lidongrong / interesting-stat-cs-papers

Interesting papers in Stat/CS I have read.

Interesting-Stat-CS-Papers

Interesting papers in Stat/CS I have read during my Ph.D. study.

Stat Learning

Generative Modelling by Estimating the Gradient of the Distribution BY Y. Song

Sparse Topic Modeling: Computational Efficiency,Near-Optimal Algorithms, and Statistical Inference by Tony Cai

Stat Methodology

Transfer Learning for High-Dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality by Tony Cai

gaussian variational approximation with composite likelihood for crossed random effect models by Libai Xu

Universal Inference by Larry Wasserman

Ranking Inferences Based on the Top Choice of Multiway Comparisons by Jianqing Fan

Embedding Learning by Ben Dai

Significance tests of feature relevance for a black-box learner by Ben Dai

Smooth neighborhood recommender systems by Ben Dai

Scalable collaborative ranking for personalized prediction by Ben Dai

Coupled Generation by Ben Dai

Optimization / Computational Statistics

ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence by Ben Dai

Stat Theory

Information Flow in Self-Supervised Learning by Zhiquan Tan

Monte Carlo Methods

Geometry of Sampling by Zhu Jun

Gradient-Based Markov Chain Monte Carlo for Bayesian Inference with Non-differentiable Priors on JASA

Efficient Informed Proposals for Discrete Distributions via Newton's Series by Ruqi Zhang

A Langevin-like Sampler for Discrete Distributions by Ruqi Zhang

Sampling Random Graph Homomorphisms and Applications to Network Data Analysis on arxiv

Informed proposals for local MCMC in discrete spaces by Zanella

The Barker proposal: combining robustness and efficiency in gradient-based MCMC by S.Livingstone

Application