37 citations · 78 across the 38 of their papers we have counts for
7 papers · 1 filter
Toward Latent Language Model Skills Steering and Optimization: An Empirical Study
Xunyi Jiang, Junda Wu, Yuxin Xiong +6
Skills, as a useful abstraction for the procedural capabilities of large language models (LLMs), capture how models perform structured, multi-step reasoning and program execution.…
Experimentation Accelerator: Interpretable Insights and Creative Recommendations for A/B Testing with Content-Aware ranking
Zhengmian Hu, Lei Shi, Ritwik Sinha +2
Modern online experimentation faces two bottlenecks: scarce traffic forces tough choices on which variants to test, and post-hoc insight extraction is manual, inconsistent, and oft…
Evaluation and Incident Prevention in an Enterprise AI Assistant
Akash V. Maharaj, David Arbour, Daniel Lee +6
Enterprise AI Assistants are increasingly deployed in domains where accuracy is paramount, making each erroneous output a potentially significant incident. This paper presents a co…
Learning Relational Causal Models with Cycles through Relational Acyclification
Ragib Ahsan, David Arbour, Elena Zheleva
In real-world phenomena which involve mutual influence or causal effects between interconnected units, equilibrium states are typically represented with cycles in graphical models.…
Relational Causal Models with Cycles:Representation and Reasoning
Ragib Ahsan, David Arbour, Elena Zheleva
Causal reasoning in relational domains is fundamental to studying real-world social phenomena in which individual units can influence each other's traits and behavior. Dynamics bet…
Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning
Tong Mu, Georgios Theocharous, David Arbour +1
Online reinforcement learning (RL) algorithms are often difficult to deploy in complex human-facing applications as they may learn slowly and have poor early performance. To addres…
A Sound and Complete Algorithm for Learning Causal Models from Relational Data
Marc Maier, Katerina Marazopoulou, David Arbour +1
The PC algorithm learns maximally oriented causal Bayesian networks. However, there is no equivalent complete algorithm for learning the structure of relational models, a more expr…