5 papers
A Heisenberg-esque Uncertainty Principle for Simultaneous (Machine) Learning and Error Assessment?
Xiao-Li Meng
A highly cited and inspiring article by Bates et al (2024) demonstrates that the prediction errors estimated through cross-validation, Bootstrap or Mallow's can all be indepe…
A Heavily Right Strategy for Statistical Inference with Dependent Studies in Arbitrary Dimensions
Tianle Liu, Xiao-Li Meng, Natesh S. Pillai
We leverage recent advances in heavy-tail approximations for global hypothesis testing with dependent studies to construct approximate confidence regions without modeling or estima…
Channelling Multimodality Through a Unimodalizing Transport: Warp-U Sampler and Stochastic Bridge Sampling Estimator
Fei Ding, Shiyuan He, David E. Jones +1
Monte Carlo integration is a powerful tool for scientific and statistical computation, but faces significant challenges when the integrand is a multi-modal distribution, even when…
BELIEF in Dependence: Leveraging Atomic Linearity in Data Bits for Rethinking Generalized Linear Models
Benjamin Brown, Kai Zhang, Xiao-Li Meng
Two linearly uncorrelated binary variables must be also independent because non-linear dependence cannot manifest with only two possible states. This inherent linearity is the atom…
Scalable Spike-and-Slab
Niloy Biswas, Lester Mackey, Xiao-Li Meng
Spike-and-slab priors are commonly used for Bayesian variable selection, due to their interpretability and favorable statistical properties. However, existing samplers for spike-an…