activity
20072026
most citedProceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (1999)

377 citations · 811 across the 28 of their papers we have counts for

collaborators

29 papers

cs.CR2026

Beyond the IT Checklist: Engineering a Reasonable Standard of Care for Cyber Safety

Matthew E. Jablonski, Linton Wells, Kathryn B. Laskey +1

Current U.S. cyber policy, centered on security, often treats documentation of controls and incident reports as a proxy for safety in the built environment. This paper argues that…

cs.LG2020

Effects of Model Misspecification on Bayesian Bandits: Case Studies in UX Optimization

Mack Sweeney, Matthew van Adelsberg, Kathryn Laskey +1

Bayesian bandits using Thompson Sampling have seen increasing success in recent years. Yet existing value models (of rewards) are misspecified on many real-world problem. We demons…

cs.AI2018

Reference Model of Multi-Entity Bayesian Networks for Predictive Situation Awareness

Cheol Young Park, Kathryn Blackmond Laskey

During the past quarter-century, situation awareness (SAW) has become a critical research theme, because of its importance. Since the concept of SAW was first introduced during Wor…

cs.LG2018★ 6 cited

MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model

Cheol Young Park, Kathryn Blackmond Laskey

Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism combining Bayesian Networks (BN) with First-Order Logic (FOL). MEBN has sufficient expressive power for…

cs.LG2018

Human-aided Multi-Entity Bayesian Networks Learning from Relational Data

Cheol Young Park, Kathryn Blackmond Laskey

An Artificial Intelligence (AI) system is an autonomous system which emulates human mental and physical activities such as Observe, Orient, Decide, and Act, called the OODA process…

cs.AI2018★ 2 cited

Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks

Cheol Young Park, Kathryn Blackmond Laskey, Paulo C. G. Costa +1

Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical di…