Search Results for author:
Found 5 papers, 1 papers with code
Integrating Tick-level Data and Periodical Signal for High-frequency Market Making
We focus on the problem of market making in high-frequency trading.
Optimal Execution Using Reinforcement Learning
This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall.
MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information
Here, we consider a matrix factorization problem by utilizing auxiliary information, which is massively available in real-world applications, to overcome the challenges caused by poor data quality.
A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail
We first explore the structure of this dataset with a focus on the application of Graph Representation Learning in order to exploit the natural data structure and provide statistical insights into particular features within the data.
Wiener filters on graphs and distributed polynomial approximation algorithms
In this paper, we consider Wiener filters to reconstruct deterministic and (wide-band) stationary graph signals from their observations corrupted by random noises, and we propose distributed algorithms to implement Wiener filters and inverse filters on networks in which agents are equipped with a data processing subsystem for limited data storage and computation power, and with a one-hop communication subsystem for direct data exchange only with their adjacent agents.
