37 citations · 37 across the 5 of their papers we have counts for
5 papers
Latent Preference Bandits
Newton Mwai, Emil Carlsson, Fredrik D. Johansson
Bandit algorithms are guaranteed to solve diverse sequential decision-making problems, provided that a sufficient exploration budget is available. However, learning from scratch is…
Prediction Models That Learn to Avoid Missing Values
Lena Stempfle, Anton Matsson, Newton Mwai +1
Handling missing values at test time is challenging for machine learning models, especially when aiming for both high accuracy and interpretability. Established approaches often ad…
Identifiable Latent Bandits: Leveraging observational data for personalized decision-making
Ahmet Zahid Balcıoğlu, Newton Mwai, Emil Carlsson +1
Sequential decision-making algorithms such as multi-armed bandits can find optimal personalized decisions, but are notoriously sample-hungry. In personalized medicine, for example,…
ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effects
Newton Mwai Kinyanjui, Fredrik D. Johansson
Simulators make unique benchmarks for causal effect estimation since they do not rely on unverifiable assumptions or the ability to intervene on real-world systems, but are often t…
Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets
Newton M. Kinyanjui, Timothy Odonga, Celia Cintas +4
Recent advances in computer vision and deep learning have led to breakthroughs in the development of automated skin image analysis. In particular, skin cancer classification models…