Topological Data Analysis on Noisy Quantum Computers
Ismail Akhalwaya, Shashanka Ubaru, et al.
ICLR 2024
This paper studies Central Limit Theorems for real-valued functionals of Conditional Markov Chains. Using a classical result by Dobrushin (1956) for non-stationary Markov chains, a conditional Central Limit Theorem for fixed sequences of observations is estab- lished. The asymptotic variance can be es- timated by resampling the latent states con- ditional on the observations. If the condi- tional means themselves are asymptotically normally distributed, an unconditional Cen- tral Limit Theorem can be obtained. The methodology is used to construct a statistical hypothesis test which is applied to syntheti- cally generated environmental data.
Ismail Akhalwaya, Shashanka Ubaru, et al.
ICLR 2024
Ira Pohl
Artificial Intelligence
Bing Zhang, Mikio Takeuchi, et al.
NAACL 2025
Conrad Albrecht, Jannik Schneider, et al.
CVPR 2025