3 citations · 11 across the 17 of their papers we have counts for
4 papers · 1 filter
Skill Reuse as Compression in Agentic RL
Zhikun Xu, Yu Feng, Jacob Dineen +3
Large language model agents trained with reinforcement learning (RL) often learn brittle, task-specific shortcuts. We hypothesize that agents generalize better when their successfu…
Model Interpretation and Explainability: Towards Creating Transparency in Prediction Models
Donald Kridel, Jacob Dineen, Daniel Dolk +1
Explainable AI (XAI) has a counterpart in analytical modeling which we refer to as model explainability. We tackle the issue of model explainability in the context of prediction mo…
Unified Explanations in Machine Learning Models: A Perturbation Approach
Jacob Dineen, Don Kridel, Daniel Dolk +1
A high-velocity paradigm shift towards Explainable Artificial Intelligence (XAI) has emerged in recent years. Highly complex Machine Learning (ML) models have flourished in many ta…
Reinforcement Learning For Data Poisoning on Graph Neural Networks
Jacob Dineen, A S M Ahsan-Ul Haque, Matthew Bielskas
Adversarial Machine Learning has emerged as a substantial subfield of Computer Science due to a lack of robustness in the models we train along with crowdsourcing practices that en…