245 citations · 489 across the 17 of their papers we have counts for
19 papers
CINDI: Conditional Imputation and Noisy Data Integrity with Flows in Power Grid Data
David Baumgartner, Helge Langseth, Heri Ramampiaro
Real-world multivariate time series, particularly in critical infrastructure such as electrical power grids, are often corrupted by noise and anomalies that degrade the performance…
Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection
David Baumgartner, Helge Langseth, Kenth Engø-Monsen +1
This paper introduces temporal-conditioned normalizing flows (tcNF), a novel framework that addresses anomaly detection in time series data with accurate modeling of temporal depen…
Causal computations in Semi Markovian Structural Causal Models using divide and conquer
Anna Rodum Bjøru, Rafael Cabañas, Helge Langseth +1
Recently, Bjøru et al. proposed a novel divide-and-conquer algorithm for bounding counterfactual probabilities in structural causal models (SCMs). They assumed that the SCMs were l…
Lecture Notes in Probabilistic Diffusion Models
Inga Strümke, Helge Langseth
Diffusion models are loosely modelled based on non-equilibrium thermodynamics, where \textit{diffusion} refers to particles flowing from high-concentration regions towards low-conc…
Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders
Bjørnar Vassøy, Helge Langseth, Benjamin Kille
An emerging definition of fairness in machine learning requires that models are oblivious to demographic user information, e.g., a user's gender or age should not influence the mod…
Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation
Bjørnar Vassøy, Helge Langseth
In the current landscape of ever-increasing levels of digitalization, we are facing major challenges pertaining to scalability. Recommender systems have become irreplaceable both f…