5 papers
Mean Testing under Truncation beyond Gaussian
Yuhao Wang, Roberto Imbuzeiro Oliveira, Themis Gouleakis
We characterize the fundamental limits of high-dimensional mean testing under arbitrary truncation, where samples are drawn from the conditional distribution for…
Product distribution learning with imperfect advice
Arnab Bhattacharyya, Davin Choo, Philips George John +1
Given i.i.d.~samples from an unknown distribution , the goal of distribution learning is to recover the parameters of a distribution that is close to . When belongs to th…
Learning High-dimensional Gaussians from Censored Data
Arnab Bhattacharyya, Constantinos Daskalakis, Themis Gouleakis +1
We provide efficient algorithms for the problem of distribution learning from high-dimensional Gaussian data where in each sample, some of the variable values are missing. We suppo…
Gaussian Mean Testing under Truncation
Clément L. Canonne, Themis Gouleakis, Yuhao Wang +1
We consider the task of Gaussian mean testing, that is, of testing whether a high-dimensional vector perturbed by white noise has large magnitude, or is the zero vector. This quest…
Learning multivariate Gaussians with imperfect advice
Arnab Bhattacharyya, Davin Choo, Philips George John +1
We revisit the problem of distribution learning within the framework of learning-augmented algorithms. In this setting, we explore the scenario where a probability distribution is…