27 citations · 63 across the 22 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…