With the overwhelming amount of data available both on and offline today...
With growing machine learning (ML) applications in healthcare, there hav...
Machine learning models are often personalized based on information that...
Forming a reliable judgement of a machine learning (ML) model's
appropri...
Literature on machine learning for multiple sclerosis has primarily focu...
Machine learning (ML) approaches have demonstrated promising results in ...
Fairness and robustness are often considered as orthogonal dimensions wh...
Survival analysis is a challenging variation of regression modeling beca...
ML models often exhibit unexpectedly poor behavior when they are deploye...
Bayesian neural networks (BNNs) demonstrate promising success in improvi...
The use of collaborative and decentralized machine learning techniques s...
In medicine, both ethical and monetary costs of incorrect predictions ca...
Learning-to-learn or meta-learning leverages data-driven inductive bias ...
We develop a hierarchical infinite latent factor model (HIFM) to
appropr...
This paper develops metrics from a social network perspective that are
d...
We propose a new method that uses deep learning techniques to solve the
...
Sepsis is a poorly understood and potentially life-threatening complicat...
We present a scalable end-to-end classifier that uses streaming physiolo...
Prediction of the future trajectory of a disease is an important challen...
For regular particle filter algorithm or Sequential Monte Carlo (SMC)
me...
We propose a second-order (Hessian or Hessian-free) based optimization m...
We present the Bayesian Echo Chamber, a new Bayesian generative model fo...
The use of L1 regularisation for sparse learning has generated immense
r...