Statistical Learning
Transferable Calibration with Lower Bias and Variance in Domain Adaptation Ximei Wang, Mingsheng Long, Jianmin Wang, and Michael I. Jordan
Calibration (TransCal) to achieve more accurate calibration with lower bias and variance in a unified hyperparameter-free optimization framework.
Common Question Q1: The covariate shift assumption
We thank the reviewers for insightful and constructive comments. We have submitted code and detailed Appdendix . TransCal, it is inadvertently omitted by us while writing. Common Question Q2: Will TransCal have a lower accuracy while achieving a better calibration? TransCal maintains the same accuracy with that before calibration, while achieving a lower ECE (Figure 1(b)).
Appendix to: Training Uncertainty-Aware Classifiers with Conformalized Deep Learning Bat-Sheva Einbinder Y aniv Romano Matteo Sesia Y anfei Zhou A1 Additional methodological details
Authors listed in alphabetical order. Figure A1: Schematic of the proposed uncertainty-aware deep classification learning algorithm. This procedure is summarized in Algorithm A1, which is a more technical version of Algorithm 1. (t 1) (t 1) This section explains the implementation of the hybrid benchmark method applied in Section 4. This This benchmark is based on a loss function designed to incentivize the trained model to produce the smallest possible conformal prediction sets with the desired coverage (e.g., 90% if (t 1) (t 1) To facilitate the exposition of our analysis, we begin by introducing some helpful notations. The first part of the proof is standard and proceeds as follows. A3.1 Details about experiments with synthetic data The conditional data-generating distribution of Y given X is given by: P[Y | X ] = null Our method (resp., the hybrid method) is applied using The hybrid loss model is trained via stochastic gradient descent for 4000 epochs with learning rate 0.01 decreased by a factor 10 halfway through training.
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
In particular, they tend to be overconfident.
Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces Vincent Cohen-Addad Google Research, Zurich. David Saulpic Sorbonne Universit e, Paris Chris Schwiegelshohn
Special cases of problem include the well-known Fermat-Weber problem - or geometric median problem - where z = 1, the mean or centroid where z = 2, and the Minimum Enclosing Ball problem, where z = . We consider these problem in the big data regime. Here, we are interested in sampling as few points as possible such that we can accurately estimate m. More specifically, we consider sublinear algorithms as well as coresets for these problems. Sublinear algorithms have a random query access to the set A and the goal is to minimize the number of queries.
