlook the challenge of automatically defining predictive tasks. We still heav…
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
Qianwen Wang, Yao Ming, Zhihua Jin +5
To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically s…
Learning Vine Copula Models For Synthetic Data Generation
Yi Sun, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
A vine copula model is a flexible high-dimensional dependence model which uses only bivariate building blocks. However, the number of possible configurations of a vine copula grows…
Prediction Factory: automated development and collaborative evaluation of predictive models
Gaurav Sheni, Benjamin Schreck, Roy Wedge +2
In this paper, we present a data science automation system called Prediction Factory. The system uses several key automation algorithms to enable data scientists to rapidly develop…
Synthesizing Tabular Data using Generative Adversarial Networks
Lei Xu, Kalyan Veeramachaneni
Generative adversarial networks (GANs) implicitly learn the probability distribution of a dataset and can draw samples from the distribution. This paper presents, Tabular GAN (TGAN…