8 citations · 16 across the 18 of their papers we have counts for
19 papers
TADACap: Time-series Adaptive Domain-Aware Captioning
Elizabeth Fons, Rachneet Kaur, Zhen Zeng +4
While image captioning has gained significant attention, the potential of captioning time-series images, prevalent in areas like finance and healthcare, remains largely untapped. E…
LAW: Legal Agentic Workflows for Custody and Fund Services Contracts
William Watson, Nicole Cho, Nishan Srishankar +7
Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights. However, it is cha…
AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations
Gaurav Verma, Rachneet Kaur, Nishan Srishankar +3
State-of-the-art multimodal web agents, powered by Multimodal Large Language Models (MLLMs), can autonomously execute many web tasks by processing user instructions and interacting…
Behavioral Sequence Modeling with Ensemble Learning
Maxime Kawawa-Beaudan, Srijan Sood, Soham Palande +3
We investigate the use of sequence analysis for behavior modeling, emphasizing that sequential context often outweighs the value of aggregate features in understanding human behavi…
Shining a Light on Hurricane Damage Estimation via Nighttime Light Data: Pre-processing Matters
Nancy Thomas, Saba Rahimi, Annita Vapsi +5
Amidst escalating climate change, hurricanes are inflicting severe socioeconomic impacts, marked by heightened economic losses and increased displacement. Previous research utilize…
Auditing and Enforcing Conditional Fairness via Optimal Transport
Mohsen Ghassemi, Alan Mishler, Niccolo Dalmasso +4
Conditional demographic parity (CDP) is a measure of the demographic parity of a predictive model or decision process when conditioning on an additional feature or set of features.…
Ensemble Methods for Sequence Classification with Hidden Markov Models
Maxime Kawawa-Beaudan, Srijan Sood, Soham Palande +3
We present a lightweight approach to sequence classification using Ensemble Methods for Hidden Markov Models (HMMs). HMMs offer significant advantages in scenarios with imbalanced…
Adaptive and Robust Watermark for Generative Tabular Data
Dung Daniel Ngo, Archan Ray, Akshay Seshadri +6
In recent years, watermarking generative tabular data has become a prominent framework to protect against the misuse of synthetic data. However, while most prior work in watermarki…
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
Aras Selvi, Eleonora Kreacic, Mohsen Ghassemi +3
Adversarially robust optimization (ARO) has emerged as the *de facto* standard for training models that hedge against adversarial attacks in the test stage. While these models are…
LETS-C: Leveraging Text Embedding for Time Series Classification
Rachneet Kaur, Zhen Zeng, Tucker Balch +1
Recent advancements in language modeling have shown promising results when applied to time series data. In particular, fine-tuning pre-trained large language models (LLMs) for time…
HiddenTables & PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies
William Watson, Nicole Cho, Tucker Balch +1
A myriad of different Large Language Models (LLMs) face a common challenge in contextually analyzing table question-answering tasks. These challenges are engendered from (1) finite…
Six Levels of Privacy: A Framework for Financial Synthetic Data
Tucker Balch, Vamsi K. Potluru, Deepak Paramanand +1
Synthetic Data is increasingly important in financial applications. In addition to the benefits it provides, such as improved financial modeling and better testing procedures, it p…
FlowMind: Automatic Workflow Generation with LLMs
Zhen Zeng, William Watson, Nicole Cho +4
The rapidly evolving field of Robotic Process Automation (RPA) has made significant strides in automating repetitive processes, yet its effectiveness diminishes in scenarios requir…
From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecasting
Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa +2
Time series forecasting plays a crucial role in decision-making across various domains, but it presents significant challenges. Recent studies have explored image-driven approaches…
Synthetic Data Applications in Finance
Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta +17
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applicatio…
Fair Wasserstein Coresets
Zikai Xiong, Niccolò Dalmasso, Shubham Sharma +5
Data distillation and coresets have emerged as popular approaches to generate a smaller representative set of samples for downstream learning tasks to handle large-scale datasets.…
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing
Zikai Xiong, Niccolò Dalmasso, Alan Mishler +3
Recent years have seen a surge of machine learning approaches aimed at reducing disparities in model outputs across different subgroups. In many settings, training data may be used…
Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections
Tom Bamford, Andrea Coletta, Elizabeth Fons +4
Financial firms commonly process and store billions of time-series data, generated continuously and at a high frequency. To support efficient data storage and retrieval, specialize…
Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe
Renbo Zhao, Niccolò Dalmasso, Mohsen Ghassemi +3
Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the sel…