Synthetic Data Applications in Finance

no code implementations • 29 Dec 2023 • Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta, Niccolò Dalmasso, Yousef El-Laham, Elizabeth Fons, Mohsen Ghassemi, Sriram Gopalakrishnan, Vikesh Gosai, Eleonora Kreačić, Ganapathy Mani, Saheed Obitayo, Deepak Paramanand, Natraj Raman, Mikhail Solonin, Srijan Sood, Svitlana Vyetrenko, Haibei Zhu, Manuela Veloso, Tucker Balch

Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.

Fairness Time Series

Visual Time Series Forecasting: An Image-driven Approach

no code implementations • 2 Jul 2021 • Naftali Cohen, Srijan Sood, Zhen Zeng, Tucker Balch, Manuela Veloso

In this work, we address time-series forecasting as a computer vision task.

Time Series Time Series Forecasting

Visual Time Series Forecasting: An Image-driven Approach

no code implementations • 18 Nov 2020 • Srijan Sood, Zhen Zeng, Naftali Cohen, Tucker Balch, Manuela Veloso

In this work, we leverage advances in deep learning to extend the field of time series forecasting to a visual setting.

Quantization Time Series +1

Debiasing classifiers: is reality at variance with expectation?

no code implementations • 4 Nov 2020 • Ashrya Agrawal, Florian Pfisterer, Bernd Bischl, Francois Buet-Golfouse, Srijan Sood, Jiahao Chen, Sameena Shah, Sebastian Vollmer

We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse rather than better.

Fairness

Cross-Domain Perceptual Reward Functions

no code implementations • 25 May 2017 • Ashley D. Edwards, Srijan Sood, Charles L. Isbell Jr

One problem with this approach is that we typically need to redefine the rewards each time the goal changes, which often requires some understanding of the solution in the agents environment.

reinforcement-learning Reinforcement Learning (RL)