Modeling the mechanics of fluid in complex scenes is vital to applicatio...
The number of states in a dynamic process is exponential in the number o...
Robustness is of central importance in machine learning and has given ri...
Standard learning approaches are designed to perform well on average for...
Most recommender systems (RS) research assumes that a user's utility can...
Many dynamic processes, including common scenarios in robotic control an...
Despite impressive progress in the last decade, it still remains an open...
In this work, we present causal directed acyclic graphs (DAGs) as a unif...
We consider the problem of learning representations that achieve group a...
How do we learn from biased data? Historical datasets often reflect
hist...
Explanations of black-box classifiers often rely on saliency maps, which...
In this work, we advocate for representation learning as the key to
miti...