72 citations · 123 across the 11 of their papers we have counts for
6 papers · 1 filter
Distribution-free uncertainty quantification for classification under label shift
Aleksandr Podkopaev, Aaditya Ramdas
Trustworthy deployment of ML models requires a proper measure of uncertainty, especially in safety-critical applications. We focus on uncertainty quantification (UQ) for classifica…
Uncertainty quantification using martingales for misspecified Gaussian processes
Willie Neiswanger, Aaditya Ramdas
We address uncertainty quantification for Gaussian processes (GPs) under misspecified priors, with an eye towards Bayesian Optimization (BO). GPs are widely used in BO because they…
A Higher-Order Kolmogorov-Smirnov Test
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas +1
We present an extension of the Kolmogorov-Smirnov (KS) two-sample test, which can be more sensitive to differences in the tails. Our test statistic is an integral probability metri…
A framework for Multi-A(rmed)/B(andit) testing with online FDR control
Fanny Yang, Aaditya Ramdas, Kevin Jamieson +1
We propose an alternative framework to existing setups for controlling false alarms when multiple A/B tests are run over time. This setup arises in many practical applications, e.g…
Fast Two-Sample Testing with Analytic Representations of Probability Measures
Kacper Chwialkowski, Aaditya Ramdas, Dino Sejdinovic +1
We propose a class of nonparametric two-sample tests with a cost linear in the sample size. Two tests are given, both based on an ensemble of distances between analytic functions r…
An Analysis of Active Learning With Uniform Feature Noise
Aaditya Ramdas, Barnabas Poczos, Aarti Singh +1
In active learning, the user sequentially chooses values for feature and an oracle returns the corresponding label . In this paper, we consider the effect of feature noise i…