7 papers
How much can we learn from quantum random circuit sampling?
Tudor Manole, Daniel K. Mark, Wenjie Gong +3
Benchmarking quantum devices is a foundational task for the sustained development of quantum technologies. However, accurate in situ characterization of large-scale quantum devices…
A Gapped Scale-Sensitive Dimension and Lower Bounds for Offset Rademacher Complexity
Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin
We study gapped scale-sensitive dimensions of a function class in both sequential and non-sequential settings. We demonstrate that covering numbers for any uniformly bounded class…
Testing and estimation in orthosymmetric Gaussian sequence model
Zeyu Jia, Yury Polyanskiy
We study the Gaussian sequence model, i.e. , where is assumed to be convex and compact. We show that goodness-of-fit te…
Nonparametric MLE for Gaussian Location Mixtures: Certified Computation and Generic Behavior
Yury Polyanskiy, Mark Sellke
We study the nonparametric maximum likelihood estimator for Gaussian location mixtures in one dimension. It has been known since (Lindsay, 1983) that given an -point…
On the Minimax Regret of Sequential Probability Assignment via Square-Root Entropy
Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin
We study the problem of sequential probability assignment under logarithmic loss, both with and without side information. Our objective is to analyze the minimax regret -- a notion…
Solving Empirical Bayes via Transformers
Anzo Teh, Mark Jabbour, Yury Polyanskiy
This work applies modern AI tools (transformers) to solving one of the oldest statistical problems: Poisson means under empirical Bayes (Poisson-EB) setting. In Poisson-EB a high-d…