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Found 17 papers, 2 papers with code
Optimal convex $M$-estimation via score matching
As an example of a non-log-concave setting, for Cauchy errors, the optimal convex loss function is Huber-like, and our procedure yields an asymptotic efficiency greater than 0. 87 relative to the oracle maximum likelihood estimator of the regression coefficients that uses knowledge of this error distribution; in this sense, we obtain robustness without sacrificing much efficiency.
Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning
We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random projections of the data.
The Projected Covariance Measure for assumption-lean variable significance testing
In this work we study the problem of testing the model-free null of conditional mean independence, i. e. that the conditional mean of $Y$ given $X$ and $Z$ does not depend on $X$.
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.
USP: an independence test that improves on Pearson's chi-squared and the $G$-test
We present the $U$-Statistic Permutation (USP) test of independence in the context of discrete data displayed in a contingency table.
Isotonic regression with unknown permutations: Statistics, computation, and adaptation
Motivated by models for multiway comparison data, we consider the problem of estimating a coordinate-wise isotonic function on the domain $[0, 1]^d$ from noisy observations collected on a uniform lattice, but where the design points have been permuted along each dimension.
High-dimensional, multiscale online changepoint detection
We introduce a new method for high-dimensional, online changepoint detection in settings where a $p$-variate Gaussian data stream may undergo a change in mean.
Optimal rates for independence testing via $U$-statistic permutation tests
We study the problem of independence testing given independent and identically distributed pairs taking values in a $\sigma$-finite, separable measure space.
Goodness-of-fit testing in high-dimensional generalized linear models
We propose a family of tests to assess the goodness-of-fit of a high-dimensional generalized linear model.
Methodology Statistics Theory Statistics Theory
Efficient functional estimation and the super-oracle phenomenon
One interesting consequence of our results is the discovery that, for certain functionals, the worst-case performance of our estimator may improve on that of the natural `oracle' estimator, which is given access to the values of the unknown densities at the observations.
The conditional permutation test for independence while controlling for confounders
Like the conditional randomization test of Cand\`es et al. (2018), our test relies on the availability of an approximation to the distribution of $X \mid Z$.
Methodology Statistics Theory Statistics Theory
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.
Sparse principal component analysis via axis-aligned random projections
We introduce a new method for sparse principal component analysis, based on the aggregation of eigenvector information from carefully-selected axis-aligned random projections of the sample covariance matrix.
Nonparametric independence testing via mutual information
We propose a test of independence of two multivariate random vectors, given a sample from the underlying population.
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.
Statistical and computational trade-offs in estimation of sparse principal components
In this paper, we show that, under a widely-believed assumption from computational complexity theory, there is a fundamental trade-off between statistical and computational performance in this problem.
