35 citations · 225 across the 65 of their papers we have counts for
11 papers · 1 filter
Once Burned, Twice Shy? The Effect of Stock Market Bubbles on Traders that Learn by Experience
Haibei Zhu, Svitlana Vyetrenko, Serafin Grundl +3
We study how experience with asset price bubbles changes the trading strategies of reinforcement learning (RL) traders and ask whether the change in trading strategies helps to pre…
A Model-Based Synthetic Stock Price Time Series Generation Framework
Haibei Zhu, Svitlana Vyetrenko, Tucker Balch
The Ornstein-Uhlenbeck (OU) process, a mean-reverting stochastic process, has been widely applied as a time series model in various domains. This paper describes the design and imp…
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…
Prime Match: A Privacy-Preserving Inventory Matching System
Antigoni Polychroniadou, Gilad Asharov, Benjamin Diamond +6
Inventory matching is a standard mechanism/auction for trading financial stocks by which buyers and sellers can be paired. In the financial world, banks often undertake the task of…
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…
INTAGS: Interactive Agent-Guided Simulation
Song Wei, Andrea Coletta, Svitlana Vyetrenko +1
In many applications involving multi-agent system (MAS), it is imperative to test an experimental (Exp) autonomous agent in a high-fidelity simulator prior to its deployment to pro…
Exploring the Effectiveness of GPT Models in Test-Taking: A Case Study of the Driver's License Knowledge Test
Saba Rahimi, Tucker Balch, Manuela Veloso
Large language models such as Open AI's Generative Pre-trained Transformer (GPT) models are proficient at answering questions, but their knowledge is confined to the information pr…
Differentially Private Synthetic Data Using KD-Trees
Eleonora Kreačić, Navid Nouri, Vamsi K. Potluru +2
Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge. Many space partitioning based a…
Financial Time Series Forecasting using CNN and Transformer
Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa +3
Time series forecasting is important across various domains for decision-making. In particular, financial time series such as stock prices can be hard to predict as it is difficult…
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs
Andrea Coletta, Svitlana Vyetrenko, Tucker Balch
Learning agent behaviors from observational data has shown to improve our understanding of their decision-making processes, advancing our ability to explain their interactions with…