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On safety in safe Bayesian optimization
Optimizing an unknown function under safety constraints is a central task in robotics,
biomedical engineering, and many other disciplines, and increasingly safe Bayesian …
biomedical engineering, and many other disciplines, and increasingly safe Bayesian …
Orthogonal representation learning for estimating causal quantities
Representation learning is widely used for estimating causal quantities (eg, the conditional
average treatment effect) from observational data. While existing representation learning …
average treatment effect) from observational data. While existing representation learning …
On statistical learning theory for distributional inputs
Kernel-based statistical learning on distributional inputs appears in many relevant
applications, from medical diagnostics to causal inference, and poses intriguing theoretical …
applications, from medical diagnostics to causal inference, and poses intriguing theoretical …
Recent kernel methods for interacting particle systems: first numerical results
Interacting particle systems (IPSs) are a very important class of dynamical systems, arising in
different domains like biology, physics, sociology and engineering. In many applications …
different domains like biology, physics, sociology and engineering. In many applications …
Safe exploration in reproducing kernel Hilbert spaces
Popular safe Bayesian optimization (BO) algorithms successfully control safety-critical
systems in unknown environments. However, most algorithms require smoothness …
systems in unknown environments. However, most algorithms require smoothness …