activity
20102025
most citedSynthetic Data Applications in Finance

8 citations · 23 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CV2025

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…

cs.MA2024

Empirical Equilibria in Agent-based Economic systems with Learning agents

Kshama Dwarakanath, Svitlana Vyetrenko, Tucker Balch

We present an agent-based simulator for economic systems with heterogeneous households, firms, central bank, and government agents. These agents interact to define production, cons…

cs.CE2024

A Language Model-Guided Framework for Mining Time Series with Distributional Shifts

Haibei Zhu, Yousef El-Laham, Elizabeth Fons +1

Effective utilization of time series data is often constrained by the scarcity of data quantity that reflects complex dynamics, especially under the condition of distributional shi…

cs.AI20243 cited

LLM-driven Imitation of Subrational Behavior : Illusion or Reality?

Andrea Coletta, Kshama Dwarakanath, Penghang Liu +2

Modeling subrational agents, such as humans or economic households, is inherently challenging due to the difficulty in calibrating reinforcement learning models or collecting data…

cs.LG2024

Neural Stochastic Differential Equations with Change Points: A Generative Adversarial Approach

Zhongchang Sun, Yousef El-Laham, Svitlana Vyetrenko

Stochastic differential equations (SDEs) have been widely used to model real world random phenomena. Existing works mainly focus on the case where the time series is modeled by a s…

cs.LG2024

Augment on Manifold: Mixup Regularization with UMAP

Yousef El-Laham, Elizabeth Fons, Dillon Daudert +1

Data augmentation techniques play an important role in enhancing the performance of deep learning models. Despite their proven benefits in computer vision tasks, their application…