Alumni

Visit Stanford University Libraries to search for the PhD dissertations of our graduates.

Graduation Year
Student
Dissertation
Advisor
Committee Members

Minimax Regret Bounds for Stochastic Linear Bandit Algorithms

Bayati (GSB)

Lai, Candes

Model-Free Methods for Multiple Testing and Predictive Inference

Candes

Tibshirani, Owen

Advances in Contextual Multi-armed Bandits, Dynamic Treatment Strategies, and Type 1 Error Rate Guarantees in Confirmatory Adaptive Multi-arm Clinical Trials

Lai

Lavori (BDS), Lu (BDS), Palacios

Extending the Reach of the Lasso and Elastic Net Penalties: Methodology and Practice

Tibshirani

Art Owen, Guenther Walther

A Nonparametric Measure of Conditional Dependence

Chatterjee

Taylor, Bayati (GSB)

Causal Inference in Genetic Trio Studies

Candes

Tibshirani, Wager

Statistics and Uncertainty Quantification in Graphs and Networks with Biomedical Applications

Holmes

Friedman, Leskovec (CS)

Topics in Machine Learning for Causal Inference with Applications in Social Science

Athey (Econ)

Owen, Hastie

Statistical methods for adaptive data analysis

Taylor

Tibshirani, Romano

The Algebra and Machine Representation of Statistical Models

Candes

Hastie, Chambers, Musen (Med/BDS)

Large-scale and High-dimensional Statistical Learning: Methods and Algorithms

Hastie

Tibshirani, Rivas (BDS)

Making Causal Conclusions from Heterogeneous Data Sources

Owen/Baiocchi

Palacios

New Methods for Variable Importance Testing with Applications to Genetic Studies

Candes

Sabatti, Tibshirani

Sampling Methods for Perfect Bipartite Matchings

Diaconis

Chatterjee, Charikar (CS)

Asymptotic Theory for Large Random Matrices and its Applications

Dembo

Chatterjee, Montanari

Boosting Like Path Algorithms for L1 Regularized Infinite Dimensional Convex Neural Networks

Hastie

Taylor, Tibshirani

When do gradient methods work well in non-convex learning problems?

Duchi

Montanari, Candes

Topics in Selective Inference and its Application to Instrumental Variables

Taylor

Tibshirani, Lai

Modern Statistical Approaches for Randomized Experiments Under Interference

Ugander (MSE)

Walther, Palacios

Some hierarchical and group regularization methods and applications

Tibshirani

Taylor, Imbens (GSB)

Classification and testing under robust model assumptions

Tibshirani/Wong

Efron

Multiple Testing with Structure and Exploration

Sabatti

Candes, Montanari

A Computationally Conscious Search for Interactions

Tibshirani

Owen, Wager (GSB)

Health and loan default risk analytics: Prediction models and their evaluation

Lai

Walther, Lu (BDS)

Topics in Robust Mean Estimation

Diaconis

Chatterjee, Wong

Adapting local minimax theory to modern applications

Duchi

Johnstone, Candes

A Modern Maximum Likelihood Theory for High-Dimensional Logistic Regression

Candes

Montanari, Johnstone

Edgeworth approximations for spiked PCA models and applications

Johnstone

Owen, Donoho

Detection and validation of genomic structural variation from DNA sequencing data

Wong

Owen, Tang

Hypothesis Testing Using Multiple Data Splitting

Romano

Taylor, Tibshirani

Eigenvalues in multivariate random effects models

Montanari/Johnstone

Candes

Learning to generate text, programs (and beyond) from weak supervision

Liang (CS)

Wong, Mackey, Manning (CS)

Topics in statistical learning with a focus on large-scale data

Hastie

Efron, Taylor

An approximation-based framework for post-selective inference

Taylor

Tibshirani, Sabatti

Discovery and Visualization of Latent Structure with Applications to the Microbiome

Holmes

Switzer, Efron

Topics in High-Dimensional Asymptotics

Donoho

Owen, Johnstone

Stein's Lemma and Subsampling in Large-Scale Optimization

Montanari/Bayati

Candes

Multivariate Methods for the Analysis of Structured Data

Holmes

Wong, Relman (Med)

Scalable Estimation and Inference for Massive Linear Mixed Models with Crossed Random Effects

Owen

Tibshirani, Mackey

Measuring Sample Quality with Stein's Method

Mackey/Candes

Owen

Monotone interactions of random walks and graphs

Dembo

Chatterjee, Diaconis

A model-free approach to high-dimensional inference

Candes

Mackey, Hastie

Two Parameter Inference Methods in Likelihood-free Models: Approximate Bayesian Computation and Contrastive Divergence

Wong

Lai, Diaconis

Large-scale inference with block structure

Walther

Lai, Siegmund

Prediction and dimension reduction methods in computer experiments

Owen

Switzer, Taylor

Evaluating Diagnostics under Dependency

Tian

Holmes, Siegmund

Leveraging similarity in statistical learning

Tibshirani

Friedman, Hastie

Optimization, Random Graphs, and Spin Glasses

Dembo/Montanari

Chatterjee

Non-parametric goodness-of-fit testing and applications

Diaconis

Chatterjee, Bump (Math)

Topics in selective inference

Taylor

Hastie, Tibshirani