215 citations · 888 across the 37 of their papers we have counts for
38 papers
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…
Explingo: Explaining AI Predictions using Large Language Models
Alexandra Zytek, Sara Pido, Sarah Alnegheimish +2
Explanations of machine learning (ML) model predictions generated by Explainable AI (XAI) techniques such as SHAP are essential for people using ML outputs for decision-making. We…
LLMs for XAI: Future Directions for Explaining Explanations
Alexandra Zytek, Sara Pidò, Kalyan Veeramachaneni
In response to the demand for Explainable Artificial Intelligence (XAI), we investigate the use of Large Language Models (LLMs) to transform ML explanations into natural, human-rea…
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…
Single Word Change is All You Need: Using LLMs to Create Synthetic Training Examples for Text Classifiers
Lei Xu, Sarah Alnegheimish, Laure Berti-Equille +2
In text classification, creating an adversarial example means subtly perturbing a few words in a sentence without changing its meaning, causing it to be misclassified by a classifi…
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…
MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series
Dongyu Liu, Sarah Alnegheimish, Alexandra Zytek +1
Detecting anomalies in time-varying multivariate data is crucial in various industries for the predictive maintenance of equipment. Numerous machine learning (ML) algorithms have b…
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models
Furui Cheng, Dongyu Liu, Fan Du +5
Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks. Although many ML models perform promisingly, issues with model…
R&R: Metric-guided Adversarial Sentence Generation
Lei Xu, Alfredo Cuesta-Infante, Laure Berti-Equille +1
Adversarial examples are helpful for analyzing and improving the robustness of text classifiers. Generating high-quality adversarial examples is a challenging task as it requires g…
Meeting in the notebook: a notebook-based environment for micro-submissions in data science collaborations
Micah J. Smith, Jürgen Cito, Kalyan Veeramachaneni
Developers in data science and other domains frequently use computational notebooks to create exploratory analyses and prototype models. However, they often struggle to incorporate…
AQEyes: Visual Analytics for Anomaly Detection and Examination of Air Quality Data
Dongyu Liu, Kalyan Veeramachaneni, Alexander Geiger +2
Anomaly detection plays a key role in air quality analysis by enhancing situational awareness and alerting users to potential hazards. However, existing anomaly detection approache…
Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making
Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan +1
Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts - who often have no expertise in ML or data science - are asked t…
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…
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers
Lei Xu, Ivan Ramirez, Kalyan Veeramachaneni
Most adversarial attack methods that are designed to deceive a text classifier change the text classifier's prediction by modifying a few words or characters. Few try to attack cla…
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…
Robust Invisible Video Watermarking with Attention
Kevin Alex Zhang, Lei Xu, Alfredo Cuesta-Infante +1
The goal of video watermarking is to embed a message within a video file in a way such that it minimally impacts the viewing experience but can be recovered even if the video is re…