Abstaining classifiers have the option to abstain from making prediction...
On observing a sequence of i.i.d. data with distribution P on
ℝ^d, we as...
We propose a doubly optimistic strategy for the
safe-linear-bandit probl...
We investigate a natural but surprisingly unstudied approach to the
mult...
Motivated by applications to resource-limited and safety-critical domain...
We present novel information-theoretic limits on detecting sparse change...
We propose a novel method for selective classification (SC), a problem w...
We present a new piecewise linear regression methodology that utilizes
f...
Conventional machine learning applications in the mobile/IoT setting tra...
We introduce the problems of goodness-of-fit and two-sample testing of t...
The change detection problem is to determine if the Markov network struc...