Traditional Statistics

1. Clustering

1. Hidden Integrality of SDP Relaxation for Sub-Gaussian Mixture Models
2. Exponential Error Rates of SDP for Block Models: Beyond Grothendieck's Inequality

2. Concentration Inequalities

1. Sum-of-Squares Lower Bounds for Sparse PCA
2. The Masked Sample Covariance Estimator- an Analysis Using Matrix Concentration Inequalities

3. Causal Inference

1. Hitchcock, Christopher, "Causal Models", The Stanford Encyclopedia of Philosophy
2. Identification of Causal Effects using Instrumental Variables
3. Implications of Confounding for Making Intervention Decisions Using Data Mining
4. The Blessings of Multiple Causes
5. Causal Inference in Statistics: An Overview
6. A Tree-Based Approach for Addressing Self-Selection in Impact Studies with Big Data
7. Recursive Partitioning for Heterogeneous Causal Effects
8. Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
9. Double Machine Learning for Treatment and Causal Parameters
10. Double/debiased Machine Learning for Treatmentand Structural Parameters
11. Double/debiased/neyman Machine Learning of Treatment Effects
12. Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions
13. Conformal Inference of Counterfactuals and Individual Treatment Effects

4. Causal Mediation Analysis

1. Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
2. A General Approach to Causal Mediation Analysis
3. Mediation Analysis with Multiple Mediators
4. Unpacking the Black Box of Causality
5. Challenges Raised by Mediation Analysis in a High-Dimension Setting
6. Testing Mediation Effects in High-Dimensional Epigenetic Studies
7. Estimating and Testing High-dimensional Mediation Effects in Epigenetic Studies
8. A Comparison of Methods to Test Mediation and Other Intervening Variable Effects
9. Joint Significance Tests for Mediation Effects of Socioeconomic Adversity on Adiposity via Epigenetics
10. Genome-wide Analyses of Sparse Mediation Effects Under Composite Null Hypotheses
11. Sparse Principal Component-based High-dimensional Mediation Analysis
12. Hypothesis Test of Mediation Effect in Causal Mediation Model with High-Dimensional Continuous Mediators

5. Differential Privacy

1. Deep Learning with Gaussian Differential Privacy
2. Deep Learning with Differential Privacy
3. Oracle Efficient Private Non-Convex Optimization
4. Smooth Sensitivity and Sampling in Private Data Analysis
5. On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
6. Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems
7. The Cost of Privacy in Generalized Linear Models Algorithms and Minimax Lower Bounds
8. Private Stochastic Convex Optimization with Optimal Rates
9. Preserving Statistical Validity in Adaptive Data Analysis
10. Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds
11. Differentially Private Empirical Risk Minimization with Non-convex Loss Functions
12. Understanding Gradient Clipping in Private SGD: A Geometric Perspective
13. Private PAC Learning Implies Finite Littlestone

6. Distributed Inference

1. Distributed Inference for PCA
2. Communication-Efficient Distributed Statistical Inference
3. Communication-Efficient Algorithms for Statistical Optimization
4. Distributed Estimation of Principal Eigenspaces
5. Communication-Efficient Algorithms for Distributed Stochastic Principal Component Analysis
6. Communication Efficient Distributed Optimization using an Approximate Newton-type Method
7. Communication-Efficient Accurate Statistical Estimation
8. A Massive Data Framework for M-Estimators with Cubic-Rate

7. False Discovery Rate

1. Black Box FDR

8. General Inference

1. A Scalable Bootstrap for Massive Data
2. A Galtonian Perspective on Shrinkage Estimators
3. Predictive Inference with the Jackknife+
4. Conformalized Quantile Regression
5. Conformal Prediction Under Covariate Shift

9. High Dimensional Linear Regression

1. Regression Shrinkage and Selection via the Lasso
2. Nonparametric Instrumental Regression
3. On Asymptotically Optimal Confidence Regions and Tests for High-dimensional Models
4. Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
5. Sure Independence Screening for Ultrahighdimensional Feature Space

10. Hypothesis Testing

1. Testing for Independence of Large Dimensional Vectors

11. Non-smooth Estimation and Inference

1. A General Bahadur Representation of M-estimators and Its Application to Linear Regression with Nonstochastic Designs (He and Shao, AoS 1996)

12. Post-Selection Inference

1. Exact Post-Selection Inference, with Application to the Lasso
2. Principled Statistical Inference in Data Science

13. Probability

1. Optimal Estimation of a Large-dimensional Covariance Matrix under Stein’s Loss

14. Reinforcement Learning/Adaptive Inference

1. Doubly-Robust Lasso Bandit
2. Statistical Inference for Online Decision Making: In a Contextual Bandit Setting
3. Statistical Inference for Online Decision Making via Stochastic Gradient Descent
4. Statistical Inference with M-Estimators on Adaptively Collected Data (Zhang, Jason, and Murphy, NIPS 2021)
5. Confidence Intervals for Policy Evaluation in Adaptive Experiments (Zhan, Wager and Athey, PNAS 2021)
6. Inference for Batched Bandits (Zhang, Jason, and Murphy, NIPS 2021)
7. Near-optimal Inference in Adaptive Linear Regression (Khamaru and Wainwright, 2021)

15. Semi-supervised Inference

1. Semi-supervised Inference: General Theory and Estimation of Means

16. SGD Inference

1. Statistical Inference for Model Parameters in Stochastic Gradient Descent
2. Online Bootstrap Confidence Interval for the Stochastic Gradient Descent Estimator
3. Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains
4. On Constructing Confidence Region for Model Parameters in Stochastic Gradient Descent via Batch Means
5. A Fully Online Approach for Covariance Matrices Estimation of Stochastic Gradient Descent Solutions
6. Uncertainty Quantification for Online Learning and Stochastic Approximation via Hierarchical Incremental Gradient Descent
7. Acceleration of Stochastic Approximation by Averaging
8. Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients
9. Asymptotic and Finite-sample Properties of Estimators Based on Stochastic Gradients
10. On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration
11. ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm
12. Asymptotic Optimality in Stochastic Optimization