activity
20192026
most citedABIDES: Towards High-Fidelity Market Simulation for AI Research

35 citations · 225 across the 65 of their papers we have counts for

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Showing 2024Show all

23 papers · 1 filter

cs.AI2024★ 2 cited

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…

cs.AI2024

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…

cs.LG2024

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…

cs.LG2024

Variational Neural Stochastic Differential Equations with Change Points

Yousef El-Laham, Zhongchang Sun, Haibei Zhu +2

In this work, we explore modeling change points in time-series data using neural stochastic differential equations (neural SDEs). We propose a novel model formulation and training…

cs.LG2024★ 9 cited

AI in Investment Analysis: LLMs for Equity Stock Ratings

Kassiani Papasotiriou, Srijan Sood, Shayleen Reynolds +1

Investment Analysis is a cornerstone of the Financial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), o…

cs.CV2024

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…

cs.LG2024

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.…

cs.LG2024

AI versus AI in Financial Crimes and Detection: GenAI Crime Waves to Co-Evolutionary AI

Eren Kurshan, Dhagash Mehta, Bayan Bruss +1

Adoption of AI by criminal entities across traditional and emerging financial crime paradigms has been a disturbing recent trend. Particularly concerning is the proliferation of ge…

cs.LG2024

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…

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…

math.OC2024

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…

cs.LG2024

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…

cs.AI2024★ 2 cited

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…

cs.CR2024

Atlas-X Equity Financing: Unlocking New Methods to Securely Obfuscate Axe Inventory Data Based on Differential Privacy

Antigoni Polychroniadou, Gabriele Cipriani, Richard Hua +1

Banks publish daily a list of available securities/assets (axe list) to selected clients to help them effectively locate Long (buy) or Short (sell) trades at reduced financing rate…

cs.CL2024

Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark

Elizabeth Fons, Rachneet Kaur, Soham Palande +4

Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, clima…

cs.CR2024

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…

cs.CL2024

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…

cs.CV2024

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…

cs.AI2024★ 3 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.MA2024★ 1 cited

ABIDES-Economist: Agent-Based Simulator of Economic Systems with Learning Agents

Kshama Dwarakanath, Tucker Balch, Svitlana Vyetrenko

We present ABIDES-Economist, an agent-based simulator for economic systems that includes heterogeneous households, firms, a central bank, and a government. Agent behavior can be de…

cs.MA2024

Transparency as Delayed Observability in Multi-Agent Systems

Kshama Dwarakanath, Svitlana Vyetrenko, Toks Oyebode +1

Is transparency always beneficial in complex systems such as traffic networks and stock markets? How is transparency defined in multi-agent systems, and what is its optimal degree…

cs.LG2024★ 1 cited

Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models

Yinan Cheng, Chi-Hua Wang, Vamsi K. Potluru +2

Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance. Existing studies focused on the utility of a single f…

cs.LG2024★ 8 cited

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…