17 citations · 29 across the 6 of their papers we have counts for
6 papers
DYNOTEARS: Structure Learning from Time-Series Data
Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai +4
We revisit the structure learning problem for dynamic Bayesian networks and propose a method that simultaneously estimates contemporaneous (intra-slice) and time-lagged (inter-slic…
EMAP: Explanation by Minimal Adversarial Perturbation
Matt Chapman-Rounds, Marc-Andre Schulz, Erik Pazos +1
Modern instance-based model-agnostic explanation methods (LIME, SHAP, L2X) are of great use in data-heavy industries for model diagnostics, and for end-user explanations. These met…
Clusters in Explanation Space: Inferring disease subtypes from model explanations
Marc-Andre Schulz, Matt Chapman-Rounds, Manisha Verma +2
Identification of disease subtypes and corresponding biomarkers can substantially improve clinical diagnosis and treatment selection. Discovering these subtypes in noisy, high dime…
Auditing and Achieving Intersectional Fairness in Classification Problems
Giulio Morina, Viktoriia Oliinyk, Julian Waton +2
Machine learning algorithms are extensively used to make increasingly more consequential decisions about people, so achieving optimal predictive performance can no longer be the on…
Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring
Konstantinos Georgatzis, Christopher K. I. Williams, Christopher Hawthorne
We present a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients admitted in Intensive Care Units (ICUs). More specifically we are…
Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring
Konstantinos Georgatzis, Christopher K. I. Williams
We present a Discriminative Switching Linear Dynamical System (DSLDS) applied to patient monitoring in Intensive Care Units (ICUs). Our approach is based on identifying the state-o…