2 citations · 5 across the 6 of their papers we have counts for
7 papers
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…
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…
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…
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…