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1219 directly classified papers

Papers per year

Papers

Estimating the Lasso's Effective Noise JMLR 2021
Simultaneous Change Point Inference and Structure Recovery for High Dimensional Gaussian Graphical Models JMLR 2021
The Base Measure Problem and its Solution AISTATS 2021
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression NIPS 2021
Inference for the Case Probability in High-dimensional Logistic Regression JMLR 2021
Improved Shrinkage Prediction under a Spiked Covariance Structure JMLR 2021
Generalization Properties of hyper-RKHS and its Applications JMLR 2021
Adaptive estimation of nonparametric functionals JMLR 2021
Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage JMLR 2021
Sequential Algorithms for Testing Closeness of Distributions NIPS 2021
Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler JMLR 2021
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection NIPS 2021
Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation NIPS 2021
Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach JMLR 2021
Homogeneity Structure Learning in Large-scale Panel Data with Heavy-tailed Errors JMLR 2021
Differential Privacy Over Riemannian Manifolds NIPS 2021
A Computationally Efficient Method for Learning Exponential Family Distributions NIPS 2021
Learning and Testing Irreducible Markov Chains via the $k$-Cover Time ALT 2021
Mixing Time of Metropolis-Hastings for Bayesian Community Detection JMLR 2021
γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator AISTATS 2021
Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers AISTATS 2021
A constrained risk inequality for general losses AISTATS 2021
Efficient Interpolation of Density Estimators AISTATS 2021
Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction ICML 2021
A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions ICML 2021