27 citations · 63 across the 22 of their papers we have counts for
22 papers
REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values
Shubham Sharma, Sanghamitra Dutta, Emanuele Albini +3
Feature selection is a crucial step in building machine learning models. This process is often achieved with accuracy as an objective, and can be cumbersome and computationally exp…
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…
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…
Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates
Kyle Mana, Fernando Acero, Stephen Mak +4
Discrete optimization belongs to the set of -hard problems, spanning fields such as mixed-integer programming and combinatorial optimization. A current standard appro…
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…
Learn to explain yourself, when you can: Equipping Concept Bottleneck Models with the ability to abstain on their concept predictions
Joshua Lockhart, Daniele Magazzeni, Manuela Veloso
The Concept Bottleneck Models (CBMs) of Koh et al. [2020] provide a means to ensure that a neural network based classifier bases its predictions solely on human understandable conc…
Towards learning to explain with concept bottleneck models: mitigating information leakage
Joshua Lockhart, Nicolas Marchesotti, Daniele Magazzeni +1
Concept bottleneck models perform classification by first predicting which of a list of human provided concepts are true about a datapoint. Then a downstream model uses these predi…
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations
Nelson Vadori, Leo Ardon, Sumitra Ganesh +7
We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market, for which the typical example is foreign exchange. We show how a su…
ASPiRe:Adaptive Skill Priors for Reinforcement Learning
Mengda Xu, Manuela Veloso, Shuran Song
We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning. Unlike existing methods that learn a single…
Online Learning for Mixture of Multivariate Hawkes Processes
Mohsen Ghassemi, Niccolò Dalmasso, Simran Lamba +4
Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors. However, these works typically either…
Differentially Private Learning of Hawkes Processes
Mohsen Ghassemi, Eleonora Kreačić, Niccolò Dalmasso +3
Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data. While they have a rich history…
Bandit Sampling for Multiplex Networks
Cenk Baykal, Vamsi K. Potluru, Sameena Shah +1
Graph neural networks have gained prominence due to their excellent performance in many classification and prediction tasks. In particular, they are used for node classification an…
Structure and Semantics Preserving Document Representations
Natraj Raman, Sameena Shah, Manuela Veloso
Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text. The inclusion of structural relationship betw…
Synthetic Document Generator for Annotation-free Layout Recognition
Natraj Raman, Sameena Shah, Manuela Veloso
Analyzing the layout of a document to identify headers, sections, tables, figures etc. is critical to understanding its content. Deep learning based approaches for detecting the la…
Tradeoffs in Streaming Binary Classification under Limited Inspection Resources
Parisa Hassanzadeh, Danial Dervovic, Samuel Assefa +2
Institutions are increasingly relying on machine learning models to identify and alert on abnormal events, such as fraud, cyber attacks and system failures. These alerts often need…
Parameterized Explanations for Investor / Company Matching
Simerjot Kaur, Ivan Brugere, Andrea Stefanucci +3
Matching companies and investors is usually considered a highly specialized decision making process. Building an AI agent that can automate such recommendation process can signific…
How Robust are Limit Order Book Representations under Data Perturbation?
Yufei Wu, Mahmoud Mahfouz, Daniele Magazzeni +1
The success of machine learning models in the financial domain is highly reliant on the quality of the data representation. In this paper, we focus on the representation of limit o…
Towards Robust Representation of Limit Orders Books for Deep Learning Models
Yufei Wu, Mahmoud Mahfouz, Daniele Magazzeni +1
The success of deep learning-based limit order book forecasting models is highly dependent on the quality and the robustness of the input data representation. A significant body of…
Visual Time Series Forecasting: An Image-driven Approach
Naftali Cohen, Srijan Sood, Zhen Zeng +2
In this work, we address time-series forecasting as a computer vision task. We capture input data as an image and train a model to produce the subsequent image. This approach resul…
Visual Time Series Forecasting: An Image-driven Approach
Srijan Sood, Zhen Zeng, Naftali Cohen +2
Time series forecasting is essential for agents to make decisions. Traditional approaches rely on statistical methods to forecast given past numeric values. In practice, end-users…
SURF: Improving classifiers in production by learning from busy and noisy end users
Joshua Lockhart, Samuel Assefa, Ayham Alajdad +3
Supervised learning classifiers inevitably make mistakes in production, perhaps mis-labeling an email, or flagging an otherwise routine transaction as fraudulent. It is vital that…