Search Results for author:
Found 6 papers, 1 papers with code
Optimal subgroup selection
We formulate the problem as one of constrained optimisation, where we seek a low-complexity, data-dependent selection set on which, with a guaranteed probability, the regression function is uniformly at least as large as the threshold; subject to this constraint, we would like the region to contain as much mass under the marginal feature distribution as possible.
Adaptive transfer learning
In transfer learning, we wish to make inference about a target population when we have access to data both from the distribution itself, and from a different but related source distribution.
Data-driven design of targeted gene panels for estimating immunotherapy biomarkers
Based on this model, we then propose a new procedure for estimating biomarkers such as tumour mutation burden and tumour indel nurden.
Random projections: data perturbation for classification problems
Random projections offer an appealing and flexible approach to a wide range of large-scale statistical problems.
Classification with imperfect training labels
One consequence of these results is that the knn and SVM classifiers are robust to imperfect training labels, in the sense that the rate of convergence of the excess risks of these classifiers remains unchanged; in fact, our theoretical and empirical results even show that in some cases, imperfect labels may improve the performance of these methods.
Local nearest neighbour classification with applications to semi-supervised learning
We derive a new asymptotic expansion for the global excess risk of a local-$k$-nearest neighbour classifier, where the choice of $k$ may depend upon the test point.
