1 citations · 1 across the 5 of their papers we have counts for
6 papers
SpeedCP: Fast Kernel-based Conditional Conformal Prediction
Yating Liu, Yeo Jin Jung, Zixuan Wu +2
Conformal prediction provides distribution-free prediction sets with finite-sample conditional guarantees. We build upon the RKHS-based framework of Gibbs et al. (2023), which leve…
Filtering with Confidence: When Data Augmentation Meets Conformal Prediction
Zixuan Wu, So Won Jeong, Yating Liu +2
With promising empirical performance across a wide range of applications, synthetic data augmentation appears a viable solution to data scarcity and the demands of increasingly dat…
Efficient Canonical Correlation Analysis with Sparsity
Zixuan Wu, Elena Tuzhilina, Coralie Rousseau +1
In high-dimensional settings, Canonical Correlation Analysis (CCA) often fails, and existing sparse methods force an untenable choice between computational speed and statistical ri…
Convergence Rates of Attractive-Repulsive MCMC Algorithms
Yu Hang Jiang, Tong Liu, Zhiya Lou +4
We consider MCMC algorithms for certain particle systems which include both attractive and repulsive forces, making their convergence analysis challenging. We prove that a version…
MCMC Confidence Intervals and Biases
Yu Hang Jiang, Tong Liu, Zhiya Lou +4
The recent paper "Simple confidence intervals for MCMC without CLTs" by J.S. Rosenthal, showed the derivation of a simple MCMC confidence interval using only Chebyshev's inequality…
The Coupling/Minorization/Drift Approach to Markov Chain Convergence Rates
Yu Hang Jiang, Tong Liu, Zhiya Lou +4
This review paper provides an introduction of Markov chains and their convergence rates which is an important and interesting mathematical topic which also has important applicatio…