Statistical Learning
Learning-to-learn non-convex piecewise-Lipschitz functions
The flexibility of this approach has led to its widespread adoption, e.g. in robotics [
Robust Generalized Method of Moments: A Finite Sample Viewpoint
For many inference problems in statistics and econometrics, the unknown parameter is identified by a set of moment conditions.
7e0ff37942c2de60cbcbd27041196ce3-Paper.pdf
To measure the difference between two probability distributions, referred to as the source and target, respectively, we exploit both the chain rule and Bayes' theorem to construct conditional transport (CT), which is constituted by both a
Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles
When a deep learning model is deployed in the wild, it can encounter test data drawn from distributions different from the training data distribution and suffer drop in performance. For safe deployment, it is essential to estimate the accuracy of the pre-trained model on the test data. However, the labels for the test inputs are usually not immediately available in practice, and obtaining them can be expensive. This observation leads to two challenging tasks: (1) unsupervised accuracy estimation, which aims to estimate the accuracy of a pre-trained classifier on a set of unlabeled test inputs; (2) error detection, which aims to identify mis-classified test inputs. In this paper, we propose a principled and practically effective framework that simultaneously addresses the two tasks.
AutoML Two-Sample Test Jonas M. Kübler
Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts.
Supplementary Material -- Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions
Fast parallel circuits for the quantum fourier transform.